diff --git a/.all-contributorsrc b/.all-contributorsrc index 5b003ed874..72be42003c 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -1,5 +1,6 @@ { "files": [ + "all_contributors.md", "README.md" ], "imageSize": 100, @@ -7,7 +8,7 @@ "badgeTemplate": "[![All Contributors](https://img.shields.io/badge/all_contributors-<%= contributors.length %>-orange.svg)](#-contributors)", "contributors": [ { - "login": "tinosulzer", + "login": "valentinsulzer", "name": "Valentin Sulzer", "avatar_url": "https://avatars3.githubusercontent.com/u/20817509?v=4", "profile": "https://sites.google.com/view/valentinsulzer", @@ -215,7 +216,8 @@ "profile": "https://github.com/jonchapman1", "contributions": [ "ideas", - "fundingFinding" + "fundingFinding", + "doc" ] }, { @@ -772,7 +774,9 @@ "avatar_url": "https://avatars.githubusercontent.com/u/99216956?v=4", "profile": "https://github.com/prady0t", "contributions": [ - "infra" + "infra", + "code", + "test" ] }, { @@ -793,6 +797,149 @@ "contributions": [ "code" ] + }, + { + "login": "AlessioBugetti", + "name": "Alessio Bugetti", + "avatar_url": "https://avatars.githubusercontent.com/u/38499721?v=4", + "profile": "https://github.com/AlessioBugetti", + "contributions": [ + "infra", + "code", + "doc", + "test" + ] + }, + { + "login": "kawaMANMI", + "name": "kawaMANMI", + "avatar_url": "https://avatars.githubusercontent.com/u/39382602?v=4", + "profile": "https://github.com/kawaMANMI", + "contributions": [ + "bug", + "code" + ] + }, + { + "login": "Akhil-Sharma30", + "name": "AKHIL SHARMA", + "avatar_url": "https://avatars.githubusercontent.com/u/68015525?v=4", + "profile": "http://akhilsharma.info", + "contributions": [ + "doc" + ] + }, + { + "login": "HarshvirSandhu", + "name": "Harshvir Sandhu", + "avatar_url": "https://avatars.githubusercontent.com/u/75773763?v=4", + "profile": "https://github.com/HarshvirSandhu", + "contributions": [ + "code" + ] + }, + { + "login": "lorenzofavaro", + "name": "Lorenzo", + "avatar_url": "https://avatars.githubusercontent.com/u/44714920?v=4", + "profile": "https://github.com/lorenzofavaro", + "contributions": [ + "code", + "test", + "doc" + ] + }, + { + "login": "AndyLiuElysia", + "name": "AndyLiuElysia", + "avatar_url": "https://avatars.githubusercontent.com/u/143705453?v=4", + "profile": "https://github.com/AndyLiuElysia", + "contributions": [ + "doc" + ] + }, + { + "login": "Hongmeiqi", + "name": "Hongmeiqi", + "avatar_url": "https://avatars.githubusercontent.com/u/143798726?v=4", + "profile": "https://github.com/Hongmeiqi", + "contributions": [ + "doc" + ] + }, + { + "login": "mleot", + "name": "mleot", + "avatar_url": "https://avatars.githubusercontent.com/u/140573653?v=4", + "profile": "https://github.com/mleot", + "contributions": [ + "code", + "test" + ] + }, + { + "login": "abhicodes369", + "name": "Abhi ram", + "avatar_url": "https://avatars.githubusercontent.com/u/119055274?v=4", + "profile": "https://github.com/abhicodes369", + "contributions": [ + "test" + ] + }, + { + "login": "parkec3", + "name": "Caitlin D. Parke", + "avatar_url": "https://avatars.githubusercontent.com/u/26883801?v=4", + "profile": "https://github.com/parkec3", + "contributions": [ + "code" + ] + }, + { + "login": "Afgr1087", + "name": "Andres Felipe Galvis Rodriguez", + "avatar_url": "https://avatars.githubusercontent.com/u/56610829?v=4", + "profile": "https://github.com/Afgr1087", + "contributions": [ + "code" + ] + }, + { + "login": "ikorotkin", + "name": "Ivan Korotkin", + "avatar_url": "https://avatars.githubusercontent.com/u/29599800?v=4", + "profile": "https://github.com/ikorotkin", + "contributions": [ + "code" + ] + }, + { + "login": "santacodes", + "name": "Santhosh", + "avatar_url": "https://avatars.githubusercontent.com/u/52504160?v=4", + "profile": "https://github.com/santacodes", + "contributions": [ + "code", + "infra" + ] + }, + { + "login": "smitasahu2", + "name": "Smita Sahu", + "avatar_url": "https://avatars.githubusercontent.com/u/57876346?v=4", + "profile": "https://github.com/smitasahu2", + "contributions": [ + "code" + ] + }, + { + "login": "Ubham16", + "name": "Ubham16", + "avatar_url": "https://avatars.githubusercontent.com/u/173074476?v=4", + "profile": "https://github.com/Ubham16", + "contributions": [ + "code" + ] } ], "contributorsPerLine": 7, diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs index b38e6697cb..97a8d33089 100644 --- a/.git-blame-ignore-revs +++ b/.git-blame-ignore-revs @@ -10,3 +10,5 @@ a63e49ece0f9336d1f5c2562f7459e555c6e6693 ff6d81c01331c7d269303b4a8321d9881bdf98fa # migrated to ruff-format - https://github.com/pybamm-team/PyBaMM/pull/3655 60ebd4148059a95428a496f4f55c1175ead362d3 +# implemented cleaner string formatting via f-strings - https://github.com/pybamm-team/PyBaMM/pull/3890 +f395819d1c874071b7e76e32ec4f0bbe42462b48 diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS new file mode 100644 index 0000000000..789a05a9b8 --- /dev/null +++ b/.github/CODEOWNERS @@ -0,0 +1,34 @@ +# Automatically request reviews from maintainers + +# Package +/pybamm/discretisations/ @martinjrobins @rtimms @valentinsulzer +/pybamm/experiment/ @brosaplanella @martinjrobins @rtimms @valentinsulzer @TomTranter +/pybamm/expression_tree/ @martinjrobins @rtimms @valentinsulzer +/pybamm/geometry/ @martinjrobins @rtimms @valentinsulzer +/pybamm/input/ @brosaplanella @DrSOKane @rtimms @valentinsulzer @TomTranter @kratman +/pybamm/meshes/ @martinjrobins @rtimms @valentinsulzer @rtimms +/pybamm/models/ @brosaplanella @DrSOKane @rtimms @valentinsulzer @TomTranter @rtimms +/pybamm/parameters/ @brosaplanella @DrSOKane @rtimms @valentinsulzer @TomTranter @rtimms @kratman +/pybamm/plotting/ @martinjrobins @rtimms @Saransh-cpp @valentinsulzer @rtimms @kratman @agriyakhetarpal +/pybamm/solvers/ @martinjrobins @rtimms @valentinsulzer @TomTranter @rtimms +/pybamm/spatial_methods/ @martinjrobins @rtimms @valentinsulzer @rtimms +/pybamm/* @pybamm-team/maintainers # the files directly under /pybamm/, will not recurse + +# CI/CD workflows +/.github/ @martinjrobins @Saransh-cpp @agriyakhetarpal @kratman @arjxn-py + +# Benchmarks +/benchmarks/ @brosaplanella @Saransh-cpp @agriyakhetarpal @arjxn-py + +# Documentation +/docs/ @kratman @arjxn-py @agriyakhetarpal @Saransh-cpp + +# Example scripts +/examples/ @kratman @agriyakhetarpal @Saransh-cpp + +# Installation and other scripts +/scripts/ @martinjrobins @Saransh-cpp @agriyakhetarpal @kratman @arjxn-py + +# Files in the root directory +/* @martinjrobins @Saransh-cpp @agriyakhetarpal @kratman @arjxn-py +/CHANGELOG.md # no owner (almost every PR edits the CHANGELOG) diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml index be34da414e..78ee71d016 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yml +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -34,7 +34,7 @@ body: id: reproduce attributes: label: Steps to Reproduce - description: Tell us how to reproduce this behaviour. Ideally, this should take the form of a [Minimum Workable Example](https://stackoverflow.com/help/minimal-reproducible-example) + description: Tell us how to reproduce this behaviour. Ideally, this should include a code block which produces the error. Strive to make this example as small and simple as possible. It should contain the code required to reproduce the error, and no additional code. For example, if your code includes lines to run a simulation, then lines to plot the results, and the lines to run the simulation fail, only include those lines (and not the plotting lines, which are irrelevant). Often, the act of simplifying code to pinpoint errors can help you find bugs in your own code. For more information, see [these references](https://en.wikipedia.org/wiki/Minimal_reproducible_example#External_links) validations: required: true - type: textarea diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml index 949b3f55c9..aab4debfff 100644 --- a/.github/ISSUE_TEMPLATE/config.yml +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -1,5 +1,5 @@ blank_issues_enabled: true contact_links: - name: I'm unsure where to go - url: https://www.pybamm.org/contact + url: https://www.pybamm.org/community about: If you are unsure where to go, then joining our chat is recommended; Just ask! diff --git a/.github/codecov.yml b/.github/codecov.yml index 1f0452076a..e69de29bb2 100644 --- a/.github/codecov.yml +++ b/.github/codecov.yml @@ -1,2 +0,0 @@ -ignore: - - pybamm/install_odes.py diff --git a/.github/workflows/benchmark_on_push.yml b/.github/workflows/benchmark_on_push.yml index 49fcdee116..b0da71461e 100644 --- a/.github/workflows/benchmark_on_push.yml +++ b/.github/workflows/benchmark_on_push.yml @@ -2,7 +2,6 @@ name: Run benchmarks on push on: push: branches: [main, develop] - pull_request: concurrency: # Cancel intermediate builds always @@ -14,10 +13,10 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - - name: Set up Python 3.8 + - name: Set up Python 3.12 uses: actions/setup-python@v5 with: - python-version: 3.8 + python-version: 3.12 - name: Install Linux system dependencies run: | diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index f92ee76c9e..f76b67829c 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -7,15 +7,11 @@ on: - develop jobs: - build_docker_images: + build_docker_image: # This workflow is only of value to PyBaMM and would always be skipped in forks if: github.repository_owner == 'pybamm-team' - name: Image (${{ matrix.build-args }}) + name: Build image runs-on: ubuntu-latest - strategy: - matrix: - build-args: ["No solvers", "JAX", "ODES", "IDAKLU", "ALL"] - fail-fast: true steps: - name: Checkout @@ -33,27 +29,12 @@ jobs: username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_TOKEN }} - - name: Create tags for Docker images based on build-time arguments - id: tags - run: | - if [ "${{ matrix.build-args }}" = "No solvers" ]; then - echo "tag=latest" >> "$GITHUB_OUTPUT" - elif [ "${{ matrix.build-args }}" = "JAX" ]; then - echo "tag=jax" >> "$GITHUB_OUTPUT" - elif [ "${{ matrix.build-args }}" = "ODES" ]; then - echo "tag=odes" >> "$GITHUB_OUTPUT" - elif [ "${{ matrix.build-args }}" = "IDAKLU" ]; then - echo "tag=idaklu" >> "$GITHUB_OUTPUT" - elif [ "${{ matrix.build-args }}" = "ALL" ]; then - echo "tag=all" >> "$GITHUB_OUTPUT" - fi - - - name: Build and push Docker image to Docker Hub (${{ matrix.build-args }}) - uses: docker/build-push-action@v5 + - name: Build and push Docker image to Docker Hub + uses: docker/build-push-action@v6 with: context: . file: scripts/Dockerfile - tags: pybamm/pybamm:${{ steps.tags.outputs.tag }} + tags: pybamm/pybamm:latest push: true platforms: linux/amd64, linux/arm64 diff --git a/.github/workflows/lychee_url_checker.yml b/.github/workflows/lychee_url_checker.yml index 93dde63845..9a636fda8a 100644 --- a/.github/workflows/lychee_url_checker.yml +++ b/.github/workflows/lychee_url_checker.yml @@ -17,7 +17,7 @@ jobs: # cache Lychee results to avoid hitting rate limits - name: Restore lychee cache - uses: actions/cache@v3 + uses: actions/cache@v4 with: path: .lycheecache key: cache-lychee-${{ github.sha }} @@ -28,7 +28,7 @@ jobs: # use stable version for now to avoid breaking changes - name: Lychee URL checker - uses: lycheeverse/lychee-action@v1.8.0 + uses: lycheeverse/lychee-action@v1.10.0 with: # arguments with file types to check args: >- @@ -47,6 +47,7 @@ jobs: --exclude-path ./scripts/update_version.py --exclude-path asv.conf.json --exclude-path docs/conf.py + --exclude-path all_contributors.md './**/*.rst' './**/*.md' './**/*.py' diff --git a/.github/workflows/need_reply_remove.yml b/.github/workflows/need_reply_remove.yml index 959891aec8..71fe2151d7 100644 --- a/.github/workflows/need_reply_remove.yml +++ b/.github/workflows/need_reply_remove.yml @@ -1,6 +1,8 @@ name: Remove needs-reply label on: + schedule: + - cron: '0 3 * * 1' issue_comment: types: - created @@ -11,7 +13,8 @@ jobs: if: | github.event.comment.author_association != 'OWNER' && github.event.comment.author_association != 'COLLABORATOR' && - github.repository_owner == 'pybamm-team' + github.repository_owner == 'pybamm-team' && + github.event_name != 'pull_request' steps: - name: Remove needs-reply label uses: octokit/request-action@v2.x diff --git a/.github/workflows/periodic_benchmarks.yml b/.github/workflows/periodic_benchmarks.yml index c778c934bf..33d7bc0bbe 100644 --- a/.github/workflows/periodic_benchmarks.yml +++ b/.github/workflows/periodic_benchmarks.yml @@ -8,7 +8,7 @@ # - Publish website name: Benchmarks on: - # Everyday at 3 am UTC + # Every day at 3 am UTC schedule: - cron: "0 3 * * *" # Make it possible to trigger the @@ -21,10 +21,10 @@ jobs: steps: - uses: actions/checkout@v4 - - name: Set up Python 3.8 + - name: Set up Python 3.12 uses: actions/setup-python@v5 with: - python-version: 3.8 + python-version: 3.12 - name: Install Linux system dependencies run: | @@ -52,16 +52,18 @@ jobs: with: name: asv_periodic_results path: results + if-no-files-found: error publish-results: + if: github.repository == 'pybamm-team/PyBaMM' name: Push and publish results needs: benchmarks runs-on: ubuntu-latest steps: - - name: Set up Python 3.8 + - name: Set up Python 3.12 uses: actions/setup-python@v5 with: - python-version: 3.8 + python-version: 3.12 - name: Install asv run: pip install asv @@ -72,18 +74,17 @@ jobs: repository: pybamm-team/pybamm-bench token: ${{ secrets.BENCH_PAT }} - - name: Download results artifact + - name: Download results artifact(s) uses: actions/download-artifact@v4 with: - name: asv_periodic_results - path: new_results + path: results + merge-multiple: true - name: Copy new results and push to pybamm-bench repo env: PUSH_BENCH_EMAIL: ${{ secrets.PUSH_BENCH_EMAIL }} PUSH_BENCH_NAME: ${{ secrets.PUSH_BENCH_NAME }} run: | - cp -vr new_results/* results git config --global user.email "$PUSH_BENCH_EMAIL" git config --global user.name "$PUSH_BENCH_NAME" git add results diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml index ce930733db..afb0a51386 100644 --- a/.github/workflows/publish_pypi.yml +++ b/.github/workflows/publish_pypi.yml @@ -35,7 +35,7 @@ jobs: - uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: - python-version: 3.8 + python-version: 3.11 - name: Get number of cores on Windows id: get_num_cores @@ -48,7 +48,7 @@ jobs: output_stream.write(f"count={num_cpus}\n") - name: Clone pybind11 repo (no history) - run: git clone --depth 1 --branch v2.11.1 https://github.com/pybind/pybind11.git + run: git clone --depth 1 --branch v2.12.0 https://github.com/pybind/pybind11.git -c advice.detachedHead=false - name: Install vcpkg on Windows run: | @@ -59,7 +59,7 @@ jobs: .\bootstrap-vcpkg.bat - name: Cache packages installed through vcpkg on Windows - uses: actions/cache@v3 + uses: actions/cache@v4 env: cache-name: vckpg_binary_cache with: @@ -83,23 +83,20 @@ jobs: CMAKE_GENERATOR_PLATFORM=x64 CMAKE_BUILD_PARALLEL_LEVEL=${{ steps.get_num_cores.outputs.count }} CIBW_ARCHS: AMD64 - CIBW_BEFORE_BUILD: python -m pip install setuptools wheel # skip CasADi and CMake - CIBW_TEST_COMMAND: python -c "import pybamm; pybamm.IDAKLUSolver()" + CIBW_BEFORE_BUILD: python -m pip install setuptools wheel delvewheel # skip CasADi and CMake + CIBW_REPAIR_WHEEL_COMMAND: delvewheel repair -w {dest_dir} {wheel} + CIBW_TEST_COMMAND: python -c "import pybamm; print(pybamm.IDAKLUSolver())" - name: Upload Windows wheels uses: actions/upload-artifact@v4 with: - name: windows_wheels + name: wheels_windows path: ./wheelhouse/*.whl if-no-files-found: error - build_macos_and_linux_wheels: - name: Wheels (${{ matrix.os }}) - runs-on: ${{ matrix.os }} - strategy: - fail-fast: false - matrix: - os: [ubuntu-latest, macos-latest] + build_manylinux_wheels: + name: Wheels (linux-amd64) + runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 name: Check out PyBaMM repository @@ -107,23 +104,13 @@ jobs: - uses: actions/setup-python@v5 name: Set up Python with: - python-version: 3.8 + python-version: 3.11 - name: Clone pybind11 repo (no history) - run: git clone --depth 1 --branch v2.11.1 https://github.com/pybind/pybind11.git - - # sometimes gfortran cannot be found, so reinstall gcc just to be sure - - name: Install SuiteSparse and SUNDIALS on macOS - if: matrix.os == 'macos-latest' - run: | - brew install graphviz openblas libomp - brew reinstall gcc - python -m pip install cmake wget - python scripts/install_KLU_Sundials.py + run: git clone --depth 1 --branch v2.12.0 https://github.com/pybind/pybind11.git -c advice.detachedHead=false - name: Build wheels on Linux run: pipx run cibuildwheel --output-dir wheelhouse - if: matrix.os == 'ubuntu-latest' env: CIBW_ARCHS_LINUX: x86_64 CIBW_BEFORE_ALL_LINUX: > @@ -131,30 +118,145 @@ jobs: bash scripts/install_sundials.sh 6.0.3 6.5.0 CIBW_BEFORE_BUILD_LINUX: python -m pip install cmake casadi setuptools wheel CIBW_REPAIR_WHEEL_COMMAND_LINUX: auditwheel repair -w {dest_dir} {wheel} - CIBW_TEST_COMMAND: python -c "import pybamm; pybamm.IDAKLUSolver()" - - - name: Build wheels on macOS - if: matrix.os == 'macos-latest' - run: pipx run cibuildwheel --output-dir wheelhouse - env: - CIBW_BEFORE_BUILD_MACOS: > - python -m pip install --upgrade cmake casadi setuptools wheel - CIBW_REPAIR_WHEEL_COMMAND_MACOS: delocate-listdeps {wheel} && delocate-wheel -v -w {dest_dir} {wheel} - CIBW_TEST_COMMAND: python -c "import pybamm; pybamm.IDAKLUSolver()" + CIBW_TEST_COMMAND: | + set -e -x + python -c "import pybamm; print(pybamm.IDAKLUSolver())" - name: Upload wheels for Linux uses: actions/upload-artifact@v4 - if: matrix.os == 'ubuntu-latest' with: - name: linux_wheels + name: wheels_manylinux path: ./wheelhouse/*.whl if-no-files-found: error - - name: Upload wheels for macOS + build_macos_wheels: + name: Wheels (${{ matrix.os }}) + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [macos-13, macos-14] + fail-fast: false + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: '3.11' + + - name: Clone pybind11 repo (no history) + run: git clone --depth 1 --branch v2.12.0 https://github.com/pybind/pybind11.git -c advice.detachedHead=false + + - name: Set macOS-specific environment variables + run: echo "MACOSX_DEPLOYMENT_TARGET=11.0" >> $GITHUB_ENV + + - name: Install cibuildwheel + run: python -m pip install cibuildwheel + + - name: Build wheels on macOS + shell: bash + run: | + set -e -x + + # Set LLVM-OpenMP URL + if [[ $(uname -m) == "x86_64" ]]; then + OPENMP_URL="https://anaconda.org/conda-forge/llvm-openmp/11.1.0/download/osx-64/llvm-openmp-11.1.0-hda6cdc1_1.tar.bz2" + elif [[ $(uname -m) == "arm64" ]]; then + OPENMP_URL="https://anaconda.org/conda-forge/llvm-openmp/11.1.0/download/osx-arm64/llvm-openmp-11.1.0-hf3c4609_1.tar.bz2" + fi + + # Download gfortran with proper macOS minimum version (11.0) + if [[ $(uname -m) == "x86_64" ]]; then + GFORTRAN_URL="https://github.com/isuruf/gcc/releases/download/gcc-11.3.0-2/gfortran-darwin-x86_64-native.tar.gz" + KNOWN_SHA256="981367dd0ad4335613e91bbee453d60b6669f5d7e976d18c7bdb7f1966f26ae4 gfortran.tar.gz" + elif [[ $(uname -m) == "arm64" ]]; then + GFORTRAN_URL="https://github.com/isuruf/gcc/releases/download/gcc-11.3.0-2/gfortran-darwin-arm64-native.tar.gz" + KNOWN_SHA256="84364eee32ba843d883fb8124867e2bf61a0cd73b6416d9897ceff7b85a24604 gfortran.tar.gz" + fi + + # Validate gfortran tarball + curl -L $GFORTRAN_URL -o gfortran.tar.gz + if ! echo "$KNOWN_SHA256" != "$(shasum --algorithm 256 gfortran.tar.gz)"; then + echo "Checksum failed" + exit 1 + fi + + mkdir -p gfortran_installed + tar -xv -C gfortran_installed/ -f gfortran.tar.gz + + if [[ $(uname -m) == "x86_64" ]]; then + export FC=$(pwd)/gfortran_installed/gfortran-darwin-x86_64-native/bin/gfortran + export PATH=$(pwd)/gfortran_installed/gfortran-darwin-x86_64-native/bin:$PATH + elif [[ $(uname -m) == "arm64" ]]; then + export FC=$(pwd)/gfortran_installed/gfortran-darwin-arm64-native/bin/gfortran + export PATH=$(pwd)/gfortran_installed/gfortran-darwin-arm64-native/bin:$PATH + fi + + # link libgfortran dylibs and place them in $HOME/.local/lib + # and then change rpath to $HOME/.local/lib for each of them + # Note: libgcc_s.1.dylib not available on macOS arm64; skip for now + mkdir -p $HOME/.local/lib + if [[ $(uname -m) == "x86_64" ]]; then + lib_dir=$(pwd)/gfortran_installed/gfortran-darwin-x86_64-native/lib + for lib in libgfortran.5.dylib libgfortran.dylib libquadmath.0.dylib libquadmath.dylib libgcc_s.1.dylib libgcc_s.1.1.dylib; do + cp $lib_dir/$lib $HOME/.local/lib/ + install_name_tool -id $HOME/.local/lib/$lib $HOME/.local/lib/$lib + codesign --force --sign - $HOME/.local/lib/$lib + done + elif [[ $(uname -m) == "arm64" ]]; then + lib_dir=$(pwd)/gfortran_installed/gfortran-darwin-arm64-native/lib + for lib in libgfortran.5.dylib libgfortran.dylib libquadmath.0.dylib libquadmath.dylib libgcc_s.1.1.dylib; do + cp $lib_dir/$lib $HOME/.local/lib/ + install_name_tool -id $HOME/.local/lib/$lib $HOME/.local/lib/$lib + codesign --force --sign - $HOME/.local/lib/$lib + done + fi + + export SDKROOT=${SDKROOT:-$(xcrun --show-sdk-path)} + + # Can't download LLVM-OpenMP directly, use conda/mamba and set environment variables + brew install miniforge + mamba create -n pybamm-dev $OPENMP_URL + if [[ $(uname -m) == "x86_64" ]]; then + PREFIX="/usr/local/Caskroom/miniforge/base/envs/pybamm-dev" + elif [[ $(uname -m) == "arm64" ]]; then + PREFIX="/opt/homebrew/Caskroom/miniforge/base/envs/pybamm-dev" + fi + + # Copy libomp.dylib from PREFIX to $HOME/.local/lib, needed for wheel repair + cp $PREFIX/lib/libomp.dylib $HOME/.local/lib/ + install_name_tool -id $HOME/.local/lib/libomp.dylib $HOME/.local/lib/libomp.dylib + codesign --force --sign - $HOME/.local/lib/libomp.dylib + + export CC=/usr/bin/clang + export CXX=/usr/bin/clang++ + export CPPFLAGS="$CPPFLAGS -Xpreprocessor -fopenmp" + export CFLAGS="$CFLAGS -I$PREFIX/include" + export CXXFLAGS="$CXXFLAGS -I$PREFIX/include" + export LDFLAGS="$LDFLAGS -L$PREFIX/lib -lomp" + + # cibuildwheel not recognising its environment variable, so set manually + export CIBUILDWHEEL="1" + + python scripts/install_KLU_Sundials.py + python -m cibuildwheel --output-dir wheelhouse + env: + CIBW_ARCHS_MACOS: auto + CIBW_BEFORE_BUILD: python -m pip install cmake casadi setuptools wheel delocate + CIBW_REPAIR_WHEEL_COMMAND: | + if [[ $(uname -m) == "x86_64" ]]; then + delocate-listdeps {wheel} && delocate-wheel -v -w {dest_dir} {wheel} + elif [[ $(uname -m) == "arm64" ]]; then + # Use higher macOS target for now: https://github.com/casadi/casadi/issues/3698 + delocate-listdeps {wheel} && delocate-wheel -v -w {dest_dir} {wheel} --require-target-macos-version 11.1 + for file in {dest_dir}/*.whl; do mv "$file" "${file//macosx_11_1/macosx_11_0}"; done + fi + CIBW_TEST_COMMAND: | + set -e -x + python -c "import pybamm; print(pybamm.IDAKLUSolver())" + + - name: Upload wheels for macOS (amd64, arm64) uses: actions/upload-artifact@v4 - if: matrix.os == 'macos-latest' with: - name: macos_wheels + name: wheels_${{ matrix.os }} path: ./wheelhouse/*.whl if-no-files-found: error @@ -182,36 +284,49 @@ jobs: # This job is only of value to PyBaMM and would always be skipped in forks if: github.event_name != 'schedule' && github.repository == 'pybamm-team/PyBaMM' name: Upload package to PyPI - needs: [build_macos_and_linux_wheels, build_windows_wheels, build_sdist] + needs: [ + build_manylinux_wheels, + build_macos_wheels, + build_windows_wheels, + build_sdist + ] runs-on: ubuntu-latest + environment: + name: pypi + url: https://pypi.org/p/pybamm + permissions: + id-token: write + steps: - name: Download all artifacts uses: actions/download-artifact@v4 + with: + path: artifacts + merge-multiple: true - - name: Move all package files to files/ - run: | - mkdir files - mv windows_wheels/* linux_wheels/* macos_wheels/* sdist/* files/ + - name: Sanity check downloaded artifacts + run: ls -lA artifacts/ - - name: Publish on PyPI + - name: Publish to PyPI if: github.event.inputs.target == 'pypi' || github.event_name == 'release' uses: pypa/gh-action-pypi-publish@release/v1 with: - user: __token__ - password: ${{ secrets.PYPI_TOKEN }} - packages-dir: files/ + packages-dir: artifacts/ - - name: Publish on TestPyPI + - name: Publish to TestPyPI if: github.event.inputs.target == 'testpypi' uses: pypa/gh-action-pypi-publish@release/v1 with: - user: __token__ - password: ${{ secrets.TESTPYPI_TOKEN }} - packages-dir: files/ + packages-dir: artifacts/ repository-url: https://test.pypi.org/legacy/ open_failure_issue: - needs: [build_windows_wheels, build_macos_and_linux_wheels, build_sdist] + needs: [ + build_windows_wheels, + build_manylinux_wheels, + build_macos_wheels, + build_sdist + ] name: Open an issue if build fails if: ${{ always() && contains(needs.*.result, 'failure') && github.repository_owner == 'pybamm-team'}} runs-on: ubuntu-latest diff --git a/.github/workflows/run_benchmarks_over_history.yml b/.github/workflows/run_benchmarks_over_history.yml index 4f7302a4a5..a281380f7f 100644 --- a/.github/workflows/run_benchmarks_over_history.yml +++ b/.github/workflows/run_benchmarks_over_history.yml @@ -23,12 +23,14 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - - name: Set up Python 3.8 + - name: Set up Python 3.12 uses: actions/setup-python@v5 with: - python-version: 3.8 + python-version: 3.12 + - name: Install nox and asv run: pip install -U pip nox asv + - name: Fetch develop branch # Not required when worklow trigerred # on develop, but useful when @@ -36,16 +38,19 @@ jobs: if: github.ref != 'refs/heads/develop' run: | git fetch origin develop:develop + - name: Run benchmarks run: | asv machine --machine "GitHubRunner" asv run -m "GitHubRunner" -s ${{ github.event.inputs.ncommits }} \ ${{ github.event.inputs.commit_start }}..${{ github.event.inputs.commit_end }} + - name: Upload results as artifact uses: actions/upload-artifact@v4 with: name: asv_over_history_results path: results + if-no-files-found: error publish-results: if: github.repository_owner == 'pybamm-team' @@ -53,33 +58,37 @@ jobs: needs: benchmarks runs-on: ubuntu-latest steps: - - name: Set up Python 3.8 + - name: Set up Python 3.12 uses: actions/setup-python@v5 with: - python-version: 3.8 + python-version: 3.12 + - name: Install asv run: pip install asv + - name: Checkout pybamm-bench repo uses: actions/checkout@v4 with: repository: pybamm-team/pybamm-bench token: ${{ secrets.BENCH_PAT }} - - name: Download results artifact + + - name: Download results artifact(s) uses: actions/download-artifact@v4 with: - name: asv_over_history_results - path: new_results + path: results + merge-multiple: true + - name: Copy new results and push to pybamm-bench repo env: PUSH_BENCH_EMAIL: ${{ secrets.PUSH_BENCH_EMAIL }} PUSH_BENCH_NAME: ${{ secrets.PUSH_BENCH_NAME }} run: | - cp -vr new_results/* results git config --global user.email "$PUSH_BENCH_EMAIL" git config --global user.name "$PUSH_BENCH_NAME" git add results git commit -am "Add new results" git push + - name: Publish results run: | asv publish diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml index 6d21393599..2083086ba6 100644 --- a/.github/workflows/run_periodic_tests.yml +++ b/.github/workflows/run_periodic_tests.yml @@ -6,9 +6,9 @@ on: workflow_dispatch: pull_request: branches: - - main + - main - # Run everyday at 3 am UTC + # Run every day at 3 am UTC schedule: - cron: "0 3 * * *" @@ -24,101 +24,236 @@ concurrency: cancel-in-progress: true jobs: - style: + run_unit_tests: + name: Unit tests (${{ matrix.os }} / Python ${{ matrix.python-version }}) + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest, macos-12, macos-14, windows-latest] + python-version: ["3.9", "3.10", "3.11", "3.12"] + # Exclude Python 3.12 from unit tests since we run it in the coverage jobs + exclude: + - os: ubuntu-latest + python-version: "3.12" + steps: + - name: Check out PyBaMM repository + uses: actions/checkout@v4 + + - name: Install Linux system dependencies + if: matrix.os == 'ubuntu-latest' + run: | + sudo apt-get update + sudo apt-get install gfortran gcc graphviz pandoc libopenblas-dev texlive-latex-extra dvipng + + - name: Install macOS system dependencies + if: matrix.os == 'macos-12' || matrix.os == 'macos-14' + env: + HOMEBREW_NO_INSTALL_CLEANUP: 1 + HOMEBREW_NO_AUTO_UPDATE: 1 + HOMEBREW_NO_COLOR: 1 + # Speed up CI + NONINTERACTIVE: 1 + # sometimes gfortran cannot be found, so reinstall gcc just to be sure + run: | + brew analytics off + brew install graphviz libomp + brew reinstall gcc + + - name: Install Windows system dependencies + if: matrix.os == 'windows-latest' + run: choco install graphviz --version=8.0.5 + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + + - name: Install nox + run: python -m pip install nox + + - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS + timeout-minutes: 10 + if: matrix.os != 'windows-latest' + run: python -m nox -s pybamm-requires + + - name: Run unit tests for ${{ matrix.os }} with Python ${{ matrix.python-version }} + run: python -m nox -s unit + + check_coverage: runs-on: ubuntu-latest + name: Coverage tests (ubuntu-latest / Python 3.12) + steps: - - uses: actions/checkout@v4 - - name: Setup python + - name: Check out PyBaMM repository + uses: actions/checkout@v4 + + - name: Install Linux system dependencies + run: | + sudo apt-get update + sudo apt-get install gfortran gcc graphviz pandoc libopenblas-dev texlive-latex-extra dvipng + + - name: Set up Python 3.12 uses: actions/setup-python@v5 with: python-version: 3.12 - - name: Check style - run: | - python -m pip install pre-commit - git add . - pre-commit run ruff + - name: Install nox + run: python -m pip install nox + + - name: Install SuiteSparse and SUNDIALS on GNU/Linux + timeout-minutes: 10 + run: python -m nox -s pybamm-requires - build: - needs: style + - name: Run unit tests for Ubuntu with Python 3.12 and generate coverage report + run: python -m nox -s coverage + + - name: Upload coverage report + uses: codecov/codecov-action@v4.5.0 + if: github.repository == 'pybamm-team/PyBaMM' + with: + token: ${{ secrets.CODECOV_TOKEN }} + + run_integration_tests: + name: Integration tests (${{ matrix.os }} / Python ${{ matrix.python-version }}) runs-on: ${{ matrix.os }} strategy: fail-fast: false matrix: - os: [ubuntu-latest, macos-latest, windows-latest] - python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] - + os: [ubuntu-latest, macos-12, macos-14, windows-latest] + python-version: ["3.9", "3.10", "3.11", "3.12"] steps: - name: Check out PyBaMM repository uses: actions/checkout@v4 - with: - fetch-depth: 0 - - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python-version }} - name: Install Linux system dependencies if: matrix.os == 'ubuntu-latest' run: | sudo apt-get update - sudo apt install gfortran gcc libopenblas-dev graphviz pandoc - sudo apt install texlive-full + sudo apt-get install gfortran gcc graphviz pandoc libopenblas-dev texlive-latex-extra dvipng - name: Install macOS system dependencies - if: matrix.os == 'macos-latest' + if: matrix.os == 'macos-12' || matrix.os == 'macos-14' + env: + HOMEBREW_NO_INSTALL_CLEANUP: 1 + HOMEBREW_NO_AUTO_UPDATE: 1 + HOMEBREW_NO_COLOR: 1 + # Speed up CI + NONINTERACTIVE: 1 + # sometimes gfortran cannot be found, so reinstall gcc just to be sure run: | brew analytics off - brew install graphviz openblas libomp + brew install graphviz brew reinstall gcc - name: Install Windows system dependencies if: matrix.os == 'windows-latest' - run: choco install graphviz --version=2.38.0.20190211 + run: choco install graphviz --version=8.0.5 + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} - name: Install nox run: python -m pip install nox - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS + timeout-minutes: 10 if: matrix.os != 'windows-latest' run: python -m nox -s pybamm-requires - - name: Run unit tests for GNU/Linux with Python 3.8, 3.9, 3.10, and 3.12; and for macOS and Windows with all Python versions - if: (matrix.os == 'ubuntu-latest' && matrix.python-version != 3.11) || (matrix.os != 'ubuntu-latest') - run: python -m nox -s unit + - name: Run integration tests for ${{ matrix.os }} with Python ${{ matrix.python-version }} + run: python -m nox -s integration - - name: Run unit tests for GNU/Linux with Python 3.11 and generate coverage report - if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.11 - run: python -m nox -s coverage + # Skips IDAKLU module compilation for speedups, which is already tested in other jobs. + run_doctests: + runs-on: ubuntu-latest + name: Doctests (ubuntu-latest / Python 3.11) + steps: + - name: Check out PyBaMM repository + uses: actions/checkout@v4 + with: + fetch-depth: 0 - - name: Upload coverage report - if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.11 - uses: codecov/codecov-action@v3.1.4 + - name: Install Linux system dependencies + run: | + sudo apt-get update + sudo apt-get install graphviz pandoc libopenblas-dev texlive-latex-extra dvipng - - name: Run integration tests - run: python -m nox -s integration + - name: Set up Python 3.11 + uses: actions/setup-python@v5 + with: + python-version: 3.11 - - name: Install docs dependencies and run doctests - if: matrix.os == 'ubuntu-latest' + - name: Install nox + run: python -m pip install nox + + - name: Install docs dependencies and run doctests for GNU/Linux run: python -m nox -s doctests - - name: Check if the documentation can be built - if: matrix.os == 'ubuntu-latest' + - name: Check if the documentation can be built for GNU/Linux run: python -m nox -s docs - - name: Install dev dependencies and run example tests - if: matrix.os == 'ubuntu-latest' + run_example_tests: + runs-on: ubuntu-latest + name: Example notebooks (ubuntu-latest / Python 3.12) + + steps: + - name: Check out PyBaMM repository + uses: actions/checkout@v4 + + - name: Install Linux system dependencies + run: | + sudo apt-get update + sudo apt-get install gfortran gcc graphviz pandoc libopenblas-dev texlive-latex-extra dvipng + + - name: Set up Python 3.12 + uses: actions/setup-python@v5 + with: + python-version: 3.12 + + - name: Install nox + run: python -m pip install nox + + - name: Install SuiteSparse and SUNDIALS on GNU/Linux + timeout-minutes: 10 + run: python -m nox -s pybamm-requires + + - name: Run example notebooks tests for GNU/Linux with Python 3.12 run: python -m nox -s examples - - name: Run example scripts tests - if: matrix.os == 'ubuntu-latest' + run_scripts_tests: + runs-on: ubuntu-latest + name: Example scripts (ubuntu-latest / Python 3.12) + + steps: + - name: Check out PyBaMM repository + uses: actions/checkout@v4 + + - name: Install Linux system dependencies + run: | + sudo apt-get update + sudo apt install gfortran gcc graphviz libopenblas-dev texlive-latex-extra dvipng + + - name: Set up Python 3.12 + uses: actions/setup-python@v5 + with: + python-version: 3.12 + + - name: Install nox + run: python -m pip install nox + + - name: Install SuiteSparse and SUNDIALS on GNU/Linux + timeout-minutes: 10 + run: python -m nox -s pybamm-requires + + - name: Run example scripts tests for GNU/Linux with Python 3.12 run: python -m nox -s scripts # M-series Mac Mini build-apple-mseries: if: github.repository_owner == 'pybamm-team' - needs: style runs-on: [self-hosted, macOS, ARM64] env: GITHUB_PATH: ${PYENV_ROOT/bin:$PATH} @@ -126,10 +261,12 @@ jobs: strategy: fail-fast: false matrix: - python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] + python-version: ["3.9", "3.10", "3.11", "3.12"] steps: - - uses: actions/checkout@v4 + - name: Check out PyBaMM repository + uses: actions/checkout@v4 + - name: Install Python & create virtualenv shell: bash run: | @@ -140,19 +277,12 @@ jobs: - name: Install build-time dependencies & run unit tests for M-series macOS runner shell: bash env: - # Point scikits.odes to the correct SUNDIALS installation - SUNDIALS_INST: $HOME/.local/lib - # Homebrew environment variables HOMEBREW_NO_INSTALL_CLEANUP: 1 NONINTERACTIVE: 1 run: | eval "$(pyenv init -)" pyenv activate pybamm-${{ matrix.python-version }} python -m pip install --upgrade pip nox - # Don't use Homebrew to install SUNDIALS because scikits.odes looks for - # in Homebrew folders instead, which we don't want - brew uninstall sundials --force - pip cache remove scikits.odes python -m nox -s pybamm-requires -- --force python -m nox -s unit @@ -169,50 +299,3 @@ jobs: eval "$(pyenv init -)" pyenv activate pybamm-${{ matrix.python-version }} pyenv uninstall -f $( python --version ) - - test_install_odes: - runs-on: ${{ matrix.os }} - strategy: - matrix: - os: [ubuntu-latest, macos-latest] - python-version: ["3.8", "3.9", "3.10", "3.11"] - fail-fast: false - name: Test pybamm_install_odes on ${{ matrix.os }} - - steps: - - name: Check out PyBaMM repository - uses: actions/checkout@v4 - - - name: Install Linux system dependencies - if: matrix.os == 'ubuntu-latest' - run: | - sudo apt-get update - sudo apt-get install gfortran gcc libopenblas-dev - - name: Install macOS system dependencies - if: matrix.os == 'macos-latest' - env: - # Homebrew environment variables - HOMEBREW_NO_INSTALL_CLEANUP: 1 - HOMEBREW_NO_AUTO_UPDATE: 1 - HOMEBREW_NO_COLOR: 1 - # Speed up CI - NONINTERACTIVE: 1 - run: | - brew analytics off - brew install openblas - brew reinstall gcc gfortran - - - name: Set up Python ${{ matrix.python-version }} - id: setup-python - uses: actions/setup-python@v4 - with: - python-version: ${{ matrix.python-version }} - - - name: Install PyBaMM - run: python -m pip install -e . - - - name: Test pybamm_install_odes on ${{ matrix.os }} - run: | - python -m pip cache purge - python -m pip install wget cmake - pybamm_install_odes diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml index 7297f48fad..97c37e8c28 100644 --- a/.github/workflows/test_on_push.yml +++ b/.github/workflows/test_on_push.yml @@ -28,31 +28,24 @@ jobs: - name: Check style run: | python -m pip install pre-commit - git add . - pre-commit run ruff + pre-commit run -a - run_unit_tests: + run_unit_integration_and_coverage_tests: needs: style runs-on: ${{ matrix.os }} strategy: fail-fast: false matrix: - os: [ubuntu-latest, macos-latest, windows-latest] - python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] - # We check coverage on Ubuntu with Python 3.11, so we skip unit tests for it here - # TODO: check coverage with Python 3.12 when [odes] supports it - exclude: - - os: ubuntu-latest - python-version: "3.11" - name: Unit tests (${{ matrix.os }} / Python ${{ matrix.python-version }}) + os: [ubuntu-latest, macos-12, macos-14, windows-latest] + python-version: ["3.9", "3.10", "3.11", "3.12"] + name: Tests (${{ matrix.os }} / Python ${{ matrix.python-version }}) steps: - name: Check out PyBaMM repository uses: actions/checkout@v4 - # Install and cache apt packages - name: Install Linux system dependencies - uses: awalsh128/cache-apt-pkgs-action@v1.3.1 + uses: awalsh128/cache-apt-pkgs-action@v1.4.2 if: matrix.os == 'ubuntu-latest' with: packages: gfortran gcc graphviz pandoc @@ -67,9 +60,8 @@ jobs: sudo apt-get install libopenblas-dev texlive-latex-extra dvipng - name: Install macOS system dependencies - if: matrix.os == 'macos-latest' + if: matrix.os == 'macos-12' || matrix.os == 'macos-14' env: - # Homebrew environment variables HOMEBREW_NO_INSTALL_CLEANUP: 1 HOMEBREW_NO_AUTO_UPDATE: 1 HOMEBREW_NO_COLOR: 1 @@ -78,7 +70,7 @@ jobs: # sometimes gfortran cannot be found, so reinstall gcc just to be sure run: | brew analytics off - brew install graphviz openblas libomp + brew install graphviz libomp brew reinstall gcc - name: Install Windows system dependencies @@ -96,7 +88,7 @@ jobs: run: python -m pip install nox - name: Cache pybamm-requires nox environment for GNU/Linux and macOS - uses: actions/cache@v3 + uses: actions/cache@v4 if: matrix.os != 'windows-latest' with: path: | @@ -109,165 +101,43 @@ jobs: key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py', '**/test_on_push.yml') }} - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS + timeout-minutes: 10 if: matrix.os != 'windows-latest' run: python -m nox -s pybamm-requires - name: Run unit tests for ${{ matrix.os }} with Python ${{ matrix.python-version }} + if: matrix.os != 'ubuntu-latest' || matrix.python-version != '3.12' run: python -m nox -s unit - # Runs only on Ubuntu with Python 3.11 - # TODO: check coverage with Python 3.12 when [odes] supports it - check_coverage: - needs: style - runs-on: ubuntu-latest - strategy: - fail-fast: false - name: Coverage tests (ubuntu-latest / Python 3.11) - - steps: - - name: Check out PyBaMM repository - uses: actions/checkout@v4 - - # Install and cache apt packages - - name: Install Linux system dependencies - uses: awalsh128/cache-apt-pkgs-action@v1.3.1 - with: - packages: gfortran gcc graphviz pandoc - execute_install_scripts: true - - # dot -c is for registering graphviz fonts and plugins - - name: Install OpenBLAS and TeXLive for Linux - run: | - sudo apt-get update - sudo dot -c - sudo apt-get install libopenblas-dev texlive-latex-extra dvipng - - - name: Set up Python 3.11 - id: setup-python - uses: actions/setup-python@v5 - with: - python-version: 3.11 - cache: 'pip' - - - name: Install nox - run: python -m pip install nox - - - name: Cache pybamm-requires nox environment for GNU/Linux - uses: actions/cache@v3 - with: - path: | - # Repository files - ${{ github.workspace }}/pybind11/ - ${{ github.workspace }}/install_KLU_Sundials/ - # Headers and dynamic library files for SuiteSparse and SUNDIALS - ${{ env.HOME }}/.local/lib/ - ${{ env.HOME }}/.local/include/ - key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py', '**/test_on_push.yml') }} - - - name: Install SuiteSparse and SUNDIALS on GNU/Linux - run: python -m nox -s pybamm-requires - - - name: Run unit tests for Ubuntu with Python 3.11 and generate coverage report + - name: Run coverage tests for ${{ matrix.os }} with Python ${{ matrix.python-version }} + if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.12' run: python -m nox -s coverage - name: Upload coverage report - uses: codecov/codecov-action@v3.1.4 - - run_integration_tests: - needs: style - runs-on: ${{ matrix.os }} - strategy: - fail-fast: false - matrix: - os: [ubuntu-latest, macos-latest, windows-latest] - python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] - name: Integration tests (${{ matrix.os }} / Python ${{ matrix.python-version }}) - - steps: - - name: Check out PyBaMM repository - uses: actions/checkout@v4 - - # Install and cache apt packages - - name: Install Linux system dependencies - uses: awalsh128/cache-apt-pkgs-action@v1.3.1 - if: matrix.os == 'ubuntu-latest' + if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.12' + uses: codecov/codecov-action@v4.5.0 with: - packages: gfortran gcc graphviz pandoc - execute_install_scripts: true - - # dot -c is for registering graphviz fonts and plugins - - name: Install OpenBLAS and TeXLive for Linux - if: matrix.os == 'ubuntu-latest' - run: | - sudo apt-get update - sudo dot -c - sudo apt-get install libopenblas-dev texlive-latex-extra dvipng - - - name: Install macOS system dependencies - if: matrix.os == 'macos-latest' - env: - # Homebrew environment variables - HOMEBREW_NO_INSTALL_CLEANUP: 1 - HOMEBREW_NO_AUTO_UPDATE: 1 - HOMEBREW_NO_COLOR: 1 - # Speed up CI - NONINTERACTIVE: 1 - # sometimes gfortran cannot be found, so reinstall gcc just to be sure - run: | - brew analytics off - brew install graphviz openblas libomp - brew reinstall gcc - - - name: Install Windows system dependencies - if: matrix.os == 'windows-latest' - run: choco install graphviz --version=8.0.5 - - - name: Set up Python ${{ matrix.python-version }} - id: setup-python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python-version }} - cache: 'pip' - - - name: Install nox - run: python -m pip install nox - - - name: Cache pybamm-requires nox environment for GNU/Linux and macOS - uses: actions/cache@v3 - if: matrix.os != 'windows-latest' - with: - path: | - # Repository files - ${{ github.workspace }}/pybind11/ - ${{ github.workspace }}/install_KLU_Sundials/ - # Headers and dynamic library files for SuiteSparse and SUNDIALS - ${{ env.HOME }}/.local/lib/ - ${{ env.HOME }}/.local/include/ - key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py', '**/test_on_push.yml') }} - - - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS - if: matrix.os != 'windows-latest' - run: python -m nox -s pybamm-requires + token: ${{ secrets.CODECOV_TOKEN }} - name: Run integration tests for ${{ matrix.os }} with Python ${{ matrix.python-version }} run: python -m nox -s integration -# Runs only on Ubuntu with Python 3.12. Skips IDAKLU module compilation -# for speedups, which is already tested in other jobs. + # Skips IDAKLU module compilation for speedups, which is already tested in other jobs. run_doctests: needs: style runs-on: ubuntu-latest strategy: fail-fast: false - name: Doctests (ubuntu-latest / Python 3.12) + name: Doctests (ubuntu-latest / Python 3.11) steps: - name: Check out PyBaMM repository uses: actions/checkout@v4 + with: + fetch-depth: 0 - # Install and cache apt packages - name: Install Linux system dependencies - uses: awalsh128/cache-apt-pkgs-action@v1.3.1 + uses: awalsh128/cache-apt-pkgs-action@v1.4.2 with: packages: graphviz pandoc execute_install_scripts: true @@ -279,23 +149,22 @@ jobs: sudo dot -c sudo apt-get install texlive-latex-extra dvipng - - name: Set up Python 3.12 + - name: Set up Python id: setup-python uses: actions/setup-python@v5 with: - python-version: 3.12 + python-version: 3.11 cache: 'pip' - name: Install nox run: python -m pip install nox - - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.12 + - name: Install docs dependencies and run doctests for GNU/Linux run: python -m nox -s doctests - - name: Check if the documentation can be built for GNU/Linux with Python 3.12 + - name: Check if the documentation can be built for GNU/Linux run: python -m nox -s docs - # Runs only on Ubuntu with Python 3.12 run_example_tests: needs: style runs-on: ubuntu-latest @@ -307,9 +176,8 @@ jobs: - name: Check out PyBaMM repository uses: actions/checkout@v4 - # Install and cache apt packages - name: Install Linux system dependencies - uses: awalsh128/cache-apt-pkgs-action@v1.3.1 + uses: awalsh128/cache-apt-pkgs-action@v1.4.2 with: packages: gfortran gcc graphviz pandoc execute_install_scripts: true @@ -332,7 +200,7 @@ jobs: run: python -m pip install nox - name: Cache pybamm-requires nox environment for GNU/Linux - uses: actions/cache@v3 + uses: actions/cache@v4 with: path: | # Repository files @@ -344,12 +212,12 @@ jobs: key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py', '**/test_on_push.yml') }} - name: Install SuiteSparse and SUNDIALS on GNU/Linux + timeout-minutes: 10 run: python -m nox -s pybamm-requires - name: Run example notebooks tests for GNU/Linux with Python 3.12 run: python -m nox -s examples - # Runs only on Ubuntu with Python 3.12 run_scripts_tests: needs: style runs-on: ubuntu-latest @@ -361,9 +229,8 @@ jobs: - name: Check out PyBaMM repository uses: actions/checkout@v4 - # Install and cache apt packages - name: Install Linux system dependencies - uses: awalsh128/cache-apt-pkgs-action@v1.3.1 + uses: awalsh128/cache-apt-pkgs-action@v1.4.2 with: packages: gfortran gcc graphviz execute_install_scripts: true @@ -386,7 +253,7 @@ jobs: run: python -m pip install nox - name: Cache pybamm-requires nox environment for GNU/Linux - uses: actions/cache@v3 + uses: actions/cache@v4 with: path: | # Repository files @@ -398,6 +265,7 @@ jobs: key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py', '**/test_on_push.yml') }} - name: Install SuiteSparse and SUNDIALS on GNU/Linux + timeout-minutes: 10 run: python -m nox -s pybamm-requires - name: Run example scripts tests for GNU/Linux with Python 3.12 diff --git a/.github/workflows/update_version.yml b/.github/workflows/update_version.yml index f04b033272..eb62469778 100644 --- a/.github/workflows/update_version.yml +++ b/.github/workflows/update_version.yml @@ -44,7 +44,7 @@ jobs: - name: Set up Python uses: actions/setup-python@v5 with: - python-version: 3.8 + python-version: 3.12 - name: Install dependencies run: | @@ -82,7 +82,7 @@ jobs: # create a pull request updating versions in develop - name: Create Pull Request id: version_pr - uses: peter-evans/create-pull-request@v3 + uses: peter-evans/create-pull-request@v6 with: delete-branch: true branch-suffix: short-commit-hash diff --git a/.github/workflows/work_precision_sets.yml b/.github/workflows/work_precision_sets.yml index ba587b6d89..fafc5b1738 100644 --- a/.github/workflows/work_precision_sets.yml +++ b/.github/workflows/work_precision_sets.yml @@ -27,7 +27,7 @@ jobs: python benchmarks/work_precision_sets/time_vs_reltols.py python benchmarks/work_precision_sets/time_vs_abstols.py - name: Create Pull Request - uses: peter-evans/create-pull-request@v5 + uses: peter-evans/create-pull-request@v6 with: delete-branch: true branch-suffix: short-commit-hash diff --git a/.gitignore b/.gitignore index 46c7e02b9f..03750e18b2 100644 --- a/.gitignore +++ b/.gitignore @@ -107,9 +107,6 @@ KLU_module_deps # setup setup.log -# odes setup -scikits_odes_setup.log - # test test.c test.json @@ -129,20 +126,14 @@ html/ results/ # expression tree images for notebooks in docs -!docs/source/examples/notebooks/expression_tree/expression_tree1.png -!docs/source/examples/notebooks/expression_tree/expression_tree2.png -!docs/source/examples/notebooks/expression_tree/expression_tree3.png -!docs/source/examples/notebooks/expression_tree/expression_tree4.png -!docs/source/examples/notebooks/expression_tree/expression_tree5.png +!docs/source/examples/notebooks/expression_tree/*.png # do not ignore SPM images for notebooks in docs -!docs/source/examples/notebooks/models/spm1.png -!docs/source/examples/notebooks/models/spm2.png +!docs/source/examples/notebooks/models/*.png # do not ignore images in _static folder in docs !docs/_static/favicon/favicon.png -!docs/_static/pybamm_logo.png -!docs/_static/pybamm_logo_whitetext.png +!docs/_static/*.png # tests test_callback.log diff --git a/.lycheeignore b/.lycheeignore index fd332a54ff..55a4a4c623 100644 --- a/.lycheeignore +++ b/.lycheeignore @@ -1,5 +1,8 @@ # a list of links/files to be ignored by lychee link checker (see workflow file) +https://github.com/DrTimothyAldenDavis/SuiteSparse/archive/v%7BSUITESPARSE_VERSION%7D.tar.gz https://github.com/LLNL/sundials/releases/download/v%7BSUNDIALS_VERSION%7D/sundials-%7BSUNDIALS_VERSION%7D.tar.gz +https://mac.r-project.org/openmp/openmp-%7BOPENMP_VERSION%7D-darwin20-Release.tar.gz +https://github.com/pybamm-team/pybamm-data/releases/download/%7Bself.version%7D # Errors in docs/source/user_guide/getting_started.md file:///home/runner/work/PyBaMM/PyBaMM/docs/source/user_guide/api_docs diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 3998ad1076..d308a5893d 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -4,7 +4,7 @@ ci: repos: - repo: https://github.com/astral-sh/ruff-pre-commit - rev: "v0.1.9" + rev: "v0.4.10" hooks: - id: ruff args: [--fix, --show-fixes] @@ -19,7 +19,7 @@ repos: additional_dependencies: [black==23.*] - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.5.0 + rev: v4.6.0 hooks: - id: check-added-large-files - id: check-case-conflict diff --git a/.readthedocs.yaml b/.readthedocs.yaml index fb84bce9cb..ae6a74a5cf 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -1,8 +1,3 @@ -# .readthedocs.yaml -# Read the Docs configuration file -# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details - -# Required version: 2 # Build documentation in the docs/ directory with Sphinx @@ -25,16 +20,11 @@ build: os: ubuntu-22.04 tools: python: "3.12" - # You can also specify other tool versions: - # nodejs: "19" - # rust: "1.64" - # golang: "1.19" jobs: # Unshallow the git clone otherwise this may cause issues with Sphinx extensions post_checkout: - git fetch --unshallow - # Altered PDF build and upload job, attributed to - # https://stackoverflow.com/a/76992101/14001839 + # Altered PDF build and upload job # This also runs on PR builds, but does not upload the PDF post_build: - mkdir --parents $READTHEDOCS_OUTPUT/pdf/ diff --git a/CHANGELOG.md b/CHANGELOG.md index 6f713d828d..b8beaa1fab 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,10 +1,77 @@ # [Unreleased](https://github.com/pybamm-team/PyBaMM/) +# [v24.5](https://github.com/pybamm-team/PyBaMM/tree/v24.5) - 2024-07-31 + +## Features + +- Added new parameters `"f{pref]Initial inner SEI on cracks thickness [m]"` and `"f{pref]Initial outer SEI on cracks thickness [m]"`, instead of hardcoding these to `L_inner_0 / 10000` and `L_outer_0 / 10000`. ([#4168](https://github.com/pybamm-team/PyBaMM/pull/4168)) +- Added `pybamm.DataLoader` class to fetch data files from [pybamm-data](https://github.com/pybamm-team/pybamm-data/releases/tag/v1.0.0) and store it under local cache. ([#4098](https://github.com/pybamm-team/PyBaMM/pull/4098)) +- Added `time` as an option for `Experiment.termination`. Now allows solving up to a user-specified time while also allowing different cycles and steps in an experiment to be handled normally. ([#4073](https://github.com/pybamm-team/PyBaMM/pull/4073)) +- Added `plot_thermal_components` to plot the contributions to the total heat generation in a battery ([#4021](https://github.com/pybamm-team/PyBaMM/pull/4021)) +- Added functions for normal probability density function (`pybamm.normal_pdf`) and cumulative distribution function (`pybamm.normal_cdf`) ([#3999](https://github.com/pybamm-team/PyBaMM/pull/3999)) +- "Basic" models are now compatible with experiments ([#3995](https://github.com/pybamm-team/PyBaMM/pull/3995)) +- Updates multiprocess `Pool` in `BaseSolver.solve()` to be constructed with context `fork`. Adds small example for multiprocess inputs. ([#3974](https://github.com/pybamm-team/PyBaMM/pull/3974)) +- Lithium plating now works on composite electrodes ([#3919](https://github.com/pybamm-team/PyBaMM/pull/3919)) +- Added lithium plating parameters to `Ecker2015` and `Ecker2015_graphite_halfcell` parameter sets ([#3919](https://github.com/pybamm-team/PyBaMM/pull/3919)) +- Added custom experiment steps ([#3835](https://github.com/pybamm-team/PyBaMM/pull/3835)) +- MSMR open-circuit voltage model now depends on the temperature ([#3832](https://github.com/pybamm-team/PyBaMM/pull/3832)) +- Added support for macOS arm64 (M-series) platforms. ([#3789](https://github.com/pybamm-team/PyBaMM/pull/3789)) +- Added the ability to specify a custom solver tolerance in `get_initial_stoichiometries` and related functions ([#3714](https://github.com/pybamm-team/PyBaMM/pull/3714)) +- Modified `step` function to take an array of time `t_eval` as an argument and deprecated use of `npts`. ([#3627](https://github.com/pybamm-team/PyBaMM/pull/3627)) +- Renamed "electrode diffusivity" to "particle diffusivity" as a non-breaking change with a deprecation warning ([#3624](https://github.com/pybamm-team/PyBaMM/pull/3624)) +- Add support for BPX version 0.4.0 which allows for blended electrodes and user-defined parameters in BPX([#3414](https://github.com/pybamm-team/PyBaMM/pull/3414)) +- Added `by_submodel` feature in `print_parameter_info` method to allow users to print parameters and types of submodels in a tabular and readable format ([#3628](https://github.com/pybamm-team/PyBaMM/pull/3628)) +- Added `WyciskOpenCircuitPotential` for differential capacity hysteresis state open-circuit potential submodel ([#3593](https://github.com/pybamm-team/PyBaMM/pull/3593)) +- Transport efficiency submodel has new options from the literature relating to different tortuosity factor models and also a new option called "tortuosity factor" for specifying the value or function directly as parameters ([#3437](https://github.com/pybamm-team/PyBaMM/pull/3437)) +- Heat of mixing source term can now be included into thermal models ([#2837](https://github.com/pybamm-team/PyBaMM/pull/2837)) +- Added a JAX interface to the IDAKLU solver ([#3658](https://github.com/pybamm-team/PyBaMM/pull/3658)) + +## Bug Fixes + +- Fixed bug where passing deprecated `electrode diffusivity` parameter resulted in a breaking change and/or the corresponding diffusivity parameter not updating. Improved the deprecated translation around BPX. ([#4176](https://github.com/pybamm-team/PyBaMM/pull/4176)) +- Fixed a bug where a factor of electrode surface area to volume ratio is missing in the rhs of the LeadingOrderDifferential conductivity model ([#4139](https://github.com/pybamm-team/PyBaMM/pull/4139)) +- Fixes the breaking changes caused by [#3624](https://github.com/pybamm-team/PyBaMM/pull/3624), specifically enables the deprecated parameter `electrode diffusivity` to be used by `ParameterValues.update({name:value})` and `Solver.solve(inputs={name:value})`. Fixes parameter translation from old name to new name, with corrected tests. ([#4072](https://github.com/pybamm-team/PyBaMM/pull/4072) +- Set the `remove_independent_variables_from_rhs` to `False` by default, and moved the option from `Discretisation.process_model` to `Discretisation.__init__`. This fixes a bug related to the discharge capacity, but may make the simulation slower in some cases. To set the option to `True`, use `Simulation(..., discretisation_kwargs={"remove_independent_variables_from_rhs": True})`. ([#4020](https://github.com/pybamm-team/PyBaMM/pull/4020)) +- Fixed a bug where independent variables were removed from models even if they appeared in events ([#4019](https://github.com/pybamm-team/PyBaMM/pull/4019)) +- Fix bug with upwind and downwind schemes producing the wrong discretised system ([#3979](https://github.com/pybamm-team/PyBaMM/pull/3979)) +- Allow evaluation of an `Interpolant` object with a number ([#3932](https://github.com/pybamm-team/PyBaMM/pull/3932)) +- Added scale to dead lithium variable ([#3919](https://github.com/pybamm-team/PyBaMM/pull/3919)) +- `plot_voltage_components` now works even if the time does not start at 0 ([#3915](https://github.com/pybamm-team/PyBaMM/pull/3915)) +- Fixed bug where separator porosity was used in calculation instead of transport efficiency ([#3905](https://github.com/pybamm-team/PyBaMM/pull/3905)) +- Initial voltage can now match upper or lower cut-offs exactly ([#3842](https://github.com/pybamm-team/PyBaMM/pull/3842)) +- Fixed a bug where 1+1D and 2+1D models would not work with voltage or power controlled experiments([#3829](https://github.com/pybamm-team/PyBaMM/pull/3829)) +- Update IDAKLU solver to fail gracefully when a variable is requested that was not in the solves `output_variables` list ([#3803](https://github.com/pybamm-team/PyBaMM/pull/3803)) +- Updated `_steps_util.py` to throw a specific exception when drive cycle starts at t>0 ([#3756](https://github.com/pybamm-team/PyBaMM/pull/3756)) +- Updated `plot_voltage_components.py` to support both `Simulation` and `Solution` objects. Added new methods in both `Simulation` and `Solution` classes for allow the syntax `simulation.plot_voltage_components` and `solution.plot_voltage_components`. Updated `test_plot_voltage_components.py` to reflect these changes ([#3723](https://github.com/pybamm-team/PyBaMM/pull/3723)). +- The SEI thickness decreased at some intervals when the 'electron-migration limited' model was used. It has been corrected ([#3622](https://github.com/pybamm-team/PyBaMM/pull/3622)) +- Allow input parameters in ESOH model ([#3921](https://github.com/pybamm-team/PyBaMM/pull/3921)) +- Use casadi MX.interpn_linear function instead of plugin to fix casadi_interpolant_linear.dll not found on Windows ([#4077](https://github.com/pybamm-team/PyBaMM/pull/4077)) + +## Optimizations + +- Sped up initialization of a `ProcessedVariable` by making the internal `xarray.DataArray` initialization lazy (only gets created if interpolation is needed) ([#3862](https://github.com/pybamm-team/PyBaMM/pull/3862)) + +## Breaking changes + +- Functions that are created using `pybamm.Function(function_object, children)` can no longer be differentiated symbolically (e.g. to compute the Jacobian). This should affect no users, since function derivatives for all "standard" functions are explicitly implemented ([#4196](https://github.com/pybamm-team/PyBaMM/pull/4196)) +- Removed data files under `pybamm/input` and released them in a separate repository upstream at [pybamm-data](https://github.com/pybamm-team/pybamm-data/releases/tag/v1.0.0). Note that data files under `pybamm/input/parameters` have not been removed. ([#4098](https://github.com/pybamm-team/PyBaMM/pull/4098)) +- Removed `check_model` argument from `Simulation.solve`. To change the `check_model` option, use `Simulation(..., discretisation_kwargs={"check_model": False})`. ([#4020](https://github.com/pybamm-team/PyBaMM/pull/4020)) +- Removed multiple Docker images. Here on, a single Docker image tagged `pybamm/pybamm:latest` will be provided with both solvers (`IDAKLU` and `JAX`) pre-installed. ([#3992](https://github.com/pybamm-team/PyBaMM/pull/3992)) +- Removed support for Python 3.8 ([#3961](https://github.com/pybamm-team/PyBaMM/pull/3961)) +- Renamed "ocp_soc_0_dimensional" to "ocp_soc_0" and "ocp_soc_100_dimensional" to "ocp_soc_100" ([#3942](https://github.com/pybamm-team/PyBaMM/pull/3942)) +- The ODES solver was removed due to compatibility issues. Users should use IDAKLU, Casadi, or JAX instead. ([#3932](https://github.com/pybamm-team/PyBaMM/pull/3932)) +- Integrated the `[pandas]` extra into the core PyBaMM package, deprecating the `pybamm[pandas]` optional dependency. Pandas is now a required dependency and will be installed upon installing PyBaMM ([#3892](https://github.com/pybamm-team/PyBaMM/pull/3892)) +- Renamed "have_optional_dependency" to "import_optional_dependency" ([#3866](https://github.com/pybamm-team/PyBaMM/pull/3866)) +- Integrated the `[latexify]` extra into the core PyBaMM package, deprecating the `pybamm[latexify]` set of optional dependencies. SymPy is now a required dependency and will be installed upon installing PyBaMM ([#3848](https://github.com/pybamm-team/PyBaMM/pull/3848)) +- Renamed "testing" argument for plots to "show_plot" and flipped its meaning (show_plot=True is now the default and shows the plot) ([#3842](https://github.com/pybamm-team/PyBaMM/pull/3842)) +- Dropped support for BPX version 0.3.0 and below ([#3414](https://github.com/pybamm-team/PyBaMM/pull/3414)) +- The function `get_spatial_var` in `pybamm.QuickPlot.py` is made private. ([#3755](https://github.com/pybamm-team/PyBaMM/pull/3755)) + # [v24.1](https://github.com/pybamm-team/PyBaMM/tree/v24.1) - 2024-01-31 ## Features -- The `pybamm_install_odes` command now includes support for macOS systems and can be used to set up SUNDIALS and install the `scikits.odes` solver on macOS ([#3417](https://github.com/pybamm-team/PyBaMM/pull/3417), [#3706](https://github.com/pybamm-team/PyBaMM/3706])) +- The `pybamm_install_odes` command now includes support for macOS systems and can be used to set up SUNDIALS and install the `scikits.odes` solver on macOS ([#3417](https://github.com/pybamm-team/PyBaMM/pull/3417)) - Added support for Python 3.12 ([#3531](https://github.com/pybamm-team/PyBaMM/pull/3531)) - Added method to get QuickPlot axes by variable ([#3596](https://github.com/pybamm-team/PyBaMM/pull/3596)) - Added custom experiment terminations ([#3596](https://github.com/pybamm-team/PyBaMM/pull/3596)) @@ -13,14 +80,12 @@ - Added a method, `insert_reference_electrode`, to `pybamm.lithium_ion.BaseModel` that insert a reference electrode to measure the electrolyte potential at a given position in space and adds new variables that mimic a 3E cell setup. ([#3573](https://github.com/pybamm-team/PyBaMM/pull/3573)) - Serialisation added so models can be written to/read from JSON ([#3397](https://github.com/pybamm-team/PyBaMM/pull/3397)) - Added a `get_parameter_info` method for models and modified "print_parameter_info" functionality to extract all parameters and their type in a tabular and readable format ([#3584](https://github.com/pybamm-team/PyBaMM/pull/3584)) -- Mechanical parameters are now a function of stoichiometry and temperature ([#3576](https://github.com/pybamm-team/PyBaMM/pull/3576)) - ## Bug fixes - Fixed a bug that lead to a `ShapeError` when specifying "Ambient temperature [K]" as an `Interpolant` with an isothermal model ([#3761](https://github.com/pybamm-team/PyBaMM/pull/3761)) - Fixed a bug where if the first step(s) in a cycle are skipped then the cycle solution started from the model's initial conditions instead of from the last state of the previous cycle ([#3708](https://github.com/pybamm-team/PyBaMM/pull/3708)) -- Fixed a bug where the lumped thermal model conflates cell volume with electrode volume ([#3707](https://github.com/pybamm-team/PyBaMM/pull/3707)) +- Fixed a bug where the lumped thermal model conflates cell volume with electrode volume ([#3707](https://github.com/pybamm-team/PyBaMM/pull/3707)) - Reverted a change to the coupled degradation example notebook that caused it to be unstable for large numbers of cycles ([#3691](https://github.com/pybamm-team/PyBaMM/pull/3691)) - Fixed a bug where simulations using the CasADi-based solvers would fail randomly with the half-cell model ([#3494](https://github.com/pybamm-team/PyBaMM/pull/3494)) - Fixed bug that made identical Experiment steps with different end times crash ([#3516](https://github.com/pybamm-team/PyBaMM/pull/3516)) diff --git a/CITATION.cff b/CITATION.cff index 10e942667c..cc2479e8f3 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -24,6 +24,6 @@ keywords: - "expression tree" - "python" - "symbolic differentiation" -version: "24.1" +version: "24.5" repository-code: "https://github.com/pybamm-team/PyBaMM" title: "Python Battery Mathematical Modelling (PyBaMM)" diff --git a/CMakeLists.txt b/CMakeLists.txt index e9b3675e59..b9fe37c331 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -37,6 +37,7 @@ if(NOT PYBIND11_DIR) endif() add_subdirectory(${PYBIND11_DIR}) +# The sources list should mirror the list in setup.py pybind11_add_module(idaklu pybamm/solvers/c_solvers/idaklu/casadi_functions.cpp pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp @@ -50,6 +51,8 @@ pybind11_add_module(idaklu pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP_solvers.hpp pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.hpp + pybamm/solvers/c_solvers/idaklu/idaklu_jax.cpp + pybamm/solvers/c_solvers/idaklu/idaklu_jax.hpp pybamm/solvers/c_solvers/idaklu/common.hpp pybamm/solvers/c_solvers/idaklu/python.hpp pybamm/solvers/c_solvers/idaklu/python.cpp diff --git a/CODE-OF-CONDUCT.md b/CODE-OF-CONDUCT.md index 5ff091633c..9a5b7ff56d 100644 --- a/CODE-OF-CONDUCT.md +++ b/CODE-OF-CONDUCT.md @@ -1,46 +1,135 @@ +[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg)](https://www.contributor-covenant.org/version/2/1/code_of_conduct.html) + + # PyBaMM Code of Conduct ## Our Pledge -In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation. +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, caste, color, religion, or sexual +identity and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. ## Our Standards -Examples of behavior that contributes to creating a positive environment include: +Examples of behavior that contributes to a positive environment for our +community include: -- Using welcoming and inclusive language -- Being respectful of differing viewpoints and experiences -- Gracefully accepting constructive criticism -- Focusing on what is best for the community -- Showing empathy towards other community members +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the overall + community -Examples of unacceptable behavior by participants include: +Examples of unacceptable behavior include: -- The use of sexualized language or imagery and unwelcome sexual attention or advances -- Trolling, insulting/derogatory comments, and personal or political attacks -- Public or private harassment -- Publishing others' private information, such as a physical or electronic address, without explicit permission -- Other conduct which could reasonably be considered inappropriate in a professional setting +* The use of sexualized language or imagery, and sexual attention or advances of + any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email address, + without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting -## Our Responsibilities +## Enforcement Responsibilities -Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior. +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. -Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful. +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. ## Scope -This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers. +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official email address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. ## Enforcement -Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at pybamm@gmail.com. The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately. +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +[pybamm@pybamm.org](mailto:pybamm@pybamm.org). +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning -Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership. +**Community Impact**: A violation through a single incident or series of +actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or permanent +ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within the +community. ## Attribution -This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at [http://contributor-covenant.org/version/1/4][version] +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.1, available at +[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1]. + +Community Impact Guidelines were inspired by +[Mozilla's code of conduct enforcement ladder][Mozilla CoC]. + +For answers to common questions about this code of conduct, see the FAQ at +[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at +[https://www.contributor-covenant.org/translations][translations]. -[homepage]: http://contributor-covenant.org -[version]: http://contributor-covenant.org/version/1/4/ +[homepage]: https://www.contributor-covenant.org +[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html +[Mozilla CoC]: https://github.com/mozilla/diversity +[FAQ]: https://www.contributor-covenant.org/faq +[translations]: https://www.contributor-covenant.org/translations diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index b9800dcd61..450a061d39 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -44,9 +44,12 @@ You now have everything you need to start making changes! ### B. Writing your code -6. PyBaMM is developed in [Python](https://en.wikipedia.org/wiki/Python_(programming_language)), and makes heavy use of [NumPy](https://en.wikipedia.org/wiki/NumPy) (see also [NumPy for MatLab users](https://numpy.org/doc/stable/user/numpy-for-matlab-users.html) and [Python for R users](https://www.rebeccabarter.com/blog/2023-09-11-from_r_to_python)). +6. PyBaMM is developed in [Python](https://www.python.org)), and makes heavy use of [NumPy](https://numpy.org/) (see also [NumPy for MatLab users](https://numpy.org/doc/stable/user/numpy-for-matlab-users.html) and [Python for R users](https://www.rebeccabarter.com/blog/2023-09-11-from_r_to_python)). 7. Make sure to follow our [coding style guidelines](#coding-style-guidelines). -8. Commit your changes to your branch with [useful, descriptive commit messages](https://chris.beams.io/posts/git-commit/): Remember these are publicly visible and should still make sense a few months ahead in time. While developing, you can keep using the GitHub issue you're working on as a place for discussion. [Refer to your commits](https://stackoverflow.com/questions/8910271/how-can-i-reference-a-commit-in-an-issue-comment-on-github) when discussing specific lines of code. +8. Commit your changes to your branch with [useful, descriptive commit messages](https://chris.beams.io/posts/git-commit/): Remember these are + publicly visible and should still make sense a few months ahead in time. + While developing, you can keep using the GitHub issue you're working on + as a place for discussion. 9. If you want to add a dependency on another library, or re-use code you found somewhere else, have a look at [these guidelines](#dependencies-and-reusing-code). ### C. Merging your changes with PyBaMM @@ -87,7 +90,12 @@ Class names are CamelCase, and start with an upper case letter, for example `MyO While it's a bad idea for developers to "reinvent the wheel", it's important for users to get a _reasonably sized download and an easy install_. In addition, external libraries can sometimes cease to be supported, and when they contain bugs it might take a while before fixes become available as automatic downloads to PyBaMM users. For these reasons, all dependencies in PyBaMM should be thought about carefully, and discussed on GitHub. -Direct inclusion of code from other packages is possible, as long as their license permits it and is compatible with ours, but again should be considered carefully and discussed in the group. Snippets from blogs and [stackoverflow](https://stackoverflow.com/) can often be included without attribution, but if they solve a particularly nasty problem (or are very hard to read) it's often a good idea to attribute (and document) them, by making a comment with a link in the source code. +Direct inclusion of code from other packages is possible, as long as their +license permits it and is compatible with ours, but again should be +considered carefully and discussed in the group. Snippets from blogs and +stackoverflow can often be included without attribution, but if they solve a +particularly nasty problem (or are very hard to read) it's often a good idea to +attribute (and document) them, by making a comment with a link in the source code. ### Separating dependencies @@ -104,13 +112,13 @@ Only 'core pybamm' is installed by default. The others have to be specified expl PyBaMM utilizes optional dependencies to allow users to choose which additional libraries they want to use. Managing these optional dependencies and their imports is essential to provide flexibility to PyBaMM users. -PyBaMM provides a utility function `have_optional_dependency`, to check for the availability of optional dependencies within methods. This function can be used to conditionally import optional dependencies only if they are available. Here's how to use it: +PyBaMM provides a utility function `import_optional_dependency`, to check for the availability of optional dependencies within methods. This function can be used to conditionally import optional dependencies only if they are available. Here's how to use it: Optional dependencies should never be imported at the module level, but always inside methods. For example: ``` def use_pybtex(x,y,z): - pybtex = have_optional_dependency("pybtex") + pybtex = import_optional_dependency("pybtex") ... ``` @@ -118,7 +126,7 @@ While importing a specific module instead of an entire package/library: ```python def use_parse_file(x, y, z): - parse_file = have_optional_dependency("pybtex.database", "parse_file") + parse_file = import_optional_dependency("pybtex.database", "parse_file") ... ``` @@ -126,26 +134,18 @@ This allows people to (1) use PyBaMM without importing optional dependencies by **Writing Tests for Optional Dependencies** -Whenever a new optional dependency is added for optional functionality, it is recommended to write a corresponding unit test in `test_util.py`. This ensures that an error is raised upon the absence of said dependency. Here's an example: +Below, we list the currently available test functions to provide an overview. If you find it useful to add new test cases please do so within `tests/unit/test_util.py`. -```python -from tests import TestCase -import pybamm - - -class TestUtil(TestCase): - def test_optional_dependency(self): - # Test that an error is raised when pybtex is not available - with self.assertRaisesRegex( - ModuleNotFoundError, "Optional dependency pybtex is not available" - ): - sys.modules["pybtex"] = None - pybamm.function_using_pybtex(x, y, z) - - # Test that the function works when pybtex is available - sys.modules["pybtex"] = pybamm.util.have_optional_dependency("pybtex") - pybamm.function_using_pybtex(x, y, z) -``` +Currently, there are three functions to test what concerns optional dependencies: +- `test_import_optional_dependency` +- `test_pybamm_import` +- `test_optional_dependencies` + +The `test_import_optional_dependency` function extracts the optional dependencies installed in the setup environment, makes them unimportable (by setting them to `None` among the `sys.modules`), and tests that the `pybamm.util.import_optional_dependency` function throws a `ModuleNotFoundError` exception when their import is attempted. + +The `test_pybamm_import` function extracts the optional dependencies installed in the setup environment and makes them unimportable (by setting them to `None` among the `sys.modules`), unloads `pybamm` and its sub-modules, and finally tests that `pybamm` can be imported successfully. In fact, it is essential that the `pybamm` package is importable with only the mandatory dependencies. + +The `test_optional_dependencies` function extracts `pybamm` mandatory distribution packages and verifies that they are not present in the optional distribution packages list in `pyproject.toml`. This test is crucial for ensuring the consistency of the released package information and potential updates to dependencies during development. ## Testing diff --git a/MANIFEST.in b/MANIFEST.in index 0d05e9f158..9481c2d875 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -2,6 +2,7 @@ graft pybamm include CITATION.cff prune tests -exclude CHANGELOG.md CODE-OF-CONDUCT.md CONTRIBUTING.md CMakeLists.txt +exclude CHANGELOG.md CODE-OF-CONDUCT.md CONTRIBUTING.md all_contributors.md +include CMakeLists.txt global-exclude __pycache__ *.py[cod] .venv diff --git a/README.md b/README.md index 2aa9220e70..e09e21e26c 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ [![code style](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff) -[![All Contributors](https://img.shields.io/badge/all_contributors-73-orange.svg)](#-contributors) +[![All Contributors](https://img.shields.io/badge/all_contributors-88-orange.svg)](#-contributors) @@ -128,9 +128,8 @@ conda install -c conda-forge pybamm ### Optional solvers -Following GNU/Linux and macOS solvers are optionally available: +The following solvers are optionally available: -- [scikits.odes](https://scikits-odes.readthedocs.io/en/latest/)-based solver, see [the documentation](https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-scikits-odes-solver). - [jax](https://jax.readthedocs.io/en/latest/notebooks/quickstart.html)-based solver, see [the documentation](https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-jaxsolver). ## 📖 Citing PyBaMM @@ -170,7 +169,8 @@ If you'd like to help us develop PyBaMM by adding new methods, writing documenta ## 📫 Get in touch -For any questions, comments, suggestions or bug reports, please see the [contact page](https://www.pybamm.org/contact). +For any questions, comments, suggestions or bug reports, please see the +[contact page](https://www.pybamm.org/community). ## 📃 License @@ -178,114 +178,6 @@ PyBaMM is fully open source. For more information about its license, see [LICENS ## ✨ Contributors -Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)): - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Valentin Sulzer
Valentin Sulzer
🐛 💻 📖 💡 🤔 🚧 👀 ⚠️ ✅ 📝		Robert Timms
Robert Timms
🐛 💻 📖 💡 🤔 🚧 👀 ⚠️ ✅		Scott Marquis
Scott Marquis
🐛 💻 📖 💡 🤔 🚧 👀 ⚠️ ✅		Martin Robinson
Martin Robinson
🐛 💻 📖 💡 🤔 👀 ⚠️ ✅		Ferran Brosa Planella
Ferran Brosa Planella
👀 🐛 💻 📖 💡 🤔 🚧 ⚠️ ✅ 📝		Tom Tranter
Tom Tranter
🐛 💻 📖 💡 🤔 👀 ⚠️ ✅		Thibault Lestang
Thibault Lestang
🐛 💻 📖 💡 🤔 👀 ⚠️ 🚇
Diego
Diego
🐛 👀 💻 🚇		felipe-salinas
felipe-salinas
💻 ⚠️		suhaklee
suhaklee
💻 ⚠️		viviantran27
viviantran27
💻 ⚠️		gyouhoc
gyouhoc
🐛 💻 ⚠️		Yannick Kuhn
Yannick Kuhn
💻 ⚠️		Jacqueline Edge
Jacqueline Edge
🤔 📋 🔍
Fergus Cooper
Fergus Cooper
💻 ⚠️		jonchapman1
jonchapman1
🤔 🔍		Colin Please
Colin Please
🤔 🔍		cwmonroe
cwmonroe
🤔 🔍		Greg
Greg
🤔 🔍		Faraday Institution
Faraday Institution
💵		Alexander Bessman
Alexander Bessman
🐛 💡
dalbamont
dalbamont
💻		Anand Mohan Yadav
Anand Mohan Yadav
📖		WEILONG AI
WEILONG AI
💻 💡 ⚠️		lonnbornj
lonnbornj
💻 ⚠️ 💡		Priyanshu Agarwal
Priyanshu Agarwal
⚠️ 💻 🐛 👀 🚧 ✅		DrSOKane
DrSOKane
💻 💡 📖 ⚠️ ✅ 👀		Saransh Chopra
Saransh Chopra
💻 ⚠️ 📖 ✅ 👀 🚧
David Straub
David Straub
🐛 💻		maurosgroi
maurosgroi
🤔		Amarjit Singh Gaba
Amarjit Singh Gaba
💻		KennethNwanoro
KennethNwanoro
💻 ⚠️		Ali Hussain Umar Bhatti
Ali Hussain Umar Bhatti
💻 ⚠️		Leshinka Molel
Leshinka Molel
💻 🤔		tobykirk
tobykirk
🤔 💻 ⚠️ ✅
Chuck Liu
Chuck Liu
🐛 💻		partben
partben
📖		Gavin Wiggins
Gavin Wiggins
🐛 💻		Dion Wilde
Dion Wilde
🐛 💻		Elias Hohl
Elias Hohl
💻		KAschad
KAschad
🐛		Vaibhav-Chopra-GT
Vaibhav-Chopra-GT
💻
bardsleypt
bardsleypt
🐛 💻		ndrewwang
ndrewwang
🐛 💻		MichaPhilipp
MichaPhilipp
🐛		Alec Bills
Alec Bills
💻		Agriya Khetarpal
Agriya Khetarpal
🚇 💻 📖 👀		Alex Wadell
Alex Wadell
💻 ⚠️ 📖		iatzak
iatzak
📖 🐛 💻
Ankit Kumar
Ankit Kumar
💻		Aniket Singh Rawat
Aniket Singh Rawat
💻 📖		Jerom Palimattom Tom
Jerom Palimattom Tom
📖 💻 ⚠️		Brady Planden
Brady Planden
💡		jsbrittain
jsbrittain
💻 ⚠️		Arjun
Arjun
🚇 💻 📖 👀		CHEN ZHAO
CHEN ZHAO
🐛
darryl-ad
darryl-ad
💻 🐛 🤔		julian-evers
julian-evers
💻		Jason Siegel
Jason Siegel
💻 🤔		Tom Maull
Tom Maull
💻 ⚠️		ejfdickinson
ejfdickinson
🤔 🐛		bobonice
bobonice
🐛 💻		Eric G. Kratz
Eric G. Kratz
📖 🚇 🐛 💻 ⚠️
Andrés Ignacio Torres
Andrés Ignacio Torres
🚇		Agnik Bakshi
Agnik Bakshi
📖		RuiheLi
RuiheLi
💻 ⚠️		chmabaur
chmabaur
🐛 💻		Abhishek Chaudhari
Abhishek Chaudhari
📖 💻		Shubham Bhardwaj
Shubham Bhardwaj
🚇		Jonathan Lauber
Jonathan Lauber
🚇
Pradyot Ranjan
Pradyot Ranjan
🚇		XuboGU
XuboGU
💻 🐛		Ankit Meda
Ankit Meda
💻
- - - - - - -This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! +This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind are welcome! + +Click here to see [a full list](https://github.com/pybamm-team/PyBaMM/blob/develop/all_contributors.md) of our contributors' profiles. diff --git a/all_contributors.md b/all_contributors.md new file mode 100644 index 0000000000..c41e5ec36e --- /dev/null +++ b/all_contributors.md @@ -0,0 +1,128 @@ +Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)): + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Valentin Sulzer
Valentin Sulzer
🐛 💻 📖 💡 🤔 🚧 👀 ⚠️ ✅ 📝		Robert Timms
Robert Timms
🐛 💻 📖 💡 🤔 🚧 👀 ⚠️ ✅		Scott Marquis
Scott Marquis
🐛 💻 📖 💡 🤔 🚧 👀 ⚠️ ✅		Martin Robinson
Martin Robinson
🐛 💻 📖 💡 🤔 👀 ⚠️ ✅		Ferran Brosa Planella
Ferran Brosa Planella
👀 🐛 💻 📖 💡 🤔 🚧 ⚠️ ✅ 📝		Tom Tranter
Tom Tranter
🐛 💻 📖 💡 🤔 👀 ⚠️ ✅		Thibault Lestang
Thibault Lestang
🐛 💻 📖 💡 🤔 👀 ⚠️ 🚇
Diego
Diego
🐛 👀 💻 🚇		felipe-salinas
felipe-salinas
💻 ⚠️		suhaklee
suhaklee
💻 ⚠️		viviantran27
viviantran27
💻 ⚠️		gyouhoc
gyouhoc
🐛 💻 ⚠️		Yannick Kuhn
Yannick Kuhn
💻 ⚠️		Jacqueline Edge
Jacqueline Edge
🤔 📋 🔍
Fergus Cooper
Fergus Cooper
💻 ⚠️		jonchapman1
jonchapman1
🤔 🔍 📖		Colin Please
Colin Please
🤔 🔍		cwmonroe
cwmonroe
🤔 🔍		Greg
Greg
🤔 🔍		Faraday Institution
Faraday Institution
💵		Alexander Bessman
Alexander Bessman
🐛 💡
dalbamont
dalbamont
💻		Anand Mohan Yadav
Anand Mohan Yadav
📖		WEILONG AI
WEILONG AI
💻 💡 ⚠️		lonnbornj
lonnbornj
💻 ⚠️ 💡		Priyanshu Agarwal
Priyanshu Agarwal
⚠️ 💻 🐛 👀 🚧 ✅		DrSOKane
DrSOKane
💻 💡 📖 ⚠️ ✅ 👀		Saransh Chopra
Saransh Chopra
💻 ⚠️ 📖 ✅ 👀 🚧
David Straub
David Straub
🐛 💻		maurosgroi
maurosgroi
🤔		Amarjit Singh Gaba
Amarjit Singh Gaba
💻		KennethNwanoro
KennethNwanoro
💻 ⚠️		Ali Hussain Umar Bhatti
Ali Hussain Umar Bhatti
💻 ⚠️		Leshinka Molel
Leshinka Molel
💻 🤔		tobykirk
tobykirk
🤔 💻 ⚠️ ✅
Chuck Liu
Chuck Liu
🐛 💻		partben
partben
📖		Gavin Wiggins
Gavin Wiggins
🐛 💻		Dion Wilde
Dion Wilde
🐛 💻		Elias Hohl
Elias Hohl
💻		KAschad
KAschad
🐛		Vaibhav-Chopra-GT
Vaibhav-Chopra-GT
💻
bardsleypt
bardsleypt
🐛 💻		ndrewwang
ndrewwang
🐛 💻		MichaPhilipp
MichaPhilipp
🐛		Alec Bills
Alec Bills
💻		Agriya Khetarpal
Agriya Khetarpal
🚇 💻 📖 👀		Alex Wadell
Alex Wadell
💻 ⚠️ 📖		iatzak
iatzak
📖 🐛 💻
Ankit Kumar
Ankit Kumar
💻		Aniket Singh Rawat
Aniket Singh Rawat
💻 📖		Jerom Palimattom Tom
Jerom Palimattom Tom
📖 💻 ⚠️		Brady Planden
Brady Planden
💡		jsbrittain
jsbrittain
💻 ⚠️		Arjun
Arjun
🚇 💻 📖 👀		CHEN ZHAO
CHEN ZHAO
🐛
darryl-ad
darryl-ad
💻 🐛 🤔		julian-evers
julian-evers
💻		Jason Siegel
Jason Siegel
💻 🤔		Tom Maull
Tom Maull
💻 ⚠️		ejfdickinson
ejfdickinson
🤔 🐛		bobonice
bobonice
🐛 💻		Eric G. Kratz
Eric G. Kratz
📖 🚇 🐛 💻 ⚠️
Andrés Ignacio Torres
Andrés Ignacio Torres
🚇		Agnik Bakshi
Agnik Bakshi
📖		RuiheLi
RuiheLi
💻 ⚠️		chmabaur
chmabaur
🐛 💻		Abhishek Chaudhari
Abhishek Chaudhari
📖 💻		Shubham Bhardwaj
Shubham Bhardwaj
🚇		Jonathan Lauber
Jonathan Lauber
🚇
Pradyot Ranjan
Pradyot Ranjan
🚇 💻 ⚠️		XuboGU
XuboGU
💻 🐛		Ankit Meda
Ankit Meda
💻		Alessio Bugetti
Alessio Bugetti
🚇 💻 📖 ⚠️		kawaMANMI
kawaMANMI
🐛 💻		AKHIL SHARMA
AKHIL SHARMA
📖		Harshvir Sandhu
Harshvir Sandhu
💻
Lorenzo
Lorenzo
💻 ⚠️ 📖		AndyLiuElysia
AndyLiuElysia
📖		Hongmeiqi
Hongmeiqi
📖		mleot
mleot
💻 ⚠️		Abhi ram
Abhi ram
⚠️		Caitlin D. Parke
Caitlin D. Parke
💻		Andres Felipe Galvis Rodriguez
Andres Felipe Galvis Rodriguez
💻
Ivan Korotkin
Ivan Korotkin
💻		Santhosh
Santhosh
💻 🚇		Smita Sahu
Smita Sahu
💻		Ubham16
Ubham16
💻
+ + + + + diff --git a/asv.conf.json b/asv.conf.json index 98f1a9b282..bf9935ce95 100644 --- a/asv.conf.json +++ b/asv.conf.json @@ -25,10 +25,12 @@ // "uninstall_command": ["return-code=any python -mpip uninstall -y {project}"], "build_command": [ "/bin/rm -rf pybind11", - "/usr/bin/git clone --depth 1 --branch v2.6.2 https://github.com/pybind/pybind11.git", - "python setup.py build", - "PIP_NO_BUILD_ISOLATION=false python -mpip wheel --no-deps --no-index -w {build_cache_dir} {build_dir}" + "/usr/bin/git clone --depth 1 --branch v2.12.0 https://github.com/pybind/pybind11.git pybind11 -c advice.detachedHead=false", + "python -m pip install build", + "python -m build --wheel -o {build_cache_dir} {build_dir}" ], + "build_cache_dir": ".asv/cache", + "build_dir": ".asv/build", // List of branches to benchmark. If not provided, defaults to "master" // (for git) or "default" (for mercurial). @@ -80,7 +82,6 @@ "wget": [], "cmake": [], "anytree": [], - "autograd": [], "scikit-fem": [], "imageio": [], "pybtex": [], diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py index a4cf787ad9..72767e9f65 100644 --- a/benchmarks/different_model_options.py +++ b/benchmarks/different_model_options.py @@ -5,13 +5,13 @@ def compute_discretisation(model, param): var_pts = { - pybamm.standard_spatial_vars.x_n: 20, - pybamm.standard_spatial_vars.x_s: 20, - pybamm.standard_spatial_vars.x_p: 20, - pybamm.standard_spatial_vars.r_n: 30, - pybamm.standard_spatial_vars.r_p: 30, - pybamm.standard_spatial_vars.y: 10, - pybamm.standard_spatial_vars.z: 10, + "x_n": 20, + "x_s": 20, + "x_p": 20, + "r_n": 30, + "r_p": 30, + "y": 10, + "z": 10, } geometry = model.default_geometry param.process_geometry(geometry) diff --git a/benchmarks/work_precision_sets/time_vs_abstols.py b/benchmarks/work_precision_sets/time_vs_abstols.py index d680766c43..af76493abc 100644 --- a/benchmarks/work_precision_sets/time_vs_abstols.py +++ b/benchmarks/work_precision_sets/time_vs_abstols.py @@ -72,7 +72,7 @@ solver.solve(model, t_eval=t_eval) time = 0 runs = 20 - for k in range(0, runs): + for _ in range(0, runs): solution = solver.solve(model, t_eval=t_eval) time += solution.solve_time.value time = time / runs diff --git a/benchmarks/work_precision_sets/time_vs_dt_max.py b/benchmarks/work_precision_sets/time_vs_dt_max.py index a1f8ca06bc..c9979c4e47 100644 --- a/benchmarks/work_precision_sets/time_vs_dt_max.py +++ b/benchmarks/work_precision_sets/time_vs_dt_max.py @@ -76,7 +76,7 @@ solver.solve(model, t_eval=t_eval) time = 0 runs = 20 - for k in range(0, runs): + for _ in range(0, runs): solution = solver.solve(model, t_eval=t_eval) time += solution.solve_time.value time = time / runs diff --git a/benchmarks/work_precision_sets/time_vs_mesh_size.py b/benchmarks/work_precision_sets/time_vs_mesh_size.py index cbab18d16c..7b8ad525df 100644 --- a/benchmarks/work_precision_sets/time_vs_mesh_size.py +++ b/benchmarks/work_precision_sets/time_vs_mesh_size.py @@ -54,7 +54,7 @@ time = 0 runs = 20 - for k in range(0, runs): + for _ in range(0, runs): solution = sim.solve([0, 3500]) time += solution.solve_time.value time = time / runs diff --git a/benchmarks/work_precision_sets/time_vs_no_of_states.py b/benchmarks/work_precision_sets/time_vs_no_of_states.py index febc69f0a1..fdc039587f 100644 --- a/benchmarks/work_precision_sets/time_vs_no_of_states.py +++ b/benchmarks/work_precision_sets/time_vs_no_of_states.py @@ -54,7 +54,7 @@ time = 0 runs = 20 - for k in range(0, runs): + for _ in range(0, runs): solution = sim.solve([0, 3500]) time += solution.solve_time.value time = time / runs diff --git a/benchmarks/work_precision_sets/time_vs_reltols.py b/benchmarks/work_precision_sets/time_vs_reltols.py index 42e9a1bab1..4afcddf94d 100644 --- a/benchmarks/work_precision_sets/time_vs_reltols.py +++ b/benchmarks/work_precision_sets/time_vs_reltols.py @@ -78,7 +78,7 @@ solver.solve(model, t_eval=t_eval) time = 0 runs = 20 - for k in range(0, runs): + for _ in range(0, runs): solution = solver.solve(model, t_eval=t_eval) time += solution.solve_time.value time = time / runs diff --git a/docs/conf.py b/docs/conf.py index 928fc76cc6..1d26e7ce38 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -46,7 +46,6 @@ extensions = [ # Sphinx extensions "sphinx.ext.autodoc", - "sphinx.ext.doctest", "sphinx.ext.intersphinx", "sphinx.ext.mathjax", "sphinx.ext.viewcode", @@ -73,10 +72,6 @@ napoleon_use_rtype = True napoleon_google_docstring = False -doctest_global_setup = """ -from docs import * -import pybamm -""" # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] @@ -103,7 +98,7 @@ exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", ".ipynb_checkpoints"] # Suppress warnings generated by Sphinx and/or by Sphinx extensions -suppress_warnings = ["git.too_shallow"] +suppress_warnings = [] # -- Options for HTML output ------------------------------------------------- @@ -317,7 +312,7 @@ if os.environ.get("READTHEDOCS_VERSION") == "stable": notebooks_version = version - append_to_url = f"tree/v{notebooks_version}" + append_to_url = f"blob/v{notebooks_version}" if os.environ.get("READTHEDOCS_VERSION_TYPE") == "external": notebooks_version = os.environ.get("READTHEDOCS_GIT_COMMIT_HASH") @@ -371,6 +366,27 @@ """ +if os.environ.get("READTHEDOCS_VERSION") == "latest": + # append another admonition to warn about unreleased features + # note: this needs to be appended with a newline and correct dedentation + nbsphinx_prolog += r""" + +
+

+ Attention +

+

+ You are viewing this notebook on the latest version of the documentation, + where these notebooks may not be compatible with the stable release of + PyBaMM since they can contain features that are not yet released. + We recommend viewing these notebooks from the stable version of the documentation. To install the latest version of PyBaMM that is compatible with the latest notebooks, + build PyBaMM from source. +

+
+ +""" + # -- sphinxext/inheritance_diagram.py options -------------------------------- graphviz_output_format = "svg" diff --git a/docs/source/api/experiment/experiment_steps.rst b/docs/source/api/experiment/experiment_steps.rst index 6a2e2abc31..ca26b02a43 100644 --- a/docs/source/api/experiment/experiment_steps.rst +++ b/docs/source/api/experiment/experiment_steps.rst @@ -1,7 +1,7 @@ Experiment step functions ========================= -The following functions can be used to define steps in an experiment. +The following functions can be used to define steps in an experiment. Note that the drive cycle must start at t=0 .. autofunction:: pybamm.step.string @@ -16,7 +16,18 @@ The following functions can be used to define steps in an experiment. These functions return the following step class, which is not intended to be used directly: -.. autoclass:: pybamm.step._Step +.. autoclass:: pybamm.step.BaseStep + :members: + +Custom steps +------------ + +Custom steps can be defined using either explicit or implicit control: + +.. autoclass:: pybamm.step.CustomStepExplicit + :members: + +.. autoclass:: pybamm.step.CustomStepImplicit :members: Step terminations diff --git a/docs/source/api/expression_tree/functions.rst b/docs/source/api/expression_tree/functions.rst index 027c270acb..2303d7e7c3 100644 --- a/docs/source/api/expression_tree/functions.rst +++ b/docs/source/api/expression_tree/functions.rst @@ -77,3 +77,7 @@ Functions :members: .. autofunction:: pybamm.tanh + +.. autofunction:: pybamm.normal_pdf + +.. autofunction:: pybamm.normal_cdf diff --git a/docs/source/api/index.rst b/docs/source/api/index.rst index 432461a58e..33be0235a7 100644 --- a/docs/source/api/index.rst +++ b/docs/source/api/index.rst @@ -30,3 +30,4 @@ For a high-level introduction to PyBaMM, see the :ref:`user guide ` callbacks citations batch_study + pybamm_data diff --git a/docs/source/api/models/submodels/external_circuit/discharge_throughput.rst b/docs/source/api/models/submodels/external_circuit/discharge_throughput.rst new file mode 100644 index 0000000000..b8c64dad65 --- /dev/null +++ b/docs/source/api/models/submodels/external_circuit/discharge_throughput.rst @@ -0,0 +1,7 @@ +Discharge and throughput variables +================================== + +Calculates the discharge and throughput variables (capacity and power) for the battery. + +.. autoclass:: pybamm.external_circuit.DischargeThroughput + :members: diff --git a/docs/source/api/models/submodels/external_circuit/index.rst b/docs/source/api/models/submodels/external_circuit/index.rst index 21790e4666..b07ae817a6 100644 --- a/docs/source/api/models/submodels/external_circuit/index.rst +++ b/docs/source/api/models/submodels/external_circuit/index.rst @@ -11,5 +11,6 @@ variable to be constant. .. toctree:: :maxdepth: 1 + discharge_throughput explicit_control_external_circuit function_control_external_circuit diff --git a/docs/source/api/models/submodels/interface/open_circuit_potential/index.rst b/docs/source/api/models/submodels/interface/open_circuit_potential/index.rst index fc664adf2b..3a20bccb17 100644 --- a/docs/source/api/models/submodels/interface/open_circuit_potential/index.rst +++ b/docs/source/api/models/submodels/interface/open_circuit_potential/index.rst @@ -7,3 +7,4 @@ Open-circuit potential models current_sigmoid_ocp single_ocp msmr_ocp + wycisk_ocp diff --git a/docs/source/api/models/submodels/interface/open_circuit_potential/wycisk_ocp.rst b/docs/source/api/models/submodels/interface/open_circuit_potential/wycisk_ocp.rst new file mode 100644 index 0000000000..6952a2ee44 --- /dev/null +++ b/docs/source/api/models/submodels/interface/open_circuit_potential/wycisk_ocp.rst @@ -0,0 +1,5 @@ +Wycisk Open Circuit Potential +============================= + +.. autoclass:: pybamm.open_circuit_potential.WyciskOpenCircuitPotential + :members: diff --git a/docs/source/api/models/submodels/particle/msmr_diffusion.rst b/docs/source/api/models/submodels/particle/msmr_diffusion.rst index af7dfe2582..092895e2bd 100644 --- a/docs/source/api/models/submodels/particle/msmr_diffusion.rst +++ b/docs/source/api/models/submodels/particle/msmr_diffusion.rst @@ -4,4 +4,7 @@ MSMR Diffusion .. autoclass:: pybamm.particle.MSMRDiffusion :members: +.. autoclass:: pybamm.particle.MSMRStoichiometryVariables + :members: + .. footbibliography:: diff --git a/docs/source/api/models/submodels/transport_efficiency/bruggeman.rst b/docs/source/api/models/submodels/transport_efficiency/bruggeman.rst new file mode 100644 index 0000000000..548427267b --- /dev/null +++ b/docs/source/api/models/submodels/transport_efficiency/bruggeman.rst @@ -0,0 +1,7 @@ +Bruggeman Transport Efficiency Model +==================================== + +.. autoclass:: pybamm.transport_efficiency.Bruggeman + :members: + +.. footbibliography:: diff --git a/docs/source/api/models/submodels/transport_efficiency/bruggeman_transport_efficiency.rst b/docs/source/api/models/submodels/transport_efficiency/bruggeman_transport_efficiency.rst deleted file mode 100644 index f5e5f1c1bc..0000000000 --- a/docs/source/api/models/submodels/transport_efficiency/bruggeman_transport_efficiency.rst +++ /dev/null @@ -1,5 +0,0 @@ -Bruggeman Model -=============== - -.. autoclass:: pybamm.transport_efficiency.Bruggeman - :members: diff --git a/docs/source/api/models/submodels/transport_efficiency/cation_exchange_membrane.rst b/docs/source/api/models/submodels/transport_efficiency/cation_exchange_membrane.rst new file mode 100644 index 0000000000..c769920cf4 --- /dev/null +++ b/docs/source/api/models/submodels/transport_efficiency/cation_exchange_membrane.rst @@ -0,0 +1,7 @@ +Cation-Exchange Membrane Transport Efficiency Model +=================================================== + +.. autoclass:: pybamm.transport_efficiency.CationExchangeMembrane + :members: + +.. footbibliography:: diff --git a/docs/source/api/models/submodels/transport_efficiency/heterogeneous_catalyst.rst b/docs/source/api/models/submodels/transport_efficiency/heterogeneous_catalyst.rst new file mode 100644 index 0000000000..7ee8c3326a --- /dev/null +++ b/docs/source/api/models/submodels/transport_efficiency/heterogeneous_catalyst.rst @@ -0,0 +1,7 @@ +Heterogeneous Catalyst Transport Efficiency Model +================================================= + +.. autoclass:: pybamm.transport_efficiency.HeterogeneousCatalyst + :members: + +.. footbibliography:: diff --git a/docs/source/api/models/submodels/transport_efficiency/hyperbola_of_revolution.rst b/docs/source/api/models/submodels/transport_efficiency/hyperbola_of_revolution.rst new file mode 100644 index 0000000000..39f2078224 --- /dev/null +++ b/docs/source/api/models/submodels/transport_efficiency/hyperbola_of_revolution.rst @@ -0,0 +1,7 @@ +Hyperbola of Revolution Transport Efficiency Model +================================================== + +.. autoclass:: pybamm.transport_efficiency.HyperbolaOfRevolution + :members: + +.. footbibliography:: diff --git a/docs/source/api/models/submodels/transport_efficiency/index.rst b/docs/source/api/models/submodels/transport_efficiency/index.rst index fcdec7077f..3149caad2c 100644 --- a/docs/source/api/models/submodels/transport_efficiency/index.rst +++ b/docs/source/api/models/submodels/transport_efficiency/index.rst @@ -1,8 +1,15 @@ -transport_efficiency +Transport Efficiency ==================== .. toctree:: :maxdepth: 1 base_transport_efficiency - bruggeman_transport_efficiency + bruggeman + cation_exchange_membrane + heterogeneous_catalyst + hyperbola_of_revolution + ordered_packing + overlapping_spheres + random_overlapping_cylinders + tortuosity_factor diff --git a/docs/source/api/models/submodels/transport_efficiency/ordered_packing.rst b/docs/source/api/models/submodels/transport_efficiency/ordered_packing.rst new file mode 100644 index 0000000000..d0164c983e --- /dev/null +++ b/docs/source/api/models/submodels/transport_efficiency/ordered_packing.rst @@ -0,0 +1,7 @@ +Ordered Packing Transport Efficiency Model +========================================== + +.. autoclass:: pybamm.transport_efficiency.OrderedPacking + :members: + +.. footbibliography:: diff --git a/docs/source/api/models/submodels/transport_efficiency/overlapping_spheres.rst b/docs/source/api/models/submodels/transport_efficiency/overlapping_spheres.rst new file mode 100644 index 0000000000..546e15f63e --- /dev/null +++ b/docs/source/api/models/submodels/transport_efficiency/overlapping_spheres.rst @@ -0,0 +1,7 @@ +Overlapping Spheres Transport Efficiency Model +============================================== + +.. autoclass:: pybamm.transport_efficiency.OverlappingSpheres + :members: + +.. footbibliography:: diff --git a/docs/source/api/models/submodels/transport_efficiency/random_overlapping_cylinders.rst b/docs/source/api/models/submodels/transport_efficiency/random_overlapping_cylinders.rst new file mode 100644 index 0000000000..037468fabf --- /dev/null +++ b/docs/source/api/models/submodels/transport_efficiency/random_overlapping_cylinders.rst @@ -0,0 +1,7 @@ +Random Overlapping Cylinders Transport Efficiency Model +======================================================= + +.. autoclass:: pybamm.transport_efficiency.RandomOverlappingCylinders + :members: + +.. footbibliography:: diff --git a/docs/source/api/models/submodels/transport_efficiency/tortuosity_factor.rst b/docs/source/api/models/submodels/transport_efficiency/tortuosity_factor.rst new file mode 100644 index 0000000000..10913f3c5b --- /dev/null +++ b/docs/source/api/models/submodels/transport_efficiency/tortuosity_factor.rst @@ -0,0 +1,7 @@ +Tortuosity Factor Transport Efficiency Model +============================================ + +.. autoclass:: pybamm.transport_efficiency.TortuosityFactor + :members: + +.. footbibliography:: diff --git a/docs/source/api/pybamm_data.rst b/docs/source/api/pybamm_data.rst new file mode 100644 index 0000000000..16b66fd49c --- /dev/null +++ b/docs/source/api/pybamm_data.rst @@ -0,0 +1,7 @@ +PyBaMM Data +=========== + +.. autoclass:: pybamm.DataLoader + :members: + +.. footbibliography:: diff --git a/docs/source/api/solvers/idaklu_jax.rst b/docs/source/api/solvers/idaklu_jax.rst new file mode 100644 index 0000000000..46dbbfa545 --- /dev/null +++ b/docs/source/api/solvers/idaklu_jax.rst @@ -0,0 +1,5 @@ +IDAKLU-JAX Interface +==================== + +.. autoclass:: pybamm.IDAKLUJax + :members: diff --git a/docs/source/api/solvers/index.rst b/docs/source/api/solvers/index.rst index af2a8893dd..a9aa8ac1dd 100644 --- a/docs/source/api/solvers/index.rst +++ b/docs/source/api/solvers/index.rst @@ -8,7 +8,7 @@ Solvers scipy_solver jax_solver idaklu_solver - scikits_solvers + idaklu_jax casadi_solver algebraic_solvers solution diff --git a/docs/source/api/solvers/scikits_solvers.rst b/docs/source/api/solvers/scikits_solvers.rst deleted file mode 100644 index d440793632..0000000000 --- a/docs/source/api/solvers/scikits_solvers.rst +++ /dev/null @@ -1,8 +0,0 @@ -Scikits.odes Solvers -==================== - -.. autoclass:: pybamm.ScikitsOdeSolver - :members: - -.. autoclass:: pybamm.ScikitsDaeSolver - :members: diff --git a/docs/source/api/util.rst b/docs/source/api/util.rst index 03e7388e3e..f187cfbabb 100644 --- a/docs/source/api/util.rst +++ b/docs/source/api/util.rst @@ -3,8 +3,6 @@ Utility functions .. autofunction:: pybamm.get_git_commit_info -.. autofunction:: pybamm.rmse - .. autofunction:: pybamm.root_dir .. autoclass:: pybamm.Timer diff --git a/docs/source/examples/index.rst b/docs/source/examples/index.rst index e0f2bd5832..a5958b327b 100644 --- a/docs/source/examples/index.rst +++ b/docs/source/examples/index.rst @@ -20,8 +20,6 @@ The notebooks are organised into subfolders, and can be viewed in the galleries notebooks/getting_started/tutorial-7-model-options.ipynb notebooks/getting_started/tutorial-8-solver-options.ipynb notebooks/getting_started/tutorial-9-changing-the-mesh.ipynb - notebooks/getting_started/tutorial-10-creating-a-model.ipynb - notebooks/getting_started/tutorial-11-creating-a-submodel.ipynb .. nbgallery:: :caption: Creating Models @@ -33,6 +31,7 @@ The notebooks are organised into subfolders, and can be viewed in the galleries notebooks/creating_models/4-comparing-full-and-reduced-order-models.ipynb notebooks/creating_models/5-half-cell-model.ipynb notebooks/creating_models/6-a-simple-SEI-model.ipynb + notebooks/creating_models/7-creating-a-submodel.ipynb .. nbgallery:: :caption: Expression Tree @@ -51,6 +50,7 @@ The notebooks are organised into subfolders, and can be viewed in the galleries notebooks/models/compare-particle-diffusion-models.ipynb notebooks/models/composite_particle.ipynb notebooks/models/coupled-degradation.ipynb + notebooks/models/differential-capacity-hysteresis-state.ipynb notebooks/models/DFN-with-particle-size-distributions.ipynb notebooks/models/DFN.ipynb notebooks/models/electrode-state-of-health.ipynb @@ -72,6 +72,7 @@ The notebooks are organised into subfolders, and can be viewed in the galleries notebooks/models/submodel_cracking_DFN_or_SPM.ipynb notebooks/models/loss_of_active_materials.ipynb notebooks/models/thermal-models.ipynb + notebooks/models/tortuosity_models.ipynb notebooks/models/unsteady-heat-equation.ipynb notebooks/models/using-model-options_thermal-example.ipynb notebooks/models/using-submodels.ipynb @@ -109,6 +110,7 @@ The notebooks are organised into subfolders, and can be viewed in the galleries notebooks/solvers/dae-solver.ipynb notebooks/solvers/ode-solver.ipynb + notebooks/solvers/idaklu-jax-interface.ipynb notebooks/solvers/speed-up-solver.ipynb .. nbgallery:: @@ -125,3 +127,4 @@ The notebooks are organised into subfolders, and can be viewed in the galleries notebooks/change-settings.ipynb notebooks/initialize-model-with-solution.ipynb notebooks/solution-data-and-processed-variables.ipynb + notebooks/pybamm_data.ipynb diff --git a/docs/source/examples/notebooks/batch_study.ipynb b/docs/source/examples/notebooks/batch_study.ipynb index 0c0d216763..63169e6a07 100644 --- a/docs/source/examples/notebooks/batch_study.ipynb +++ b/docs/source/examples/notebooks/batch_study.ipynb @@ -199,7 +199,7 @@ "\n", "# changing the value of \"Current function [A]\" in all the parameter values present in the\n", "# parameter_values dictionary\n", - "for k, v, current_value in zip(\n", + "for _, v, current_value in zip(\n", " parameter_values.keys(), parameter_values.values(), current_values\n", "):\n", " v[\"Current function [A]\"] = current_value\n", @@ -505,7 +505,7 @@ "inner_sei_oc_v_values = [2.0e-4, 2.7e-4, 3.4e-4]\n", "\n", "# updating the value of \"Inner SEI open-circuit potential [V]\" in all the dictionary items\n", - "for k, v, inner_sei_oc_v in zip(\n", + "for _, v, inner_sei_oc_v in zip(\n", " parameter_values.keys(), parameter_values.values(), inner_sei_oc_v_values\n", "):\n", " v.update(\n", diff --git a/docs/source/examples/notebooks/change-settings.ipynb b/docs/source/examples/notebooks/change-settings.ipynb index 1a23da86fc..99e46d19d8 100644 --- a/docs/source/examples/notebooks/change-settings.ipynb +++ b/docs/source/examples/notebooks/change-settings.ipynb @@ -36,9 +36,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.3.1 is available.\n", - "You should consider upgrading via the '/home/mrobins/git/PyBaMM/env/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n", - "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n" + "\u001B[33mWARNING: You are using pip version 22.0.4; however, version 22.3.1 is available.\n", + "You should consider upgrading via the '/home/mrobins/git/PyBaMM/env/bin/python -m pip install --upgrade pip' command.\u001B[0m\u001B[33m\n", + "\u001B[0mNote: you may need to restart the kernel to use updated packages.\n" ] } ], @@ -175,7 +175,7 @@ " 'Negative electrode charge transfer coefficient': 0.5,\n", " 'Negative electrode conductivity [S.m-1]': 100.0,\n", " 'Negative electrode density [kg.m-3]': 1657.0,\n", - " 'Negative electrode diffusivity [m2.s-1]': ,\n", + " 'Negative particle diffusivity [m2.s-1]': ,\n", " 'Negative electrode double-layer capacity [F.m-2]': 0.2,\n", " 'Negative electrode electrons in reaction': 1.0,\n", " 'Negative electrode exchange-current density [A.m-2]': ,\n", @@ -210,7 +210,7 @@ " 'Positive electrode charge transfer coefficient': 0.5,\n", " 'Positive electrode conductivity [S.m-1]': 10.0,\n", " 'Positive electrode density [kg.m-3]': 3262.0,\n", - " 'Positive electrode diffusivity [m2.s-1]': ,\n", + " 'Positive particle diffusivity [m2.s-1]': ,\n", " 'Positive electrode double-layer capacity [F.m-2]': 0.2,\n", " 'Positive electrode electrons in reaction': 1.0,\n", " 'Positive electrode exchange-current density [A.m-2]': ,\n", @@ -314,7 +314,7 @@ "Current function [A] 0.680616\n", "Negative electrode conductivity [S.m-1] 100.0\n", "Maximum concentration in negative electrode [mol.m-3] 24983.2619938437\n", - "Negative electrode diffusivity [m2.s-1] \n", + "Negative particle diffusivity [m2.s-1] \n", "Negative electrode OCP [V] \n", "Negative electrode porosity 0.3\n", "Negative electrode active material volume fraction 0.6\n", @@ -332,7 +332,7 @@ "Negative electrode OCP entropic change [V.K-1] \n", "Positive electrode conductivity [S.m-1] 10.0\n", "Maximum concentration in positive electrode [mol.m-3] 51217.9257309275\n", - "Positive electrode diffusivity [m2.s-1] \n", + "Positive particle diffusivity [m2.s-1] \n", "Positive electrode OCP [V] \n", "Positive electrode porosity 0.3\n", "Positive electrode active material volume fraction 0.5\n", diff --git a/docs/source/examples/notebooks/getting_started/tutorial-11-creating-a-submodel.ipynb b/docs/source/examples/notebooks/creating_models/7-creating-a-submodel.ipynb similarity index 96% rename from docs/source/examples/notebooks/getting_started/tutorial-11-creating-a-submodel.ipynb rename to docs/source/examples/notebooks/creating_models/7-creating-a-submodel.ipynb index 45dd6a7702..6303d6bbae 100644 --- a/docs/source/examples/notebooks/getting_started/tutorial-11-creating-a-submodel.ipynb +++ b/docs/source/examples/notebooks/creating_models/7-creating-a-submodel.ipynb @@ -6,9 +6,9 @@ "id": "86a13ed9", "metadata": {}, "source": [ - "# Tutorial 11 - Creating a submodel\n", + "Creating a submodel\n", "\n", - "In [Tutorial 10](./tutorial-10-creating-a-model.ipynb) we showed how to create a simple model from scratch in PyBaMM. In this tutorial we will solve the same problem, but using separate submodels for the linear diffusion problem and the model for the surface flux. In this simple example the surface flux is just some known function of the concentration, so we could just explicitly define it in the model for diffusion. However, we write it as a separate model to show how submodels interact. \n", + "In [Tutorial 3](./3-negative-particle-problem.ipynb) we showed how to create a simple model from scratch in PyBaMM. In this tutorial we will solve a very similar problem, but using separate submodels for the linear diffusion problem and the model for the surface flux. In this simple example the surface flux is just some known function of the concentration, so we could just explicitly define it in the model for diffusion. However, we write it as a separate model to show how submodels interact. \n", "\n", "We solved the problem of linear diffusion on a unit sphere with a flux at the boundary that depends on the concentration\n", "$$\n", diff --git a/docs/source/examples/notebooks/getting_started/tutorial-1-how-to-run-a-model.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-1-how-to-run-a-model.ipynb index 226e016300..26c9cdff29 100644 --- a/docs/source/examples/notebooks/getting_started/tutorial-1-how-to-run-a-model.ipynb +++ b/docs/source/examples/notebooks/getting_started/tutorial-1-how-to-run-a-model.ipynb @@ -17,7 +17,7 @@ "\n", "To run through this jupyter notebook simply shift-enter to run the cells. If you are unfamiliar with Jupyter notebooks we recommend checking out this [cheat sheet](https://www.cheatography.com/weidadeyue/cheat-sheets/jupyter-notebook/pdf_bw/).\n", "\n", - "We begin by importing the PyBaMM library into this notebook:" + "The first we need to do is to import PyBaMM, so we can use its capabilities. Note that the very first line is only needed in Google Colab." ] }, { @@ -29,8 +29,18 @@ "name": "stdout", "output_type": "stream", "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" + ] } ], "source": [ @@ -43,7 +53,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We now load the model that we wish to run. For this notebook, we choose the Doyle-Fuller-Newman (DFN) model:" + "First, we load the model that we wish to run from PyBaMM's model library. For this notebook, we choose the Doyle-Fuller-Newman (DFN) model:" ] }, { @@ -60,7 +70,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We now use this model to create a PyBaMM Simulation, which is used to process and solve the model:" + "We now use this model to create a PyBaMM `Simulation`, which is used to process and solve the model:" ] }, { @@ -77,7 +87,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can then call 'solve' on our simulation object to solve the model, passing the window of time to solve for in seconds (here 1 hour):" + "We can then call `solve` on our simulation object to solve the model, passing the window of time to solve for in seconds (here 1 hour):" ] }, { @@ -88,7 +98,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -105,7 +115,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Finally, we can call 'plot' to generate a dynamic plot of the key variables:" + "Once the simulation is solved, we can call `plot` to generate a dynamic plot of the key variables:" ] }, { @@ -116,7 +126,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f9b1adea8f744bf3bd8ab65f6af94c18", + "model_id": "c4442a3198774c4eb5e826e6fb14c58c", "version_major": 2, "version_minor": 0 }, @@ -126,6 +136,16 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -137,9 +157,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this tutorial, we have solved a model with the inbuilt default settings. However, PyBaMM is designed to be highly customisable. Over the course of the getting started tutorials, we will see how various settings can be changed so that the model is appropriate for your situation. \n", - "\n", - "In [Tutorial 2](./tutorial-2-compare-models.ipynb) we cover how to simulate and compare different models." + "In this tutorial, we have solved a model with the inbuilt default settings. However, PyBaMM is designed to be highly customisable. Over the course of the getting started tutorials, we will see how various settings can be changed so that the model is appropriate for your situation. In [Tutorial 2](./tutorial-2-compare-models.ipynb) we cover how to simulate and compare different models." ] }, { @@ -165,7 +183,7 @@ "[2] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", - "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n", + "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", "\n" ] } @@ -181,12 +199,17 @@ "source": [ "Alternatively, you can print the citations in BibTeX format by running\n", "\n", - "```python\n", + "```python3\n", "pybamm.print_citations(output_format=\"bibtex\")\n", "```\n", "\n", "In both cases, you can pass the extra argument `filename` to store the citations into a file." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } ], "metadata": { @@ -205,7 +228,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.3" + "version": "3.11.6" } }, "nbformat": 4, diff --git a/docs/source/examples/notebooks/getting_started/tutorial-10-creating-a-model.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-10-creating-a-model.ipynb deleted file mode 100644 index c788a773b8..0000000000 --- a/docs/source/examples/notebooks/getting_started/tutorial-10-creating-a-model.ipynb +++ /dev/null @@ -1,454 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "ae6ac58d", - "metadata": {}, - "source": [ - "# Tutorial 10 - Creating a model\n", - "\n", - "In [Tutorial 9](./tutorial-9-changing-the-mesh.ipynb) we showed how to change the mesh using on of the built-in battery models in PyBaMM. In this tutorial we show how to create a simple model from scratch in PyBaMM.\n", - "\n", - "As simple example, we consider the problem of linear diffusion on a unit sphere with a flux at the boundary that depends on the concentration. We solve\n", - "$$\n", - " \\frac{\\partial c}{\\partial t} = \\nabla \\cdot (\\nabla c),\n", - "$$\n", - "with the following boundary and initial conditions:\n", - "$$\n", - " \\left.\\frac{\\partial c}{\\partial r}\\right\\vert_{r=0} = 0, \\quad \\left.\\frac{\\partial c}{\\partial r}\\right\\vert_{r=1} = -j, \\quad \\left.c\\right\\vert_{t=0} = c_0,\n", - "$$\n", - "where\n", - "$$\n", - "j = \\left.j_0(1-c)^{1/2}c^{1/2}\\right\\vert_{r=1}\n", - "$$\n", - "Here $c_0$ and $j_0$ are parameters we can control. In this example we will assume that everything is non-dimensional and focus on how to set up and solve the model rather than any specific physical interpretation." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "7d0ae1c9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "%pip install \"pybamm[plot,cite]\" -q # install PyBaMM if it is not installed\n", - "import pybamm" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f8857ad3", - "metadata": {}, - "source": [ - "## Setting up the model\n", - "\n", - "First we load an empty model. We use the `BaseModel` class that sets up all the basic framework on which our model will be built." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c9b7e903", - "metadata": {}, - "outputs": [], - "source": [ - "model = pybamm.BaseModel()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "797669c5", - "metadata": {}, - "source": [ - "We then define our variables and parameters using the `Variable` and `Parameter` classes, respectively. Since we are solving a PDE we need to tell PyBaMM the domain each variable belongs to so that it can be discretised in space in the correct way. This is done by passing the keyword argument `domain`, and in this example we arbitrarily choose the domain \"negative particle\"." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c39933ef", - "metadata": {}, - "outputs": [], - "source": [ - "c = pybamm.Variable(\"Concentration\", domain=\"negative particle\")\n", - "c0 = pybamm.Parameter(\"Initial concentration\")\n", - "j0 = pybamm.Parameter(\"Flux parameter\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ddcef414", - "metadata": {}, - "source": [ - "We then state out governing equations. In PyBaMM we distinguish between Ordinary Differential Equations of the form $dy/dt = \\text{rhs}$ and Algebraic Equations of the form $f(y) = 0$. The model equations are stored in dictionaries where the key is the variable and the value is the rhs for ODEs and the residual ($f(y)$) for algebraic equations.\n", - "\n", - "Sometime it is useful to define intermediate quantities in order to express the governing equations more easily. In this example we define the flux, then define the rhs to be minus the divergence of the flux. The equation is then added to the dictionary `model.rhs`" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "58d8fb9a", - "metadata": {}, - "outputs": [], - "source": [ - "N = -pybamm.grad(c) # define the flux\n", - "dcdt = -pybamm.div(N) # define the rhs equation\n", - "\n", - "model.rhs = {c: dcdt} # add the equation to rhs dictionary with the variable as the key" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "a960144f", - "metadata": {}, - "source": [ - "Next we add the necessary boundary and initial conditions to the model. These are also stored in dictionaries called `model.boundary_conditions` and `model.initial_conditions`, respectively. " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "90d08a1a", - "metadata": {}, - "outputs": [], - "source": [ - "# boundary conditions\n", - "c_surf = pybamm.surf(c) # concentration at the surface of the sphere\n", - "j = j0 * (1 - c_surf) ** (1 / 2) * c_surf ** (1 / 2) # prescribed boundary flux\n", - "model.boundary_conditions = {c: {\"left\": (0, \"Neumann\"), \"right\": (-j, \"Neumann\")}}\n", - "\n", - "# initial conditions\n", - "model.initial_conditions = {c: c0}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "e44314d3", - "metadata": {}, - "source": [ - "We can add any variables of interest to the dictionary `model.variables`. These can simply be the variables we solve for (in this case $c$) or any other user-defined quantities." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "178b5b86", - "metadata": {}, - "outputs": [], - "source": [ - "model.variables = {\n", - " \"Concentration\": c,\n", - " \"Surface concentration\": c_surf,\n", - " \"Flux\": N,\n", - " \"Boundary flux\": j,\n", - "}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "c7f5ff5f", - "metadata": {}, - "source": [ - "## Setting up the geometry and mesh\n", - "\n", - "In order to solve the model we need to define the geometry and choose how we are going to discretise the equations in space. We first define our radial coordinate using `pybamm.SpatialVariable`. When we define our spatial variable we pass in a name, the domain on which the variable lives, and the coordinate system we want to use." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "43ca8e55", - "metadata": {}, - "outputs": [], - "source": [ - "r = pybamm.SpatialVariable(\n", - " \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f80a28ec", - "metadata": {}, - "source": [ - "We can then define our geometry using a dictionary. The key is the name of the domain, and the value is another dictionary which gives the coordinate to use and the limits. In this case we solve on a unit sphere, so we pass out `SpatialVariable`, `r`, and the limit 0 and 1." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "afd31667", - "metadata": {}, - "outputs": [], - "source": [ - "geometry = {\"negative particle\": {r: {\"min\": 0, \"max\": 1}}}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "3506afa3", - "metadata": {}, - "source": [ - "Finally we choose how we are going to discretise in space. We choose to use the Finite Volume method on a uniform mesh with 20 volumes." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "a2b157f0", - "metadata": {}, - "outputs": [], - "source": [ - "spatial_methods = {\"negative particle\": pybamm.FiniteVolume()}\n", - "submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n", - "var_pts = {r: 20}\n", - "# create a mesh of our geometry, using a uniform grid with 20 volumes\n", - "mesh = pybamm.Mesh(geometry, submesh_types, var_pts)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "e56c1f46", - "metadata": {}, - "source": [ - "## Solving the model\n", - "\n", - "Now we are ready to solve the model. First we need to provide values for the parameters in our model. We do this by passing a dictionary of parameter names and values to the `pybamm.ParameterValues` class." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "c598b5b9", - "metadata": {}, - "outputs": [], - "source": [ - "parameter_values = pybamm.ParameterValues(\n", - " {\n", - " \"Initial concentration\": 0.9,\n", - " \"Flux parameter\": 0.8,\n", - " }\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "0ff5f14f", - "metadata": {}, - "source": [ - "Next we choose a solver. Since this is a system of ODEs we can use the `ScipySolver` which uses a Runge-Kutta scheme by default." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "1a23e584", - "metadata": {}, - "outputs": [], - "source": [ - "solver = pybamm.ScipySolver()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "2f3e3555", - "metadata": {}, - "source": [ - "We can then create a simulation by passing information about the model, geometry, parameters, discretisation and solver to the `pybamm.Simulation` class." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "cbb62ede", - "metadata": {}, - "outputs": [], - "source": [ - "sim = pybamm.Simulation(\n", - " model,\n", - " geometry=geometry,\n", - " parameter_values=parameter_values,\n", - " submesh_types=submesh_types,\n", - " var_pts=var_pts,\n", - " spatial_methods=spatial_methods,\n", - " solver=solver,\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "0131eb76", - "metadata": {}, - "source": [ - "Finally we can solve the model" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "6855600b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sim.solve([0, 1]) # solve up to a time of 1" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "db7fb57c", - "metadata": {}, - "source": [ - "The easiest way to quickly plot the results is to call `sim.plot` to create a slider plot. \n", - "\n", - "Note that at present the `plot` method is set up to plot dimensional results from battery simulations, so the labels include units which can be ignored (the model assumes a default length scale of 1m and default time scale of 1s). Alternatively we could extract the solution data as seen in [Tutorial 6](./tutorial-6-managing-simulation-outputs.ipynb) and create the plots manually. You can find out more about customising plots in [this notebook](../plotting/customize-quick-plot.ipynb)." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "4500bbcf", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022-07-11 13:21:23.957 - [WARNING] processed_variable.get_spatial_scale(521): No length scale set for negative particle. Using default of 1 [m].\n", - "2022-07-11 13:21:23.975 - [WARNING] processed_variable.get_spatial_scale(521): No length scale set for negative particle. Using default of 1 [m].\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9c90fa70ce00427996365547ef9d1ba2", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# pass in a list of the variables we want to plot\n", - "sim.plot([\"Concentration\", \"Surface concentration\", \"Flux\", \"Boundary flux\"])" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "44f9b7ed", - "metadata": {}, - "source": [ - "Here we have seen how to create a basic model from scratch in PyBaMM. \n", - "In the [next tutorial](./tutorial-11-creating-a-submodel.ipynb) we will see how to split this model up into separate submodels." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f73eb971", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "The relevant papers for this notebook are:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "84a906b6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", - "[2] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", - "[3] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", - "[4] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", - "\n" - ] - } - ], - "source": [ - "pybamm.print_citations()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "pybamm", - "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": "3.9.16" - }, - "vscode": { - "interpreter": { - "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/examples/notebooks/getting_started/tutorial-2-compare-models.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-2-compare-models.ipynb index aa957be1b3..d0c2e47dd1 100644 --- a/docs/source/examples/notebooks/getting_started/tutorial-2-compare-models.ipynb +++ b/docs/source/examples/notebooks/getting_started/tutorial-2-compare-models.ipynb @@ -15,7 +15,7 @@ "source": [ "In [Tutorial 1](./tutorial-1-how-to-run-a-model.ipynb), we saw how to run a PyBaMM simulation of the DFN model. However, PyBaMM includes other standard electrochemical models such as the Single Particle Model (SPM) and the Single Particle Model with electrolyte (SPMe). In this tutorial, we will see how to simulate and compare these three models. \n", "\n", - "Again, the first step is to import the pybamm library into the notebook:" + "Again, the first step is to import PyBaMM into the notebook:" ] }, { @@ -27,8 +27,18 @@ "name": "stdout", "output_type": "stream", "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" + ] } ], "source": [ @@ -41,7 +51,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We start creating a list of all the models we wish to solve" + "As we want to compare various models, we now create a list of all the models we wish to solve:" ] }, { @@ -62,7 +72,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "and now we can loop over the list, creating and solving simulations as we go. The solved simulations are stored in the list `sims`" + "We will loop over the list, creating and solving simulations as we go (in the same way we learned in [Tutorial 1](./tutorial-1-how-to-run-a-model.ipynb)), and storing the solved simulations in the list `sims`:" ] }, { @@ -83,7 +93,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can now pass our list of simulations to the dynamic plot method, which will plot the different outputs in the same figure" + "We can now pass our list of simulations to the `dynamic_plot` method, which has similar syntax to the `sim.plot()` method we used earlier and will plot the different solutions in the same figure:" ] }, { @@ -94,7 +104,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "33d9970753e647a98da2824a1a72a6a9", + "model_id": "59b0f0a695d34ce8a5360af8d1c45e94", "version_major": 2, "version_minor": 0 }, @@ -108,7 +118,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -117,7 +127,7 @@ } ], "source": [ - "pybamm.dynamic_plot(sims, time_unit=\"seconds\")" + "pybamm.dynamic_plot(sims)" ] }, { @@ -153,7 +163,7 @@ "[2] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", - "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n", + "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", "\n" ] } @@ -179,7 +189,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.11.6" } }, "nbformat": 4, diff --git a/docs/source/examples/notebooks/getting_started/tutorial-3-basic-plotting.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-3-basic-plotting.ipynb index 022a11e48a..70c818ef95 100644 --- a/docs/source/examples/notebooks/getting_started/tutorial-3-basic-plotting.ipynb +++ b/docs/source/examples/notebooks/getting_started/tutorial-3-basic-plotting.ipynb @@ -25,13 +25,23 @@ "name": "stdout", "output_type": "stream", "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 1, @@ -42,11 +52,10 @@ "source": [ "%pip install \"pybamm[plot,cite]\" -q # install PyBaMM if it is not installed\n", "import pybamm\n", - "import matplotlib.pyplot as plt\n", "\n", - "model_dfn = pybamm.lithium_ion.DFN()\n", - "sim_dfn = pybamm.Simulation(model_dfn)\n", - "sim_dfn.solve([0, 3600])" + "model = pybamm.lithium_ion.DFN()\n", + "sim = pybamm.Simulation(model)\n", + "sim.solve([0, 3600])" ] }, { @@ -54,7 +63,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We now want to plot a selection of the model variables. To see a full list of the available variables just type:" + "There are many more variables available from PyBaMM models than those in the default plots. One can see a full list of the available variables by just calling the `variable_names` method:" ] }, { @@ -79,9 +88,9 @@ " 'Current [A]',\n", " 'C-rate',\n", " 'Discharge capacity [A.h]',\n", + " 'Throughput capacity [A.h]',\n", " 'Discharge energy [W.h]',\n", " 'Throughput energy [W.h]',\n", - " 'Throughput capacity [A.h]',\n", " 'Porosity',\n", " 'Negative electrode porosity',\n", " 'X-averaged negative electrode porosity',\n", @@ -160,54 +169,70 @@ " 'X-averaged positive electrode volume-averaged acceleration [m.s-2]',\n", " 'Positive electrode pressure [Pa]',\n", " 'X-averaged positive electrode pressure [Pa]',\n", - " 'Negative particle stoichiometry',\n", - " 'Negative particle concentration',\n", " 'Negative particle concentration [mol.m-3]',\n", - " 'X-averaged negative particle concentration',\n", " 'X-averaged negative particle concentration [mol.m-3]',\n", - " 'R-averaged negative particle concentration',\n", " 'R-averaged negative particle concentration [mol.m-3]',\n", - " 'Average negative particle concentration',\n", " 'Average negative particle concentration [mol.m-3]',\n", - " 'Negative particle surface stoichiometry',\n", - " 'Negative particle surface concentration',\n", " 'Negative particle surface concentration [mol.m-3]',\n", - " 'X-averaged negative particle surface concentration',\n", " 'X-averaged negative particle surface concentration [mol.m-3]',\n", - " 'Negative electrode extent of lithiation',\n", - " 'X-averaged negative electrode extent of lithiation',\n", - " 'Minimum negative particle concentration',\n", - " 'Maximum negative particle concentration',\n", " 'Minimum negative particle concentration [mol.m-3]',\n", " 'Maximum negative particle concentration [mol.m-3]',\n", + " 'Minimum negative particle Minimum negative particle surface concentration [mol.m-3]',\n", + " 'Maximum negative particle surface concentration [mol.m-3]',\n", + " 'Negative particle concentration',\n", + " 'X-averaged negative particle concentration',\n", + " 'R-averaged negative particle concentration',\n", + " 'Average negative particle concentration',\n", + " 'Negative particle surface concentration',\n", + " 'X-averaged negative particle surface concentration',\n", + " 'Minimum negative particle concentration',\n", + " 'Maximum negative particle concentration',\n", " 'Minimum negative particle surface concentration',\n", " 'Maximum negative particle surface concentration',\n", - " 'Minimum negative particle surface concentration [mol.m-3]',\n", - " 'Maximum negative particle surface concentration [mol.m-3]',\n", - " 'Positive particle stoichiometry',\n", - " 'Positive particle concentration',\n", + " 'Negative particle stoichiometry',\n", + " 'X-averaged negative particle stoichiometry',\n", + " 'R-averaged negative particle stoichiometry',\n", + " 'Average negative particle stoichiometry',\n", + " 'Negative particle surface stoichiometry',\n", + " 'X-averaged negative particle surface stoichiometry',\n", + " 'Minimum negative particle stoichiometry',\n", + " 'Maximum negative particle stoichiometry',\n", + " 'Minimum negative particle surface stoichiometry',\n", + " 'Maximum negative particle surface stoichiometry',\n", + " 'Negative electrode extent of lithiation',\n", + " 'X-averaged negative electrode extent of lithiation',\n", " 'Positive particle concentration [mol.m-3]',\n", - " 'X-averaged positive particle concentration',\n", " 'X-averaged positive particle concentration [mol.m-3]',\n", - " 'R-averaged positive particle concentration',\n", " 'R-averaged positive particle concentration [mol.m-3]',\n", - " 'Average positive particle concentration',\n", " 'Average positive particle concentration [mol.m-3]',\n", - " 'Positive particle surface stoichiometry',\n", - " 'Positive particle surface concentration',\n", " 'Positive particle surface concentration [mol.m-3]',\n", - " 'X-averaged positive particle surface concentration',\n", " 'X-averaged positive particle surface concentration [mol.m-3]',\n", - " 'Positive electrode extent of lithiation',\n", - " 'X-averaged positive electrode extent of lithiation',\n", - " 'Minimum positive particle concentration',\n", - " 'Maximum positive particle concentration',\n", " 'Minimum positive particle concentration [mol.m-3]',\n", " 'Maximum positive particle concentration [mol.m-3]',\n", + " 'Minimum positive particle Minimum positive particle surface concentration [mol.m-3]',\n", + " 'Maximum positive particle surface concentration [mol.m-3]',\n", + " 'Positive particle concentration',\n", + " 'X-averaged positive particle concentration',\n", + " 'R-averaged positive particle concentration',\n", + " 'Average positive particle concentration',\n", + " 'Positive particle surface concentration',\n", + " 'X-averaged positive particle surface concentration',\n", + " 'Minimum positive particle concentration',\n", + " 'Maximum positive particle concentration',\n", " 'Minimum positive particle surface concentration',\n", " 'Maximum positive particle surface concentration',\n", - " 'Minimum positive particle surface concentration [mol.m-3]',\n", - " 'Maximum positive particle surface concentration [mol.m-3]',\n", + " 'Positive particle stoichiometry',\n", + " 'X-averaged positive particle stoichiometry',\n", + " 'R-averaged positive particle stoichiometry',\n", + " 'Average positive particle stoichiometry',\n", + " 'Positive particle surface stoichiometry',\n", + " 'X-averaged positive particle surface stoichiometry',\n", + " 'Minimum positive particle stoichiometry',\n", + " 'Maximum positive particle stoichiometry',\n", + " 'Minimum positive particle surface stoichiometry',\n", + " 'Maximum positive particle surface stoichiometry',\n", + " 'Positive electrode extent of lithiation',\n", + " 'X-averaged positive electrode extent of lithiation',\n", " 'Negative electrode potential [V]',\n", " 'X-averaged negative electrode potential [V]',\n", " 'Negative electrode ohmic losses [V]',\n", @@ -237,6 +262,7 @@ " 'X-averaged positive electrolyte potential [V]',\n", " 'Gradient of positive electrolyte potential [V.m-1]',\n", " 'Ambient temperature [K]',\n", + " 'Volume-averaged ambient temperature [K]',\n", " 'Cell temperature [K]',\n", " 'Negative current collector temperature [K]',\n", " 'Positive current collector temperature [K]',\n", @@ -249,6 +275,7 @@ " 'Positive electrode temperature [K]',\n", " 'X-averaged positive electrode temperature [K]',\n", " 'Ambient temperature [C]',\n", + " 'Volume-averaged ambient temperature [C]',\n", " 'Cell temperature [C]',\n", " 'Negative current collector temperature [C]',\n", " 'Positive current collector temperature [C]',\n", @@ -261,49 +288,92 @@ " 'Positive electrode temperature [C]',\n", " 'X-averaged positive electrode temperature [C]',\n", " 'Negative current collector potential [V]',\n", - " 'Inner SEI thickness [m]',\n", - " 'Outer SEI thickness [m]',\n", - " 'X-averaged inner SEI thickness [m]',\n", - " 'X-averaged outer SEI thickness [m]',\n", - " 'SEI [m]',\n", - " 'Total SEI thickness [m]',\n", - " 'X-averaged SEI thickness [m]',\n", - " 'X-averaged total SEI thickness [m]',\n", + " 'Negative inner SEI thickness [m]',\n", + " 'Negative outer SEI thickness [m]',\n", + " 'X-averaged negative inner SEI thickness [m]',\n", + " 'X-averaged negative outer SEI thickness [m]',\n", + " 'Negative SEI [m]',\n", + " 'Negative total SEI thickness [m]',\n", + " 'X-averaged negative SEI thickness [m]',\n", + " 'X-averaged negative total SEI thickness [m]',\n", " 'X-averaged negative electrode resistance [Ohm.m2]',\n", - " 'Inner SEI interfacial current density [A.m-2]',\n", - " 'X-averaged inner SEI interfacial current density [A.m-2]',\n", - " 'Outer SEI interfacial current density [A.m-2]',\n", - " 'X-averaged outer SEI interfacial current density [A.m-2]',\n", - " 'SEI interfacial current density [A.m-2]',\n", - " 'X-averaged SEI interfacial current density [A.m-2]',\n", - " 'Inner SEI on cracks thickness [m]',\n", - " 'Outer SEI on cracks thickness [m]',\n", - " 'X-averaged inner SEI on cracks thickness [m]',\n", - " 'X-averaged outer SEI on cracks thickness [m]',\n", - " 'SEI on cracks [m]',\n", - " 'Total SEI on cracks thickness [m]',\n", - " 'X-averaged SEI on cracks thickness [m]',\n", - " 'X-averaged total SEI on cracks thickness [m]',\n", - " 'Inner SEI on cracks interfacial current density [A.m-2]',\n", - " 'X-averaged inner SEI on cracks interfacial current density [A.m-2]',\n", - " 'Outer SEI on cracks interfacial current density [A.m-2]',\n", - " 'X-averaged outer SEI on cracks interfacial current density [A.m-2]',\n", - " 'SEI on cracks interfacial current density [A.m-2]',\n", - " 'X-averaged SEI on cracks interfacial current density [A.m-2]',\n", - " 'Lithium plating concentration [mol.m-3]',\n", - " 'X-averaged lithium plating concentration [mol.m-3]',\n", - " 'Dead lithium concentration [mol.m-3]',\n", - " 'X-averaged dead lithium concentration [mol.m-3]',\n", - " 'Lithium plating thickness [m]',\n", - " 'X-averaged lithium plating thickness [m]',\n", - " 'Dead lithium thickness [m]',\n", - " 'X-averaged dead lithium thickness [m]',\n", - " 'Loss of lithium to lithium plating [mol]',\n", - " 'Loss of capacity to lithium plating [A.h]',\n", + " 'Negative electrode inner SEI interfacial current density [A.m-2]',\n", + " 'X-averaged negative electrode inner SEI interfacial current density [A.m-2]',\n", + " 'Negative electrode outer SEI interfacial current density [A.m-2]',\n", + " 'X-averaged negative electrode outer SEI interfacial current density [A.m-2]',\n", + " 'Negative electrode SEI interfacial current density [A.m-2]',\n", + " 'X-averaged negative electrode SEI interfacial current density [A.m-2]',\n", + " 'Positive inner SEI thickness [m]',\n", + " 'Positive outer SEI thickness [m]',\n", + " 'X-averaged positive inner SEI thickness [m]',\n", + " 'X-averaged positive outer SEI thickness [m]',\n", + " 'Positive SEI [m]',\n", + " 'Positive total SEI thickness [m]',\n", + " 'X-averaged positive SEI thickness [m]',\n", + " 'X-averaged positive total SEI thickness [m]',\n", + " 'X-averaged positive electrode resistance [Ohm.m2]',\n", + " 'Positive electrode inner SEI interfacial current density [A.m-2]',\n", + " 'X-averaged positive electrode inner SEI interfacial current density [A.m-2]',\n", + " 'Positive electrode outer SEI interfacial current density [A.m-2]',\n", + " 'X-averaged positive electrode outer SEI interfacial current density [A.m-2]',\n", + " 'Positive electrode SEI interfacial current density [A.m-2]',\n", + " 'X-averaged positive electrode SEI interfacial current density [A.m-2]',\n", + " 'Negative inner SEI on cracks thickness [m]',\n", + " 'Negative outer SEI on cracks thickness [m]',\n", + " 'X-averaged negative inner SEI on cracks thickness [m]',\n", + " 'X-averaged negative outer SEI on cracks thickness [m]',\n", + " 'Negative SEI on cracks [m]',\n", + " 'Negative total SEI on cracks thickness [m]',\n", + " 'X-averaged negative SEI on cracks thickness [m]',\n", + " 'X-averaged negative total SEI on cracks thickness [m]',\n", + " 'Negative electrode inner SEI on cracks interfacial current density [A.m-2]',\n", + " 'X-averaged negative electrode inner SEI on cracks interfacial current density [A.m-2]',\n", + " 'Negative electrode outer SEI on cracks interfacial current density [A.m-2]',\n", + " 'X-averaged negative electrode outer SEI on cracks interfacial current density [A.m-2]',\n", + " 'Negative electrode SEI on cracks interfacial current density [A.m-2]',\n", + " 'X-averaged negative electrode SEI on cracks interfacial current density [A.m-2]',\n", + " 'Positive inner SEI on cracks thickness [m]',\n", + " 'Positive outer SEI on cracks thickness [m]',\n", + " 'X-averaged positive inner SEI on cracks thickness [m]',\n", + " 'X-averaged positive outer SEI on cracks thickness [m]',\n", + " 'Positive SEI on cracks [m]',\n", + " 'Positive total SEI on cracks thickness [m]',\n", + " 'X-averaged positive SEI on cracks thickness [m]',\n", + " 'X-averaged positive total SEI on cracks thickness [m]',\n", + " 'Positive electrode inner SEI on cracks interfacial current density [A.m-2]',\n", + " 'X-averaged positive electrode inner SEI on cracks interfacial current density [A.m-2]',\n", + " 'Positive electrode outer SEI on cracks interfacial current density [A.m-2]',\n", + " 'X-averaged positive electrode outer SEI on cracks interfacial current density [A.m-2]',\n", + " 'Positive electrode SEI on cracks interfacial current density [A.m-2]',\n", + " 'X-averaged positive electrode SEI on cracks interfacial current density [A.m-2]',\n", + " 'Negative lithium plating concentration [mol.m-3]',\n", + " 'X-averaged negative lithium plating concentration [mol.m-3]',\n", + " 'Negative dead lithium concentration [mol.m-3]',\n", + " 'X-averaged negative dead lithium concentration [mol.m-3]',\n", + " 'Negative lithium plating thickness [m]',\n", + " 'X-averaged negative lithium plating thickness [m]',\n", + " 'Negative dead lithium thickness [m]',\n", + " 'X-averaged negative dead lithium thickness [m]',\n", + " 'Loss of lithium to negative lithium plating [mol]',\n", + " 'Loss of capacity to negative lithium plating [A.h]',\n", " 'Negative electrode lithium plating reaction overpotential [V]',\n", " 'X-averaged negative electrode lithium plating reaction overpotential [V]',\n", - " 'Lithium plating interfacial current density [A.m-2]',\n", - " 'X-averaged lithium plating interfacial current density [A.m-2]',\n", + " 'Negative lithium plating interfacial current density [A.m-2]',\n", + " 'X-averaged negative lithium plating interfacial current density [A.m-2]',\n", + " 'Positive lithium plating concentration [mol.m-3]',\n", + " 'X-averaged positive lithium plating concentration [mol.m-3]',\n", + " 'Positive dead lithium concentration [mol.m-3]',\n", + " 'X-averaged positive dead lithium concentration [mol.m-3]',\n", + " 'Positive lithium plating thickness [m]',\n", + " 'X-averaged positive lithium plating thickness [m]',\n", + " 'Positive dead lithium thickness [m]',\n", + " 'X-averaged positive dead lithium thickness [m]',\n", + " 'Loss of lithium to positive lithium plating [mol]',\n", + " 'Loss of capacity to positive lithium plating [A.h]',\n", + " 'Positive electrode lithium plating reaction overpotential [V]',\n", + " 'X-averaged positive electrode lithium plating reaction overpotential [V]',\n", + " 'Positive lithium plating interfacial current density [A.m-2]',\n", + " 'X-averaged positive lithium plating interfacial current density [A.m-2]',\n", " 'Negative crack surface to volume ratio [m-1]',\n", " 'Negative electrode roughness ratio',\n", " 'X-averaged negative electrode roughness ratio',\n", @@ -331,14 +401,14 @@ " 'Volume-averaged acceleration [m.s-1]',\n", " 'X-averaged volume-averaged acceleration [m.s-1]',\n", " 'Pressure [Pa]',\n", - " 'Negative electrode open-circuit potential [V]',\n", - " 'X-averaged negative electrode open-circuit potential [V]',\n", - " 'Negative electrode entropic change [V.K-1]',\n", - " 'X-averaged negative electrode entropic change [V.K-1]',\n", - " 'Positive electrode open-circuit potential [V]',\n", - " 'X-averaged positive electrode open-circuit potential [V]',\n", - " 'Positive electrode entropic change [V.K-1]',\n", - " 'X-averaged positive electrode entropic change [V.K-1]',\n", + " 'Negative electrode stoichiometry',\n", + " 'Negative electrode volume-averaged concentration',\n", + " 'Negative electrode volume-averaged concentration [mol.m-3]',\n", + " 'Total lithium in primary phase in negative electrode [mol]',\n", + " 'Positive electrode stoichiometry',\n", + " 'Positive electrode volume-averaged concentration',\n", + " 'Positive electrode volume-averaged concentration [mol.m-3]',\n", + " 'Total lithium in primary phase in positive electrode [mol]',\n", " 'Negative electrode effective conductivity',\n", " 'Negative electrode current density [A.m-2]',\n", " 'Positive electrode effective conductivity',\n", @@ -346,6 +416,7 @@ " 'Electrode current density [A.m-2]',\n", " 'Positive current collector potential [V]',\n", " 'Local voltage [V]',\n", + " 'Terminal voltage [V]',\n", " 'Voltage [V]',\n", " 'Contact overpotential [V]',\n", " 'Electrolyte concentration concatenation [mol.m-3]',\n", @@ -355,30 +426,24 @@ " 'X-averaged separator electrolyte concentration [mol.m-3]',\n", " 'Positive electrolyte concentration [mol.m-3]',\n", " 'X-averaged positive electrolyte concentration [mol.m-3]',\n", - " 'Negative electrolyte concentration',\n", " 'Negative electrolyte concentration [Molar]',\n", - " 'X-averaged negative electrolyte concentration',\n", " 'X-averaged negative electrolyte concentration [Molar]',\n", - " 'Separator electrolyte concentration',\n", " 'Separator electrolyte concentration [Molar]',\n", - " 'X-averaged separator electrolyte concentration',\n", " 'X-averaged separator electrolyte concentration [Molar]',\n", - " 'Positive electrolyte concentration',\n", " 'Positive electrolyte concentration [Molar]',\n", - " 'X-averaged positive electrolyte concentration',\n", " 'X-averaged positive electrolyte concentration [Molar]',\n", " 'Electrolyte concentration [mol.m-3]',\n", " 'X-averaged electrolyte concentration [mol.m-3]',\n", - " 'Electrolyte concentration',\n", " 'Electrolyte concentration [Molar]',\n", - " 'X-averaged electrolyte concentration',\n", " 'X-averaged electrolyte concentration [Molar]',\n", " 'Electrolyte current density [A.m-2]',\n", " 'X-averaged concentration overpotential [V]',\n", " 'X-averaged electrolyte ohmic losses [V]',\n", " 'Negative electrode surface potential difference [V]',\n", + " 'Negative electrode surface potential difference at separator interface [V]',\n", " 'X-averaged negative electrode surface potential difference [V]',\n", " 'Positive electrode surface potential difference [V]',\n", + " 'Positive electrode surface potential difference at separator interface [V]',\n", " 'X-averaged positive electrode surface potential difference [V]',\n", " 'Ohmic heating [W.m-3]',\n", " 'X-averaged Ohmic heating [W.m-3]',\n", @@ -393,46 +458,70 @@ " 'X-averaged total heating [W.m-3]',\n", " 'Volume-averaged total heating [W.m-3]',\n", " 'Current collector current density [A.m-2]',\n", - " 'Inner SEI concentration [mol.m-3]',\n", - " 'X-averaged inner SEI concentration [mol.m-3]',\n", - " 'Outer SEI concentration [mol.m-3]',\n", - " 'X-averaged outer SEI concentration [mol.m-3]',\n", - " 'SEI concentration [mol.m-3]',\n", - " 'X-averaged SEI concentration [mol.m-3]',\n", - " 'Loss of lithium to SEI [mol]',\n", - " 'Loss of capacity to SEI [A.h]',\n", - " 'X-averaged negative electrode SEI interfacial current density [A.m-2]',\n", - " 'Negative electrode SEI interfacial current density [A.m-2]',\n", - " 'Positive electrode SEI interfacial current density [A.m-2]',\n", - " 'X-averaged positive electrode SEI volumetric interfacial current density [A.m-2]',\n", - " 'Positive electrode SEI volumetric interfacial current density [A.m-3]',\n", + " 'Negative inner SEI concentration [mol.m-3]',\n", + " 'X-averaged negative inner SEI concentration [mol.m-3]',\n", + " 'Negative outer SEI concentration [mol.m-3]',\n", + " 'X-averaged negative outer SEI concentration [mol.m-3]',\n", + " 'Negative SEI concentration [mol.m-3]',\n", + " 'X-averaged negative SEI concentration [mol.m-3]',\n", + " 'Loss of lithium to negative SEI [mol]',\n", + " 'Loss of capacity to negative SEI [A.h]',\n", " 'Negative electrode SEI volumetric interfacial current density [A.m-3]',\n", " 'X-averaged negative electrode SEI volumetric interfacial current density [A.m-3]',\n", - " 'Inner SEI on cracks concentration [mol.m-3]',\n", - " 'X-averaged inner SEI on cracks concentration [mol.m-3]',\n", - " 'Outer SEI on cracks concentration [mol.m-3]',\n", - " 'X-averaged outer SEI on cracks concentration [mol.m-3]',\n", - " 'SEI on cracks concentration [mol.m-3]',\n", - " 'X-averaged SEI on cracks concentration [mol.m-3]',\n", - " 'Loss of lithium to SEI on cracks [mol]',\n", - " 'Loss of capacity to SEI on cracks [A.h]',\n", - " 'X-averaged negative electrode SEI on cracks interfacial current density [A.m-2]',\n", - " 'Negative electrode SEI on cracks interfacial current density [A.m-2]',\n", - " 'Positive electrode SEI on cracks interfacial current density [A.m-2]',\n", - " 'X-averaged positive electrode SEI on cracks volumetric interfacial current density [A.m-2]',\n", - " 'Positive electrode SEI on cracks volumetric interfacial current density [A.m-3]',\n", + " 'Positive inner SEI concentration [mol.m-3]',\n", + " 'X-averaged positive inner SEI concentration [mol.m-3]',\n", + " 'Positive outer SEI concentration [mol.m-3]',\n", + " 'X-averaged positive outer SEI concentration [mol.m-3]',\n", + " 'Positive SEI concentration [mol.m-3]',\n", + " 'X-averaged positive SEI concentration [mol.m-3]',\n", + " 'Loss of lithium to positive SEI [mol]',\n", + " 'Loss of capacity to positive SEI [A.h]',\n", + " 'Positive electrode SEI volumetric interfacial current density [A.m-3]',\n", + " 'X-averaged positive electrode SEI volumetric interfacial current density [A.m-3]',\n", + " 'Negative inner SEI on cracks concentration [mol.m-3]',\n", + " 'X-averaged negative inner SEI on cracks concentration [mol.m-3]',\n", + " 'Negative outer SEI on cracks concentration [mol.m-3]',\n", + " 'X-averaged negative outer SEI on cracks concentration [mol.m-3]',\n", + " 'Negative SEI on cracks concentration [mol.m-3]',\n", + " 'X-averaged negative SEI on cracks concentration [mol.m-3]',\n", + " 'Loss of lithium to negative SEI on cracks [mol]',\n", + " 'Loss of capacity to negative SEI on cracks [A.h]',\n", " 'Negative electrode SEI on cracks volumetric interfacial current density [A.m-3]',\n", " 'X-averaged negative electrode SEI on cracks volumetric interfacial current density [A.m-3]',\n", + " 'Positive inner SEI on cracks concentration [mol.m-3]',\n", + " 'X-averaged positive inner SEI on cracks concentration [mol.m-3]',\n", + " 'Positive outer SEI on cracks concentration [mol.m-3]',\n", + " 'X-averaged positive outer SEI on cracks concentration [mol.m-3]',\n", + " 'Positive SEI on cracks concentration [mol.m-3]',\n", + " 'X-averaged positive SEI on cracks concentration [mol.m-3]',\n", + " 'Loss of lithium to positive SEI on cracks [mol]',\n", + " 'Loss of capacity to positive SEI on cracks [A.h]',\n", + " 'Positive electrode SEI on cracks volumetric interfacial current density [A.m-3]',\n", + " 'X-averaged positive electrode SEI on cracks volumetric interfacial current density [A.m-3]',\n", " 'Negative electrode lithium plating interfacial current density [A.m-2]',\n", " 'X-averaged negative electrode lithium plating interfacial current density [A.m-2]',\n", - " 'Lithium plating volumetric interfacial current density [A.m-3]',\n", - " 'X-averaged lithium plating volumetric interfacial current density [A.m-3]',\n", - " 'X-averaged positive electrode lithium plating interfacial current density [A.m-2]',\n", - " 'X-averaged positive electrode lithium plating volumetric interfacial current density [A.m-3]',\n", - " 'Positive electrode lithium plating interfacial current density [A.m-2]',\n", - " 'Positive electrode lithium plating volumetric interfacial current density [A.m-3]',\n", + " 'Negative lithium plating volumetric interfacial current density [A.m-3]',\n", + " 'X-averaged negative lithium plating volumetric interfacial current density [A.m-3]',\n", " 'Negative electrode lithium plating volumetric interfacial current density [A.m-3]',\n", " 'X-averaged negative electrode lithium plating volumetric interfacial current density [A.m-3]',\n", + " 'Positive electrode lithium plating interfacial current density [A.m-2]',\n", + " 'X-averaged positive electrode lithium plating interfacial current density [A.m-2]',\n", + " 'Positive lithium plating volumetric interfacial current density [A.m-3]',\n", + " 'X-averaged positive lithium plating volumetric interfacial current density [A.m-3]',\n", + " 'Positive electrode lithium plating volumetric interfacial current density [A.m-3]',\n", + " 'X-averaged positive electrode lithium plating volumetric interfacial current density [A.m-3]',\n", + " 'Negative electrode open-circuit potential [V]',\n", + " 'X-averaged negative electrode open-circuit potential [V]',\n", + " 'Negative electrode bulk open-circuit potential [V]',\n", + " 'Negative particle concentration overpotential [V]',\n", + " 'Negative electrode entropic change [V.K-1]',\n", + " 'X-averaged negative electrode entropic change [V.K-1]',\n", + " 'Positive electrode open-circuit potential [V]',\n", + " 'X-averaged positive electrode open-circuit potential [V]',\n", + " 'Positive electrode bulk open-circuit potential [V]',\n", + " 'Positive particle concentration overpotential [V]',\n", + " 'Positive electrode entropic change [V.K-1]',\n", + " 'X-averaged positive electrode entropic change [V.K-1]',\n", " 'Negative electrode interfacial current density [A.m-2]',\n", " 'X-averaged negative electrode interfacial current density [A.m-2]',\n", " 'X-averaged negative electrode total interfacial current density [A.m-2]',\n", @@ -443,8 +532,8 @@ " 'X-averaged negative electrode reaction overpotential [V]',\n", " 'Negative electrode volumetric interfacial current density [A.m-3]',\n", " 'X-averaged negative electrode volumetric interfacial current density [A.m-3]',\n", - " 'SEI film overpotential [V]',\n", - " 'X-averaged SEI film overpotential [V]',\n", + " 'Negative electrode SEI film overpotential [V]',\n", + " 'X-averaged negative electrode SEI film overpotential [V]',\n", " 'Positive electrode interfacial current density [A.m-2]',\n", " 'X-averaged positive electrode interfacial current density [A.m-2]',\n", " 'X-averaged positive electrode total interfacial current density [A.m-2]',\n", @@ -455,24 +544,20 @@ " 'X-averaged positive electrode reaction overpotential [V]',\n", " 'Positive electrode volumetric interfacial current density [A.m-3]',\n", " 'X-averaged positive electrode volumetric interfacial current density [A.m-3]',\n", + " 'Positive electrode SEI film overpotential [V]',\n", + " 'X-averaged positive electrode SEI film overpotential [V]',\n", " 'Negative particle rhs [mol.m-3.s-1]',\n", - " 'Negative particle bc [mol.m-2]',\n", + " 'Negative particle bc [mol.m-4]',\n", " 'Negative particle effective diffusivity [m2.s-1]',\n", " 'X-averaged negative particle effective diffusivity [m2.s-1]',\n", + " 'Volume-averaged negative particle effective diffusivity [m2.s-1]',\n", " 'Negative particle flux [mol.m-2.s-1]',\n", - " 'Negative electrode stoichiometry',\n", - " 'Negative electrode volume-averaged concentration',\n", - " 'Negative electrode volume-averaged concentration [mol.m-3]',\n", - " 'Total lithium in primary phase in negative electrode [mol]',\n", " 'Positive particle rhs [mol.m-3.s-1]',\n", - " 'Positive particle bc [mol.m-2]',\n", + " 'Positive particle bc [mol.m-4]',\n", " 'Positive particle effective diffusivity [m2.s-1]',\n", " 'X-averaged positive particle effective diffusivity [m2.s-1]',\n", + " 'Volume-averaged positive particle effective diffusivity [m2.s-1]',\n", " 'Positive particle flux [mol.m-2.s-1]',\n", - " 'Positive electrode stoichiometry',\n", - " 'Positive electrode volume-averaged concentration',\n", - " 'Positive electrode volume-averaged concentration [mol.m-3]',\n", - " 'Total lithium in primary phase in positive electrode [mol]',\n", " 'Electrolyte flux [mol.m-2.s-1]',\n", " 'Electrolyte diffusion flux [mol.m-2.s-1]',\n", " 'Electrolyte migration flux [mol.m-2.s-1]',\n", @@ -489,18 +574,27 @@ " 'Exchange current density [A.m-2]',\n", " 'Sum of volumetric interfacial current densities [A.m-3]',\n", " 'Sum of electrolyte reaction source terms [A.m-3]',\n", - " 'X-averaged open-circuit voltage [V]',\n", - " 'Measured open-circuit voltage [V]',\n", + " 'Surface open-circuit voltage [V]',\n", + " 'Bulk open-circuit voltage [V]',\n", + " 'Particle concentration overpotential [V]',\n", " 'X-averaged reaction overpotential [V]',\n", + " 'X-averaged SEI film overpotential [V]',\n", " 'X-averaged solid phase ohmic losses [V]',\n", - " 'X-averaged battery open-circuit voltage [V]',\n", - " 'Measured battery open-circuit voltage [V]',\n", + " 'Battery open-circuit voltage [V]',\n", + " 'Battery negative electrode bulk open-circuit potential [V]',\n", + " 'Battery positive electrode bulk open-circuit potential [V]',\n", + " 'Battery particle concentration overpotential [V]',\n", + " 'Battery negative particle concentration overpotential [V]',\n", + " 'Battery positive particle concentration overpotential [V]',\n", " 'X-averaged battery reaction overpotential [V]',\n", + " 'X-averaged battery negative reaction overpotential [V]',\n", + " 'X-averaged battery positive reaction overpotential [V]',\n", " 'X-averaged battery solid phase ohmic losses [V]',\n", + " 'X-averaged battery negative solid phase ohmic losses [V]',\n", + " 'X-averaged battery positive solid phase ohmic losses [V]',\n", " 'X-averaged battery electrolyte ohmic losses [V]',\n", " 'X-averaged battery concentration overpotential [V]',\n", " 'Battery voltage [V]',\n", - " 'Change in measured open-circuit voltage [V]',\n", " 'Local ECM resistance [Ohm]',\n", " 'Terminal power [W]',\n", " 'Power [W]',\n", @@ -531,7 +625,7 @@ } ], "source": [ - "model_dfn.variable_names()" + "model.variable_names()" ] }, { @@ -539,7 +633,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "There are a _lot_ of variables. You can also search the list of variables for a particular string (e.g. \"electrolyte\")" + "There are a _lot_ of variables, however. You can also search the list of variables for a particular string (e.g. \"electrolyte\"), by calling:" ] }, { @@ -551,7 +645,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "Electrolyte concentration\n", "Electrolyte concentration [Molar]\n", "Electrolyte concentration [mol.m-3]\n", "Electrolyte concentration concatenation [mol.m-3]\n", @@ -567,17 +660,14 @@ "Gradient of positive electrolyte potential [V.m-1]\n", "Gradient of separator electrolyte potential [V.m-1]\n", "Loss of lithium inventory, including electrolyte [%]\n", - "Negative electrolyte concentration\n", "Negative electrolyte concentration [Molar]\n", "Negative electrolyte concentration [mol.m-3]\n", "Negative electrolyte potential [V]\n", "Negative electrolyte transport efficiency\n", - "Positive electrolyte concentration\n", "Positive electrolyte concentration [Molar]\n", "Positive electrolyte concentration [mol.m-3]\n", "Positive electrolyte potential [V]\n", "Positive electrolyte transport efficiency\n", - "Separator electrolyte concentration\n", "Separator electrolyte concentration [Molar]\n", "Separator electrolyte concentration [mol.m-3]\n", "Separator electrolyte potential [V]\n", @@ -590,23 +680,19 @@ "Total lithium in electrolyte [mol]\n", "Total lithium lost from electrolyte [mol]\n", "X-averaged battery electrolyte ohmic losses [V]\n", - "X-averaged electrolyte concentration\n", "X-averaged electrolyte concentration [Molar]\n", "X-averaged electrolyte concentration [mol.m-3]\n", "X-averaged electrolyte ohmic losses [V]\n", "X-averaged electrolyte overpotential [V]\n", "X-averaged electrolyte potential [V]\n", - "X-averaged negative electrolyte concentration\n", "X-averaged negative electrolyte concentration [Molar]\n", "X-averaged negative electrolyte concentration [mol.m-3]\n", "X-averaged negative electrolyte potential [V]\n", "X-averaged negative electrolyte transport efficiency\n", - "X-averaged positive electrolyte concentration\n", "X-averaged positive electrolyte concentration [Molar]\n", "X-averaged positive electrolyte concentration [mol.m-3]\n", "X-averaged positive electrolyte potential [V]\n", "X-averaged positive electrolyte transport efficiency\n", - "X-averaged separator electrolyte concentration\n", "X-averaged separator electrolyte concentration [Molar]\n", "X-averaged separator electrolyte concentration [mol.m-3]\n", "X-averaged separator electrolyte potential [V]\n", @@ -615,7 +701,7 @@ } ], "source": [ - "model_dfn.variables.search(\"electrolyte\")" + "model.variables.search(\"electrolyte\")" ] }, { @@ -623,15 +709,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We have tried to make variables names fairly self explanatory." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As a first example, we choose to plot the voltage. We add this to a list and then pass this list to the `plot` method of our simulation:" + "As a first example, we choose to plot only electrolyte concentration and voltage. We assemble a list of the variable names to plot and then pass this list to the `plot` method of our simulation as the `output_variables` keyword argument:" ] }, { @@ -642,7 +720,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8c87342bdc1e425ba87715d286d799d0", + "model_id": "6c80d973ac814f59b28580292f1b7bb2", "version_major": 2, "version_minor": 0 }, @@ -656,7 +734,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -665,8 +743,8 @@ } ], "source": [ - "output_variables = [\"Voltage [V]\"]\n", - "sim_dfn.plot(output_variables=output_variables)" + "output_variables = [\"Electrolyte concentration [mol.m-3]\", \"Voltage [V]\"]\n", + "sim.plot(output_variables=output_variables)" ] }, { @@ -674,7 +752,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Alternatively, we may be interested in plotting both the electrolyte concentration and the voltage. In which case, we would do:" + "Note that, if we want to plot only a single variable, we still need to pass it as a list:" ] }, { @@ -685,7 +763,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a6cc55c5b66b4ce78ca2cae475435df1", + "model_id": "830e2e38bc5949b399c23d123437a77e", "version_major": 2, "version_minor": 0 }, @@ -699,7 +777,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -708,8 +786,8 @@ } ], "source": [ - "output_variables = [\"Electrolyte concentration [mol.m-3]\", \"Voltage [V]\"]\n", - "sim_dfn.plot(output_variables=output_variables)" + "output_variables = [\"Voltage [V]\"]\n", + "sim.plot(output_variables=output_variables)" ] }, { @@ -717,7 +795,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You can also plot multiple variables on the same plot by nesting lists" + "You can also plot multiple variables on the same plot by nesting lists. For example, if we want to plot electrode and electrolyte current densities in the same plot and voltage next to them, we can write:" ] }, { @@ -728,7 +806,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "974616973e534d219b0d196b893f522b", + "model_id": "75b63940c93d4aa6b0386a77ad05f52a", "version_major": 2, "version_minor": 0 }, @@ -742,7 +820,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -751,7 +829,7 @@ } ], "source": [ - "sim_dfn.plot(\n", + "sim.plot(\n", " [\n", " [\"Electrode current density [A.m-2]\", \"Electrolyte current density [A.m-2]\"],\n", " \"Voltage [V]\",\n", @@ -759,6 +837,14 @@ ")" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PyBaMM also allows you to produce a voltage plot showing the contribution of the various overpotentials and losses by calling the `plot_votage_components` method and passing the `solution` object of the simulation as argument:" + ] + }, { "cell_type": "code", "execution_count": 7, @@ -766,13 +852,9 @@ "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "467c303add6f439fa35d549653026823", - "version_major": 2, - "version_minor": 0 - }, + "image/png": 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", "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…" + "
" ] }, "metadata": {}, @@ -781,7 +863,7 @@ { "data": { "text/plain": [ - "" + "(
, )" ] }, "execution_count": 7, @@ -790,15 +872,14 @@ } ], "source": [ - "sim_dfn.plot()" + "sim.plot_voltage_components()" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "For plotting the voltage components you can use the `plot_votage_components` function" + "The contributions can also be split by electrode by setting the keyword argument `split_by_electrode` to `True`:" ] }, { @@ -808,9 +889,9 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -819,7 +900,7 @@ { "data": { "text/plain": [ - "(
, )" + "(
, )" ] }, "execution_count": 8, @@ -828,49 +909,7 @@ } ], "source": [ - "pybamm.plot_voltage_components(sim_dfn.solution)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And with a few modifications (by creating subplots and by providing the axes on which the voltage components have to be plotted), it can also be used to compare the voltage components of different simulations" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# simulating and solving Single Particle Model\n", - "model_spm = pybamm.lithium_ion.SPM()\n", - "sim_spm = pybamm.Simulation(model_spm)\n", - "sim_spm.solve([0, 3700])\n", - "\n", - "# comparing voltage components for Doyle-Fuller-Newman model and Single Particle Model\n", - "fig, axes = plt.subplots(1, 2, figsize=(15, 6), sharey=True)\n", - "\n", - "pybamm.plot_voltage_components(sim_dfn.solution, ax=axes.flat[0])\n", - "pybamm.plot_voltage_components(sim_spm.solution, ax=axes.flat[1])\n", - "\n", - "axes.flat[0].set_title(\"Doyle-Fuller-Newman Model\")\n", - "axes.flat[1].set_title(\"Single Particle Model\")\n", - "\n", - "plt.show()" + "sim.plot_voltage_components(split_by_electrode=True)" ] }, { @@ -878,9 +917,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this tutorial we have seen how to use the plotting functionality in PyBaMM.\n", - "\n", - "In [Tutorial 4](./tutorial-4-setting-parameter-values.ipynb) we show how to change parameter values." + "In this tutorial we have seen how to use the in-built plotting functionality in PyBaMM. In [Tutorial 4](./tutorial-4-setting-parameter-values.ipynb) we show how to change parameter values." ] }, { @@ -895,20 +932,18 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n", - "[2] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", - "[3] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n", - "[4] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", - "[5] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", - "[6] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", - "[7] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", + "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", + "[2] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", + "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", + "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", + "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", "\n" ] } @@ -934,7 +969,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.11.6" }, "vscode": { "interpreter": { diff --git a/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb index 8ac3cf2eda..01ae1864b6 100644 --- a/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb +++ b/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb @@ -25,18 +25,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.3.1 is available.\n", - "You should consider upgrading via the '/home/siegeljb/Documents/PyBaMM_Master/PyBaMM/.tox/dev/bin/python3.9 -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n", - "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n" + "\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.3.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.0\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" ] } ], "source": [ "%pip install \"pybamm[plot,cite]\" -q # install PyBaMM if it is not installed\n", - "import pybamm\n", - "import os\n", - "\n", - "os.chdir(pybamm.__path__[0] + \"/..\")" + "import pybamm" ] }, { @@ -69,7 +74,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can see all the parameters stored in the dictionary" + "The parameter values are stored in a dictionary" ] }, { @@ -80,20 +85,24 @@ { "data": { "text/plain": [ - "{'Thermodynamic factor': 1.0,\n", - " 'Ambient temperature [K]': 298.15,\n", + "{'Ambient temperature [K]': 298.15,\n", + " 'Boltzmann constant [J.K-1]': 1.380649e-23,\n", " 'Bulk solvent concentration [mol.m-3]': 2636.0,\n", " 'Cation transference number': 0.2594,\n", " 'Cell cooling surface area [m2]': 0.00531,\n", " 'Cell thermal expansion coefficient [m.K-1]': 1.1e-06,\n", " 'Cell volume [m3]': 2.42e-05,\n", + " 'Contact resistance [Ohm]': 0,\n", " 'Current function [A]': 5.0,\n", " 'EC diffusivity [m2.s-1]': 2e-18,\n", " 'EC initial concentration in electrolyte [mol.m-3]': 4541.0,\n", " 'Electrode height [m]': 0.065,\n", " 'Electrode width [m]': 1.58,\n", - " 'Electrolyte conductivity [S.m-1]': ,\n", - " 'Electrolyte diffusivity [m2.s-1]': ,\n", + " 'Electrolyte conductivity [S.m-1]': ,\n", + " 'Electrolyte diffusivity [m2.s-1]': ,\n", + " 'Electron charge [C]': 1.602176634e-19,\n", + " 'Faraday constant [C.mol-1]': 96485.33212,\n", + " 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n", " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n", " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n", " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n", @@ -114,24 +123,22 @@ " 'Negative current collector specific heat capacity [J.kg-1.K-1]': 385.0,\n", " 'Negative current collector thermal conductivity [W.m-1.K-1]': 401.0,\n", " 'Negative current collector thickness [m]': 1.2e-05,\n", - " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n", + " 'Negative electrode Bruggeman coefficient (electrode)': 0,\n", " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Negative electrode OCP [V]': ,\n", + " 'Negative electrode OCP [V]': ,\n", " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n", " 'Negative electrode active material volume fraction': 0.75,\n", - " 'Negative electrode cation signed stoichiometry': -1.0,\n", " 'Negative electrode charge transfer coefficient': 0.5,\n", " 'Negative electrode conductivity [S.m-1]': 215.0,\n", " 'Negative electrode density [kg.m-3]': 1657.0,\n", - " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n", " 'Negative electrode double-layer capacity [F.m-2]': 0.2,\n", - " 'Negative electrode electrons in reaction': 1.0,\n", - " 'Negative electrode exchange-current density [A.m-2]': ,\n", + " 'Negative electrode exchange-current density [A.m-2]': ,\n", " 'Negative electrode porosity': 0.25,\n", " 'Negative electrode reaction-driven LAM factor [m3.mol-1]': 0.0,\n", " 'Negative electrode specific heat capacity [J.kg-1.K-1]': 700.0,\n", " 'Negative electrode thermal conductivity [W.m-1.K-1]': 1.7,\n", " 'Negative electrode thickness [m]': 8.52e-05,\n", + " 'Negative particle diffusivity [m2.s-1]': 3.3e-14,\n", " 'Negative particle radius [m]': 5.86e-06,\n", " 'Nominal cell capacity [A.h]': 5.0,\n", " 'Number of cells connected in series to make a battery': 1.0,\n", @@ -146,24 +153,22 @@ " 'Positive current collector specific heat capacity [J.kg-1.K-1]': 897.0,\n", " 'Positive current collector thermal conductivity [W.m-1.K-1]': 237.0,\n", " 'Positive current collector thickness [m]': 1.6e-05,\n", - " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n", + " 'Positive electrode Bruggeman coefficient (electrode)': 0,\n", " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Positive electrode OCP [V]': ,\n", + " 'Positive electrode OCP [V]': ,\n", " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n", " 'Positive electrode active material volume fraction': 0.665,\n", - " 'Positive electrode cation signed stoichiometry': -1.0,\n", " 'Positive electrode charge transfer coefficient': 0.5,\n", " 'Positive electrode conductivity [S.m-1]': 0.18,\n", " 'Positive electrode density [kg.m-3]': 3262.0,\n", - " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n", " 'Positive electrode double-layer capacity [F.m-2]': 0.2,\n", - " 'Positive electrode electrons in reaction': 1.0,\n", - " 'Positive electrode exchange-current density [A.m-2]': ,\n", + " 'Positive electrode exchange-current density [A.m-2]': ,\n", " 'Positive electrode porosity': 0.335,\n", " 'Positive electrode reaction-driven LAM factor [m3.mol-1]': 0.0,\n", " 'Positive electrode specific heat capacity [J.kg-1.K-1]': 700.0,\n", " 'Positive electrode thermal conductivity [W.m-1.K-1]': 2.1,\n", " 'Positive electrode thickness [m]': 7.56e-05,\n", + " 'Positive particle diffusivity [m2.s-1]': 4e-15,\n", " 'Positive particle radius [m]': 5.22e-06,\n", " 'Ratio of lithium moles to SEI moles': 2.0,\n", " 'Reference temperature [K]': 298.15,\n", @@ -178,9 +183,8 @@ " 'Separator specific heat capacity [J.kg-1.K-1]': 700.0,\n", " 'Separator thermal conductivity [W.m-1.K-1]': 0.16,\n", " 'Separator thickness [m]': 1.2e-05,\n", + " 'Thermodynamic factor': 1.0,\n", " 'Total heat transfer coefficient [W.m-2.K-1]': 10.0,\n", - " 'Typical current [A]': 5.0,\n", - " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n", " 'Upper voltage cut-off [V]': 4.2,\n", " 'citations': ['Chen2020']}" ] @@ -199,26 +203,52 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "or we can search for a particular parameter" + "and the specific values can be accessed using standard dictionary syntax:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.065" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parameter_values[\"Electrode height [m]\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also search parameter values with a given keyword using the `search` command:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "EC initial concentration in electrolyte [mol.m-3]\t4541.0\n", - "Electrolyte conductivity [S.m-1]\t\n", - "Electrolyte diffusivity [m2.s-1]\t\n", + "Electrolyte conductivity [S.m-1]\t\n", + "Electrolyte diffusivity [m2.s-1]\t\n", "Initial concentration in electrolyte [mol.m-3]\t1000.0\n", "Negative electrode Bruggeman coefficient (electrolyte)\t1.5\n", "Positive electrode Bruggeman coefficient (electrolyte)\t1.5\n", - "Separator Bruggeman coefficient (electrolyte)\t1.5\n", - "Typical electrolyte concentration [mol.m-3]\t1000.0\n" + "Separator Bruggeman coefficient (electrolyte)\t1.5\n" ] } ], @@ -231,12 +261,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To run a simulation with this parameter set, we can proceed as usual but passing the parameters as a keyword argument" + "To run a simulation with a given parameter set, we can proceed as usual but passing the parameters as the `parameter_values` keyword argument" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "scrolled": true }, @@ -244,12 +274,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e17d8edf481748daaef3f3b237bddd71", + "model_id": "2ac62159d85445f0b021b8800750726f", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=3554.184719744867, step=35.54184719744867), …" + "interactive(children=(FloatSlider(value=0.0, description='t', max=3555.448018330181, step=35.55448018330181), …" ] }, "metadata": {}, @@ -258,10 +288,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -278,54 +308,102 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "More details on each subset can be found [here](https://github.com/pybamm-team/PyBaMM/tree/develop/pybamm/input/parameters)." + "## Change individual parameters\n", + "\n", + "We often want to quickly change a small number of parameter values to investigate how the behaviour or the battery changes. In such cases, we can change parameter values directly in notebook or script we are working in as we demonstrate in this section.\n", + "\n", + "Parameters can either have a constant value or be a function of a model variable. These dependencies are hardcoded into the PyBaMM models. In order to check what are the specific parameters that a model requires, and whether they must be constants or they can be functions, we can call the `print_parameter_info` method." ] }, { - "attachments": {}, - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 7, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "| Parameter | Type of parameter |\n", + "| ========================================================= | =========================================================================================================================================================================================================== |\n", + "| Maximum concentration in positive electrode [mol.m-3] | Parameter |\n", + "| Maximum concentration in negative electrode [mol.m-3] | Parameter |\n", + "| Nominal cell capacity [A.h] | Parameter |\n", + "| Electrode width [m] | Parameter |\n", + "| Positive electrode Bruggeman coefficient (electrode) | Parameter |\n", + "| Faraday constant [C.mol-1] | Parameter |\n", + "| Number of electrodes connected in parallel to make a cell | Parameter |\n", + "| Negative electrode Bruggeman coefficient (electrode) | Parameter |\n", + "| Initial concentration in electrolyte [mol.m-3] | Parameter |\n", + "| Electrode height [m] | Parameter |\n", + "| Lower voltage cut-off [V] | Parameter |\n", + "| Upper voltage cut-off [V] | Parameter |\n", + "| Negative electrode Bruggeman coefficient (electrolyte) | Parameter |\n", + "| Separator Bruggeman coefficient (electrolyte) | Parameter |\n", + "| Number of cells connected in series to make a battery | Parameter |\n", + "| Ideal gas constant [J.K-1.mol-1] | Parameter |\n", + "| Positive electrode thickness [m] | Parameter |\n", + "| Reference temperature [K] | Parameter |\n", + "| Initial temperature [K] | Parameter |\n", + "| Positive electrode Bruggeman coefficient (electrolyte) | Parameter |\n", + "| Negative electrode thickness [m] | Parameter |\n", + "| Separator thickness [m] | Parameter |\n", + "| Electrolyte conductivity [S.m-1] | FunctionParameter with inputs(s) 'Electrolyte concentration [mol.m-3]', 'Temperature [K]' |\n", + "| Positive electrode OCP [V] | FunctionParameter with inputs(s) 'Positive particle stoichiometry' |\n", + "| Negative particle radius [m] | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]' |\n", + "| Positive electrode OCP entropic change [V.K-1] | FunctionParameter with inputs(s) 'Positive particle stoichiometry', 'Maximum positive particle surface concentration [mol.m-3]' |\n", + "| Negative electrode porosity | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]' |\n", + "| Positive particle radius [m] | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]' |\n", + "| Positive electrode active material volume fraction | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]' |\n", + "| Ambient temperature [K] | FunctionParameter with inputs(s) 'Distance across electrode width [m]', 'Distance across electrode height [m]', 'Time [s]' |\n", + "| Initial concentration in positive electrode [mol.m-3] | FunctionParameter with inputs(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]' |\n", + "| Cation transference number | FunctionParameter with inputs(s) 'Electrolyte concentration [mol.m-3]', 'Temperature [K]' |\n", + "| Negative electrode OCP [V] | FunctionParameter with inputs(s) 'Negative particle stoichiometry' |\n", + "| Negative particle diffusivity [m2.s-1] | FunctionParameter with inputs(s) 'Negative particle stoichiometry', 'Temperature [K]' |\n", + "| Thermodynamic factor | FunctionParameter with inputs(s) 'Electrolyte concentration [mol.m-3]', 'Temperature [K]' |\n", + "| Positive electrode exchange-current density [A.m-2] | FunctionParameter with inputs(s) 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Maximum positive particle surface concentration [mol.m-3]', 'Temperature [K]' |\n", + "| Negative electrode active material volume fraction | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]' |\n", + "| Positive particle diffusivity [m2.s-1] | FunctionParameter with inputs(s) 'Positive particle stoichiometry', 'Temperature [K]' |\n", + "| Positive electrode porosity | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]' |\n", + "| Positive electrode conductivity [S.m-1] | FunctionParameter with inputs(s) 'Temperature [K]' |\n", + "| Initial concentration in negative electrode [mol.m-3] | FunctionParameter with inputs(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]' |\n", + "| Negative electrode OCP entropic change [V.K-1] | FunctionParameter with inputs(s) 'Negative particle stoichiometry', 'Maximum negative particle surface concentration [mol.m-3]' |\n", + "| Current function [A] | FunctionParameter with inputs(s) 'Time [s]' |\n", + "| Electrolyte diffusivity [m2.s-1] | FunctionParameter with inputs(s) 'Electrolyte concentration [mol.m-3]', 'Temperature [K]' |\n", + "| Separator porosity | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]' |\n", + "| Negative electrode exchange-current density [A.m-2] | FunctionParameter with inputs(s) 'Electrolyte concentration [mol.m-3]', 'Negative particle surface concentration [mol.m-3]', 'Maximum negative particle surface concentration [mol.m-3]', 'Temperature [K]' |\n", + "| Negative electrode conductivity [S.m-1] | FunctionParameter with inputs(s) 'Temperature [K]' |\n" + ] + } + ], "source": [ - "## Change individual parameters" + "model.print_parameter_info()" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "We often want to quickly change a small number of parameter values to investigate how the behaviour or the battery changes. In such cases, we can change parameter values without having to leave the notebook or script you are working in. \n", + "The table shows all the parameters that need to be provided to the `model` (in this case DFN). For example, we see that we need to define (amongst others) the `Negative electrode thickness [m]`, which is defined to be a `Parameter`. This means it can only have a constant value. In contrast, other parameters are defined to be a `FunctionParameter`, which means that they can depend on model variables. For example, the `Current function [A]` is a `FunctionParameter` that can depend on `Time [s]`.\n", "\n", - "We start initialising the model and the parameter values" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "model = pybamm.lithium_ion.DFN()\n", - "parameter_values = pybamm.ParameterValues(\"Chen2020\")" + "Note that a `FunctionParameter` can always be defined to be a constant (i.e. like if it was a `Parameter`), but a `Parameter` cannot be defined to be a function. This is because these dependencies are hardcoded in the model definitions." ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "In this example we will change the current to 10 A" + "### Constant parameters\n", + "Let's start with a simple example: change the `Current function [A]` to be 10 A. To do this, we can simply update the value in the dictionary:" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ - "parameter_values[\"Current function [A]\"] = 10\n", - "parameter_values[\"Open-circuit voltage at 100% SOC [V]\"] = 3.4\n", - "parameter_values[\"Open-circuit voltage at 0% SOC [V]\"] = 3.0" + "parameter_values[\"Current function [A]\"] = 10" ] }, { @@ -333,23 +411,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "Note that, as we mentioned above, even though `Current function [A]` could depend on time, we can always define it to be a function. \n", + "\n", "Now we just need to run the simulation with the new parameter values" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "517cb023cdd54c27b20efccb17ce081c", + "model_id": "29a3805ee040456bbe863a52cc423492", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=1720.7505603255456, step=17.207505603255456)…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=1703.071841649571, step=17.03071841649571), …" ] }, "metadata": {}, @@ -358,17 +438,17 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sim = pybamm.Simulation(model, parameter_values=parameter_values)\n", - "sim.solve([0, 3600], initial_soc=1)\n", + "sim.solve([0, 3600])\n", "sim.plot()" ] }, @@ -377,7 +457,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note that we still passed the interval `[0, 3600]` to `sim.solve()`, but the simulation terminated early as the lower voltage cut-off was reached." + "and we observe in the plot that now the applied current is indeed 10 A. Note that we still passed the interval `[0, 3600]` to `sim.solve()`, but the simulation terminated early as the lower voltage cut-off was reached." ] }, { @@ -385,7 +465,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Drive cycle" + "### Function parameters" ] }, { @@ -393,27 +473,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You can implement drive cycles importing the dataset and creating an interpolant to pass as the current function." + "Let's now illustrate how to change the `Current function [A]` to be a given time-dependent function. In this case we will set the current to be sinusoidal. To do this, we need to define the relevant function and pass it as a parameter." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "import pandas as pd # needed to read the csv data file\n", + "import numpy as np\n", + "\n", "\n", - "# Import drive cycle from file\n", - "drive_cycle = pd.read_csv(\n", - " \"pybamm/input/drive_cycles/US06.csv\", comment=\"#\", header=None\n", - ").to_numpy()\n", + "def my_current(t):\n", + " return pybamm.sin(2 * np.pi * t / 60)\n", "\n", - "# Create interpolant\n", - "current_interpolant = pybamm.Interpolant(drive_cycle[:, 0], drive_cycle[:, 1], pybamm.t)\n", "\n", - "# Set drive cycle\n", - "parameter_values[\"Current function [A]\"] = current_interpolant" + "parameter_values[\"Current function [A]\"] = my_current" ] }, { @@ -421,23 +497,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note that your drive cycle data can be stored anywhere, you just need to pass the path of the file. Then, again, the model can be solved as usual but notice that now, if `t_eval` is not specified, the solver will take the time points from the data set." + "Note that the `my_current` function takes an argument `t`, which is time. PyBaMM will assume that the arguments are defined exactly as in the `print_parameter_info` table above, so the actual name given to the arguments does not matter, but the order does. For example, for the `Electrolyte conductivity [S.m-1]`, it will assume that the first argument is `Electrolyte concentration [mol.m-3]` and the second is `Temperature [K]`.\n", + "\n", + "We can now solve the model again. In this case, we will pass `t_eval` to be an array of the points we want to evaluate our solution to the solver (rather than the start and end points only) to make sure we have enough time points to resolve the sinusoidal function in our output." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "62d76f560ead4218ba0b3d4da49b7ba0", + "model_id": "f362d8ff79bc4b868f59470d58fdd9c6", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=97.84197033486475, step=0.9784197033486476),…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=120.0, step=1.2), Output()), _dom_classes=('…" ] }, "metadata": {}, @@ -446,121 +524,56 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "model = pybamm.lithium_ion.SPMe()\n", "sim = pybamm.Simulation(model, parameter_values=parameter_values)\n", - "sim.solve()\n", + "t_eval = np.arange(0, 121, 1)\n", + "sim.solve(t_eval=t_eval)\n", "sim.plot([\"Current [A]\", \"Voltage [V]\"])" ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Custom current function" - ] - }, - { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "Alternatively, we can define the current to be an arbitrary function of time" + "## Define a new parameter set\n", + "\n", + "We can also define a new parameter set from scratch, which is useful if there is a new battery or chemistry on which we need to run simulations repeatedly. To do so, we can initialise a `ParameterValues` object and pass as an argument a dictionary of parameter values:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", + "def cube(t):\n", + " return t**3\n", "\n", "\n", - "def my_current(t):\n", - " return pybamm.sin(2 * np.pi * t / 60)\n", - "\n", - "\n", - "parameter_values[\"Current function [A]\"] = my_current" + "parameter_values = pybamm.ParameterValues(\n", + " {\n", + " \"Negative electrode thickness [m]\": 1e-4,\n", + " \"Positive electrode thickness [m]\": 1.2e-4,\n", + " \"Current function [A]\": cube,\n", + " }\n", + ")" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "and we can now solve the model again. In this case, we can pass `t_eval` to the solver to make sure we have enough time points to resolve the function in our output." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1016bbebb3744513b4e92b0e53351deb", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=36.02442143490834, step=0.3602442143490834),…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "model = pybamm.lithium_ion.SPMe()\n", - "sim = pybamm.Simulation(model, parameter_values=parameter_values)\n", - "t_eval = np.arange(0, 121, 1)\n", - "sim.solve(t_eval=t_eval)\n", - "sim.plot([\"Current [A]\", \"Voltage [V]\"])" + "Note how, when we pass a function as a parameter, we pass the object without calling it, i.e. we pass `cube` rather than `cube(t)`. This new `parameter_values` variable could now be passed to a simulation, but note that it is incomplete as it does not include all the parameters that the model needs to run (see the parameters needed by calling `model.print_parameter_info()`, as done above). \n", + "\n", + "It is often convenient to define the parameter set in a separate file, and then call the parameters into your notebook or script. You can find some examples on how to do so in [PyBaMM's parameter library](https://github.com/pybamm-team/PyBaMM/tree/develop/pybamm/input/parameters/lithium_ion). You can copy one of the parameter sets available into a new file and modify it accordingly for the new parameter set. Then, whenever the set is needed, one can import the `get_parameter_values` method from the corresponding file and call it to obtain a copy of the parameter values." ] }, { @@ -583,21 +596,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n", - "[2] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", - "[3] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", - "[4] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n", - "[5] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", - "[6] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", - "[7] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", - "[8] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", + "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", + "[2] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", + "[3] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", + "[4] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", + "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", "\n" ] } @@ -623,7 +633,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12 (main, Apr 15 2022, 23:10:21) \n[GCC 11.2.0]" + "version": "3.11.6" }, "toc": { "base_numbering": 1, diff --git a/docs/source/examples/notebooks/getting_started/tutorial-5-run-experiments.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-5-run-experiments.ipynb index 9ec8d79cf1..85be34e421 100644 --- a/docs/source/examples/notebooks/getting_started/tutorial-5-run-experiments.ipynb +++ b/docs/source/examples/notebooks/getting_started/tutorial-5-run-experiments.ipynb @@ -25,9 +25,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.2\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } @@ -51,7 +48,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We start defining an experiment, which consists on a set of instructions on how to cycle the battery. For example, we can set the following experiment:" + "We start defining an experiment, which consists on a set of instructions on how to cycle the battery. These instructions can be defined in two different ways, but the simplest is to use strings. The instructions can be of the form `\"(Dis)charge at x A/C/W\"`, `\"Rest\"`, or `\"Hold at x V\"`. The instructions can also include how long each step should run for. The duration is introduced by the word `\"for\"` followed by the duration, e.g. `\"for 10 seconds\"`, `\"for 3 minutes\"` or `\"for 1 hour\"`. Terminating conditions can also be specified. In this case, the step will stop when that condition is met. These conditions should be a circuit state and are introduced by the word `\"until\"`, e.g. `\"until 1 A\"`, `\"until C/50\"` or `\"until 3 V\"`. Duration and termination conditions can be combined using the word `\"or\"` and the step will either finish when the condition is met or after the specified duration (whichever happens first).\n", + "\n", + "Some examples of experiment instructions are:\n", + "```python\n", + " \"Discharge at 1C for 0.5 hours\",\n", + " \"Discharge at C/20 for 0.5 hours\",\n", + " \"Charge at 0.5 C for 45 minutes\",\n", + " \"Discharge at 1 A for 90 seconds\",\n", + " \"Charge at 200mA for 45 minutes\",\n", + " \"Discharge at 1 W for 0.5 hours\",\n", + " \"Charge at 200 mW for 45 minutes\",\n", + " \"Rest for 10 minutes\",\n", + " \"Hold at 1 V for 20 seconds\",\n", + " \"Charge at 1 C until 4.1V\",\n", + " \"Hold at 4.1 V until 50 mA\",\n", + " \"Hold at 3V until C/50\",\n", + "```\n", + "\n", + "These steps can be concatenated in a list, so they are executed sequentially. To create an experiment, the list can then be passed when creating an `Experiment` object:" ] }, { @@ -62,15 +77,12 @@ "source": [ "experiment = pybamm.Experiment(\n", " [\n", - " (\n", - " \"Discharge at C/10 for 10 hours or until 3.3 V\",\n", - " \"Rest for 1 hour\",\n", - " \"Charge at 1 A until 4.1 V\",\n", - " \"Hold at 4.1 V until 50 mA\",\n", - " \"Rest for 1 hour\",\n", - " ),\n", + " \"Discharge at C/10 for 10 hours or until 3.3 V\",\n", + " \"Rest for 1 hour\",\n", + " \"Charge at 1 A until 4.1 V\",\n", + " \"Hold at 4.1 V until 50 mA\",\n", + " \"Rest for 1 hour\",\n", " ]\n", - " * 3\n", ")" ] }, @@ -79,9 +91,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "A cycle is defined by a tuple of operating instructions. In this case, the experiment consists of a cycle of constant current C/10 discharge, a one hour rest, a constant current (1 A) constant voltage (4.1 V) and another one hour rest, all of it repeated three times (notice the * 3).\n", - "\n", - "Then we can choose our model" + "In order to reproduce real cycling conditions, often the experiments will be composed of several \"cycles\", where a cycle is a user-defined collection of steps. In PyBaMM, we can define a cycle as a tuple of steps, which means that we can process the solution in terms of cycles. For more information on this functionality, please see the [long experiments notebook](../simulations_and_experiments/simulating-long-experiments.ipynb). We can also leverage the list addition and multiplication operators to combine and repeat cycles. For example, if we want a three cycles of constant current C/10 discharge, a one hour rest, a constant current (1 A) constant voltage (4.1 V) and another one hour rest, followed by a cycle of 1C discharge we can write:\n" ] }, { @@ -90,15 +100,30 @@ "metadata": {}, "outputs": [], "source": [ - "model = pybamm.lithium_ion.DFN()" + "experiment = pybamm.Experiment(\n", + " [\n", + " (\n", + " \"Discharge at C/10 for 10 hours or until 3.3 V\",\n", + " \"Rest for 1 hour\",\n", + " \"Charge at 1 A until 4.1 V\",\n", + " \"Hold at 4.1 V until 50 mA\",\n", + " \"Rest for 1 hour\",\n", + " )\n", + " ]\n", + " * 3\n", + " + [\n", + " \"Discharge at 1C until 3.3 V\",\n", + " ]\n", + ")" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "and create our simulation, passing our experiment using a keyword argument" + "Note that if a cycle is made of one step only (like the 1C discharge) we do not need to define it as a tuple. One key difference between cycles and steps, is that PyBaMM allows for steps to be skipped (e.g. if you try to charge an a fully charged battery) but not cycles.\n", + "\n", + "Then we can choose our model and create our simulation, passing our experiment using a keyword argument" ] }, { @@ -107,6 +132,7 @@ "metadata": {}, "outputs": [], "source": [ + "model = pybamm.lithium_ion.DFN()\n", "sim = pybamm.Simulation(model, experiment=experiment)" ] }, @@ -123,15 +149,23 @@ "execution_count": 5, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "At t = 339.952 and h = 1.4337e-18, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "At t = 522.687 and h = 4.04917e-14, the corrector convergence failed repeatedly or with |h| = hmin.\n" + ] + }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "45c0c0129d8f4610a494ffaaadddaa5a", + "model_id": "93feca98298f4111909ae487e2a1e273", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=38.90909528447357, step=0.3890909528447357),…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=40.13268704803602, step=0.4013268704803602),…" ] }, "metadata": {}, @@ -140,7 +174,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -154,29 +188,10 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "As we have seen, experiments allow us to define complex simulations using a very simple syntax. The instructions can be of the form \"(Dis)charge at x A/C/W\", \"Rest\", or \"Hold at x V\". The running time should be a time in seconds, minutes or hours, e.g. \"10 seconds\", \"3 minutes\" or \"1 hour\". The stopping conditions should be a circuit state, e.g. \"1 A\", \"C/50\" or \"3 V\". \n", - "\n", - "Some examples of experiment instructions are:\n", - "```python\n", - " \"Discharge at 1C for 0.5 hours\",\n", - " \"Discharge at C/20 for 0.5 hours\",\n", - " \"Charge at 0.5 C for 45 minutes\",\n", - " \"Discharge at 1 A for 90 seconds\",\n", - " \"Charge at 200mA for 45 minutes\",\n", - " \"Discharge at 1 W for 0.5 hours\",\n", - " \"Charge at 200 mW for 45 minutes\",\n", - " \"Rest for 10 minutes\",\n", - " \"Hold at 1 V for 20 seconds\",\n", - " \"Charge at 1 C until 4.1V\",\n", - " \"Hold at 4.1 V until 50 mA\",\n", - " \"Hold at 3V until C/50\",\n", - "```\n", - "\n", - "Additionally, we can use the operators `+` and `*` on lists in order to combine and repeat cycles:" + "The `solution` variable in the `simulation` object has a `cycles` variable that allows to access the solution for a specific cycle. That solution can be processed and plotted as usual. For example, if we want to plot the first cycle only we can do " ] }, { @@ -184,13 +199,24 @@ "execution_count": 6, "metadata": {}, "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4d6e43032f4e4aa6be5843c4916b4b50", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=0.0, description='t', max=13.076887099589111, step=0.1307688709958911)…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/plain": [ - "[('Discharge at 1C for 0.5 hours', 'Discharge at C/20 for 0.5 hours'),\n", - " ('Discharge at 1C for 0.5 hours', 'Discharge at C/20 for 0.5 hours'),\n", - " ('Discharge at 1C for 0.5 hours', 'Discharge at C/20 for 0.5 hours'),\n", - " ('Charge at 0.5 C for 45 minutes',)]" + "" ] }, "execution_count": 6, @@ -199,9 +225,7 @@ } ], "source": [ - "[(\"Discharge at 1C for 0.5 hours\", \"Discharge at C/20 for 0.5 hours\")] * 3 + [\n", - " (\"Charge at 0.5 C for 45 minutes\",)\n", - "]" + "sim.solution.cycles[0].plot()" ] }, { @@ -209,7 +233,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To pass additional arguments such as a period, temperature, or tags, the method `pybamm.step.string` should be used, for example:" + "Note that because `sol.cycles` is a list, the indexing starts at 0.\n", + "\n", + "As we will see in the next section, we can pass additional arguments such as a period, temperature, or tags when defining a step. The method `pybamm.step.string` can be used to add these additional conditions to a string-defined step:" ] }, { @@ -220,7 +246,7 @@ { "data": { "text/plain": [ - "_Step(C-rate, 1.0, duration=1 hour, period=1 minute, temperature=25oC, tags=['tag1'], description=Discharge at 1C for 1 hour)" + "Step(1.0, duration=1 hour, period=1 minute, temperature=25oC, tags=['tag1'], description=Discharge at 1C for 1 hour, direction=Discharge)" ] }, "execution_count": 7, @@ -258,7 +284,7 @@ { "data": { "text/plain": [ - "_Step(current, 1, duration=1 hour, termination=2.5 V)" + "Step(1, duration=1 hour, termination=2.5 V, direction=Discharge)" ] }, "execution_count": 8, @@ -286,7 +312,7 @@ { "data": { "text/plain": [ - "_Step(current, 1.0, duration=1 hour, termination=2.5V, description=Discharge at 1A for 1 hour or until 2.5V)" + "Step(1.0, duration=1 hour, termination=2.5V, description=Discharge at 1A for 1 hour or until 2.5V, direction=Discharge)" ] }, "execution_count": 9, @@ -303,11 +329,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The available methods are `current`, `c_rate`, `voltage`, `power`, and `resistance`.\n", + "The available methods are `current`, `c_rate`, `voltage`, `power`, and `resistance`. These methods also take optional keyword arguments, such as the period, temperature, tags or starting times (a complete list can be found in [the documentation](https://docs.pybamm.org/en/stable/source/api/experiment/experiment_steps.html)).\n", "\n", - "The period, temperature, and tags options are the same as for `pybamm.step.string`.\n", - "\n", - "These methods can also be used for drive cycles:" + "These methods can also be used for drive cycles. In this case, the `value` argument should be a 2-column array, where the first column is time in seconds (should start at zero) and the second column the values (i.e. current, voltage, power...). Here is an example for a synthetically defined drive cycle:" ] }, { @@ -315,10 +339,78 @@ "execution_count": 10, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-10 14:41:02.625 - [WARNING] callbacks.on_experiment_infeasible_time(240): \n", + "\n", + "\tExperiment is infeasible: default duration (1.0 seconds) was reached during 'Step([[ 0.00000000e+00 0.00000000e+00]\n", + " [ 1.69491525e-02 5.31467428e-02]\n", + " [ 3.38983051e-02 1.05691312e-01]\n", + " [ 5.08474576e-02 1.57038356e-01]\n", + " [ 6.77966102e-02 2.06606093e-01]\n", + " [ 8.47457627e-02 2.53832900e-01]\n", + " [ 1.01694915e-01 2.98183679e-01]\n", + " [ 1.18644068e-01 3.39155918e-01]\n", + " [ 1.35593220e-01 3.76285385e-01]\n", + " [ 1.52542373e-01 4.09151388e-01]\n", + " [ 1.69491525e-01 4.37381542e-01]\n", + " [ 1.86440678e-01 4.60655989e-01]\n", + " [ 2.03389831e-01 4.78711019e-01]\n", + " [ 2.20338983e-01 4.91342062e-01]\n", + " [ 2.37288136e-01 4.98406004e-01]\n", + " [ 2.54237288e-01 4.99822806e-01]\n", + " [ 2.71186441e-01 4.95576416e-01]\n", + " [ 2.88135593e-01 4.85714947e-01]\n", + " [ 3.05084746e-01 4.70350133e-01]\n", + " [ 3.22033898e-01 4.49656065e-01]\n", + " [ 3.38983051e-01 4.23867214e-01]\n", + " [ 3.55932203e-01 3.93275778e-01]\n", + " [ 3.72881356e-01 3.58228370e-01]\n", + " [ 3.89830508e-01 3.19122092e-01]\n", + " [ 4.06779661e-01 2.76400033e-01]\n", + " [ 4.23728814e-01 2.30546251e-01]\n", + " [ 4.40677966e-01 1.82080288e-01]\n", + " [ 4.57627119e-01 1.31551282e-01]\n", + " [ 4.74576271e-01 7.95317480e-02]\n", + " [ 4.91525424e-01 2.66110874e-02]\n", + " [ 5.08474576e-01 -2.66110874e-02]\n", + " [ 5.25423729e-01 -7.95317480e-02]\n", + " [ 5.42372881e-01 -1.31551282e-01]\n", + " [ 5.59322034e-01 -1.82080288e-01]\n", + " [ 5.76271186e-01 -2.30546251e-01]\n", + " [ 5.93220339e-01 -2.76400033e-01]\n", + " [ 6.10169492e-01 -3.19122092e-01]\n", + " [ 6.27118644e-01 -3.58228370e-01]\n", + " [ 6.44067797e-01 -3.93275778e-01]\n", + " [ 6.61016949e-01 -4.23867214e-01]\n", + " [ 6.77966102e-01 -4.49656065e-01]\n", + " [ 6.94915254e-01 -4.70350133e-01]\n", + " [ 7.11864407e-01 -4.85714947e-01]\n", + " [ 7.28813559e-01 -4.95576416e-01]\n", + " [ 7.45762712e-01 -4.99822806e-01]\n", + " [ 7.62711864e-01 -4.98406004e-01]\n", + " [ 7.79661017e-01 -4.91342062e-01]\n", + " [ 7.96610169e-01 -4.78711019e-01]\n", + " [ 8.13559322e-01 -4.60655989e-01]\n", + " [ 8.30508475e-01 -4.37381542e-01]\n", + " [ 8.47457627e-01 -4.09151388e-01]\n", + " [ 8.64406780e-01 -3.76285385e-01]\n", + " [ 8.81355932e-01 -3.39155918e-01]\n", + " [ 8.98305085e-01 -2.98183679e-01]\n", + " [ 9.15254237e-01 -2.53832900e-01]\n", + " [ 9.32203390e-01 -2.06606093e-01]\n", + " [ 9.49152542e-01 -1.57038356e-01]\n", + " [ 9.66101695e-01 -1.05691312e-01]\n", + " [ 9.83050847e-01 -5.31467428e-02]\n", + " [ 1.00000000e+00 -1.22464680e-16]], duration=1.0, period=0.016949152542372836, direction=Rest)'. The returned solution only contains up to step 1 of cycle 1. Please specify a duration in the step instructions.\n" + ] + }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6a03bebbd6a34a33a942c3106b70a0dd", + "model_id": "6364b4579fc447e2a607f2f8414172ba", "version_major": 2, "version_minor": 0 }, @@ -332,7 +424,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -386,13 +478,14 @@ "output_type": "stream", "text": [ "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", - "[2] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", - "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", - "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", - "[5] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n", - "[6] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", - "[7] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", - "[8] Andrew Weng, Jason B Siegel, and Anna Stefanopoulou. Differential voltage analysis for battery manufacturing process control. arXiv preprint arXiv:2303.07088, 2023.\n", + "[2] Von DAG Bruggeman. Berechnung verschiedener physikalischer konstanten von heterogenen substanzen. i. dielektrizitätskonstanten und leitfähigkeiten der mischkörper aus isotropen substanzen. Annalen der physik, 416(7):636–664, 1935.\n", + "[3] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", + "[4] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", + "[5] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", + "[6] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n", + "[7] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", + "[8] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", + "[9] Andrew Weng, Jason B Siegel, and Anna Stefanopoulou. Differential voltage analysis for battery manufacturing process control. arXiv preprint arXiv:2303.07088, 2023.\n", "\n" ] } @@ -418,7 +511,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.6" }, "toc": { "base_numbering": 1, diff --git a/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb index f2e1b9be75..f75394f133 100644 --- a/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb +++ b/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb @@ -13,7 +13,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In the previous tutorials we have interacted with the outputs of the simulation via the default dynamic plot. However, usually we need to access the output data to manipulate it or transfer to another software which is the topic of this notebook.\n", + "In the previous tutorials we have interacted with the outputs of the simulation via the default plotting functionality. However, usually we need to access the output data to manipulate it or transfer to another software, which is the topic of this notebook.\n", "\n", "We start by building and solving our model as shown in previous notebooks:" ] @@ -27,13 +27,23 @@ "name": "stdout", "output_type": "stream", "text": [ + "\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.3.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.0\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 1, @@ -76,11 +86,10 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "and now we can create a post-processed variable (for a list of all the available variables see [Tutorial 3](./tutorial-3-basic-plotting.ipynb))" + "Note that the solution object is also returned when calling the `solve` method, so this can be streamlined by running" ] }, { @@ -89,8 +98,7 @@ "metadata": {}, "outputs": [], "source": [ - "t = solution[\"Time [s]\"]\n", - "V = solution[\"Voltage [V]\"]" + "solution = sim.solve([0, 3600])" ] }, { @@ -98,40 +106,57 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "One option is to visualise the data set returned by the solver directly" + "when solving our simulation. Once we have the solution, we can define post-processed variable for the relevant variables:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, + "outputs": [], + "source": [ + "t = solution[\"Time [s]\"]\n", + "V = solution[\"Voltage [V]\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(for a list of all the available variables see [Tutorial 3](./tutorial-3-basic-plotting.ipynb)). These `ProcessedVariable` objects contain the datapoints for the corresponding variable, which can be accessed by calling the `entries` variable. For example, for voltage, we can call" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([3.77047806, 3.75305182, 3.74567027, 3.74038822, 3.73581196,\n", - " 3.73153391, 3.72742393, 3.72343929, 3.71956623, 3.71580184,\n", - " 3.71214621, 3.7086004 , 3.70516561, 3.70184253, 3.69863121,\n", - " 3.69553118, 3.69254137, 3.68966018, 3.68688562, 3.68421526,\n", - " 3.68164637, 3.67917591, 3.6768006 , 3.67451688, 3.67232094,\n", - " 3.67020869, 3.66817572, 3.66621717, 3.66432762, 3.6625009 ,\n", - " 3.66072974, 3.65900536, 3.65731692, 3.65565066, 3.65398895,\n", - " 3.65230898, 3.65058135, 3.6487688 , 3.64682546, 3.64469798,\n", - " 3.64232968, 3.63966973, 3.63668796, 3.63339303, 3.62984711,\n", - " 3.62616692, 3.6225045 , 3.61901241, 3.61580868, 3.6129572 ,\n", - " 3.61046847, 3.60831405, 3.60644483, 3.60480596, 3.60334607,\n", - " 3.60202167, 3.60079822, 3.5996495 , 3.59855637, 3.59750531,\n", - " 3.59648723, 3.59549638, 3.59452954, 3.59358541, 3.59266405,\n", - " 3.59176646, 3.59089417, 3.59004885, 3.58923192, 3.58844407,\n", - " 3.58768477, 3.58695179, 3.58624057, 3.58554372, 3.58485045,\n", - " 3.58414611, 3.58341187, 3.58262441, 3.58175587, 3.58077378,\n", - " 3.57964098, 3.57831538, 3.5767492 , 3.57488745, 3.57266504,\n", - " 3.5700019 , 3.56679523, 3.56290766, 3.5581495 , 3.55225276,\n", - " 3.54483361, 3.53533853, 3.52296795, 3.50656968, 3.48449277,\n", - " 3.45439366, 3.41299182, 3.35578871, 3.27680072, 3.16842636])" + "array([3.77048098, 3.75309871, 3.74569826, 3.74040906, 3.73582978,\n", + " 3.73155017, 3.72743983, 3.72345507, 3.71958265, 3.71581858,\n", + " 3.71216287, 3.70861698, 3.7051823 , 3.70185947, 3.69864846,\n", + " 3.69554865, 3.69255894, 3.6896778 , 3.68690322, 3.68423281,\n", + " 3.68166383, 3.67919326, 3.67681781, 3.67453394, 3.67233783,\n", + " 3.6702254 , 3.66819225, 3.66623353, 3.66434383, 3.66251699,\n", + " 3.66074577, 3.65902141, 3.65733311, 3.65566717, 3.65400602,\n", + " 3.65232696, 3.6506007 , 3.64879012, 3.64684952, 3.64472566,\n", + " 3.64236191, 3.63970731, 3.63673126, 3.63344172, 3.62989992,\n", + " 3.62622171, 3.6225587 , 3.61906361, 3.61585516, 3.61299814,\n", + " 3.61050386, 3.60834443, 3.606471 , 3.60482876, 3.60336628,\n", + " 3.60203993, 3.60081505, 3.59966528, 3.59857137, 3.59751973,\n", + " 3.59650118, 3.59550993, 3.59454272, 3.59359821, 3.59267644,\n", + " 3.59177838, 3.59090556, 3.59005965, 3.58924208, 3.58845355,\n", + " 3.58769359, 3.58695999, 3.58624826, 3.58555109, 3.58485777,\n", + " 3.58415379, 3.5834204 , 3.58263444, 3.58176818, 3.58078926,\n", + " 3.57966067, 3.57834049, 3.57678113, 3.57492782, 3.57271582,\n", + " 3.57006555, 3.566875 , 3.56300793, 3.5582764 , 3.55241508,\n", + " 3.54504405, 3.53561555, 3.52333845, 3.50707266, 3.48518447,\n", + " 3.45535426, 3.41433385, 3.35766635, 3.27941791, 3.17203869])" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -150,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -183,7 +208,7 @@ " 3490.90909091, 3527.27272727, 3563.63636364, 3600. ])" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -197,21 +222,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In addition, post-processed variables can be called at any time (by interpolation)" + "In addition, post-processed variables can be called at any time, which will return the interpolated value from the data above:" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([3.72947892, 3.7086004 , 3.67810702, 3.65400557])" + "array([3.729495 , 3.70861698, 3.67812431, 3.65402263])" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -238,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -255,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -272,13 +297,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "80a9bcfaa1264a6f80be4c12906f491e", + "model_id": "4c55af9203f344ca95df43e94633e8fc", "version_major": 2, "version_minor": 0 }, @@ -288,6 +313,16 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -304,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -322,13 +357,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1fc7b0729d6c40fe9e4f5921bb12b57d", + "model_id": "f98196a0055f45958cf6fa7b3d255e64", "version_major": 2, "version_minor": 0 }, @@ -342,17 +377,17 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sol2 = pybamm.load(\"SPMe_sol.pkl\")\n", - "pybamm.dynamic_plot(sol2)" + "sol2.plot()" ] }, { @@ -365,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -377,12 +412,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "or save in csv or mat format" + "or save it in `.csv` or `.mat` format" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -401,6 +436,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "Note that while exporting `.pkl` works for all variables, exporting to `.csv` and `.mat` only works for 0D variables (i.e. variables the do not depend on space, only on time).\n", + "\n", "In this notebook we have shown how to extract and store the outputs of PyBaMM's simulations. Next, in [Tutorial 7](./tutorial-7-model-options.ipynb) we will show how to change the model options." ] }, @@ -414,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -439,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -449,7 +486,7 @@ "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", "[2] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", "[3] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", - "[4] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n", + "[4] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", "\n" ] } @@ -475,7 +512,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.11.6" }, "toc": { "base_numbering": 1, diff --git a/docs/source/examples/notebooks/getting_started/tutorial-7-model-options.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-7-model-options.ipynb index 8969afc15a..5af3067f67 100644 --- a/docs/source/examples/notebooks/getting_started/tutorial-7-model-options.ipynb +++ b/docs/source/examples/notebooks/getting_started/tutorial-7-model-options.ipynb @@ -7,7 +7,7 @@ "source": [ "# Tutorial 7 - Model options\n", "\n", - "In all of the previous tutorials, we have made use of the default forms of the inbuilt models in PyBaMM. However, PyBaMM provides a high-level interface for tweaking these models for your particular application. " + "In all of the previous tutorials, we have made use of the default forms of the inbuilt models in PyBaMM. However, PyBaMM provides a high-level interface for tweaking these models for your particular application. The core idea is that additional physics can be added to your electrochemical model of choice. These additional models can be specified via the model options (for a full list of options see the [documentation](https://docs.pybamm.org/en/latest/source/api/models/base_models/base_battery_model.html#pybamm.BatteryModelOptions))." ] }, { @@ -19,8 +19,18 @@ "name": "stdout", "output_type": "stream", "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" + ] } ], "source": [ @@ -33,7 +43,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this tutorial, we add a thermal model to the SPMe. From the [documentation](https://docs.pybamm.org/en/latest/source/api/models/base_models/base_battery_model.html), we see that we have a choice of either a 'x-full' thermal model or a number of different lumped thermal models. For a deeper look at the thermal models see the [thermal models notebook](../models/thermal-models.ipynb). We choose the full thermal model, which solves the spatially-dependent heat equation on our battery geometry, and couples the temperature with the electrochemistry. We set the model options by creating a Python dictionary:" + "In this tutorial, we add a thermal model to the SPMe. From the [documentation](https://docs.pybamm.org/en/latest/source/api/models/base_models/base_battery_model.html), we see that we have a choice of various thermal models. For a deeper look at the thermal models see the [thermal models notebook](../models/thermal-models.ipynb). We choose the lumped thermal model, which solves the spatially-dependent heat equation on our battery geometry, and couples the temperature with the electrochemistry. We set the model options by creating a Python dictionary:" ] }, { @@ -42,7 +52,7 @@ "metadata": {}, "outputs": [], "source": [ - "options = {\"thermal\": \"x-full\"}" + "options = {\"thermal\": \"lumped\"}" ] }, { @@ -61,7 +71,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -92,7 +102,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0fc5db759e804d3fb2793222ddbec5d4", + "model_id": "7267353ed84e45a2b5aabca057eed711", "version_major": 2, "version_minor": 0 }, @@ -106,7 +116,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -125,7 +135,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this tutorial we have seen how to adjust the model options. To see all of the options currently available in PyBaMM, please take a look at the documentation [here](https://docs.pybamm.org/en/latest/source/api/models/base_models/base_battery_model.html).\n", + "In this tutorial we have seen how to adjust the model options to account for thermal effects. Here is a (non exhaustive) list of other example notebooks that demonstrate how to include additional physics:\n", + "- [Composite models](../models/composite_particle.ipynb)\n", + "- [Particle size distributions](../models/DFN-with-particle-size-distributions.ipynb)\n", + "- [Lithium plating](../models/lithium-plating.ipynb)\n", + "- [Particle cracking](../models/submodel_cracking_DFN_or_SPM.ipynb)\n", + "- [Loss of active material](../models/loss_of_active_materials.ipynb)\n", + "- [Thermal models](../models/thermal-models.ipynb)\n", + "- [Coupled degradation mechanisms](../models/coupled-degradation.ipynb)\n", "\n", "In the [next tutorial](./tutorial-8-solver-options.ipynb) we show how to change the solver options." ] @@ -179,7 +196,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.11.6" }, "vscode": { "interpreter": { diff --git a/docs/source/examples/notebooks/getting_started/tutorial-8-solver-options.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-8-solver-options.ipynb index 2e55321659..a861c88150 100644 --- a/docs/source/examples/notebooks/getting_started/tutorial-8-solver-options.ipynb +++ b/docs/source/examples/notebooks/getting_started/tutorial-8-solver-options.ipynb @@ -7,11 +7,9 @@ "source": [ "# Tutorial 8 - Solver options\n", "\n", - "In [Tutorial 7](./tutorial-7-model-options.ipynb) we saw how to change the model options. In this tutorial we will show how to pass options to the solver.\n", + "In [Tutorial 7](./tutorial-7-model-options.ipynb) we saw how to change the model options. In this tutorial we will show how to modify the solver options. All models in PyBaMM have a default solver which is typically different depending on whether the discretised model results in a system of algebraic equations, ordinary differential equations (ODEs) or differential algebraic equations (DAEs). \n", "\n", - "All models in PyBaMM have a default solver which is typically different depending on whether the model results in a system of ordinary differential equations (ODEs) or differential algebraic equations (DAEs). \n", - "\n", - "One of the most common options you will want to change is the solver tolerances. By default all tolerances are set to $10^{-6}$. However, depending on your simulation you may find you want to tighten the tolerances to obtain a more accurate solution, or you may want to loosen the tolerances to reduce the solve time. It is good practice to conduct a tolerance study, where you simulate the same problem with a tighter tolerances and compare the results. We do not show how to do this here, but we give an example of a mesh resolution study in the [next tutorial](./tutorial-9-changing-the-mesh.ipynb), which is conducted in a similar way." + "One of the most common options one might want to change is the solver tolerances. By default all tolerances are set to $10^{-6}$. However, depending on your simulation, you may find you want to tighten the tolerances to obtain a more accurate solution, or you may want to loosen the tolerances to reduce the solve time. It is good practice to conduct a tolerance study, where you simulate the same problem with a tighter tolerances and compare the results. We do not show how to do this here, but we give an example of a mesh resolution study in the [next tutorial](./tutorial-9-changing-the-mesh.ipynb), which is conducted in a similar way." ] }, { @@ -23,8 +21,18 @@ "name": "stdout", "output_type": "stream", "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" + ] } ], "source": [ @@ -37,35 +45,70 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here we will change the absolute and relative tolerances, as well as the \"mode\" of the `CasadiSolver`. For a list of all the solver options please consult the [documentation](https://docs.pybamm.org/en/latest/source/api/solvers/index.html).\n", + "Here we will change the absolute and relative tolerances, as well as the \"mode\" of the `CasadiSolver`, which is the default solver in PyBaMM. For a list of all the solver options please consult the [documentation](https://docs.pybamm.org/en/latest/source/api/solvers/index.html).\n", "\n", - "The `CasadiSolver` can operate in a number of modes, including \"safe\" (default) and \"fast\". Safe mode performs step-and-check integration and supports event handling (e.g. you can integrate until you hit a certain voltage), and is the recommended for simulations of a full charge or discharge. Fast mode performs direct integration, ignoring events, and is recommended when simulating a drive cycle or other simulation where no events should be triggered.\n", + "The `CasadiSolver` can operate in a number of modes, including \"safe\" (default) and \"fast\". The main difference between these modes is how events are handled. Safe mode performs step-and-check integration and supports event handling (e.g. you can integrate until you hit a certain voltage), and is the recommended for simulations of a full charge or discharge. Fast mode performs direct integration, ignoring events, and is recommended when simulating a drive cycle or other simulation where no events are triggered.\n", "\n", - "We'll solve the DFN with all the default options in both \"safe\" and \"fast\" mode and compare the solutions. For both simulations we'll use $10^{-3}$ for both the absolute and relative tolerance. For demonstration purposes we'll change the cut-off voltage to 3.6V so we can observe the different behaviour of the two solver modes." + "We will solve the DFN with all the default options in both \"safe\" and \"fast\" mode and compare the solutions. For both simulations we'll use $10^{-3}$ for both the absolute and relative tolerance. For demonstration purposes we change the cut-off voltage to 3.6V so we can observe the different behaviour of the two solver modes." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, + "outputs": [], + "source": [ + "model = pybamm.lithium_ion.DFN()\n", + "param = model.default_parameter_values\n", + "param[\"Lower voltage cut-off [V]\"] = 3.6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we define two instances of the solver, one using the \"safe\" mode and the other using the \"fast\" mode. Note how we also pass the tolerances as keyword arguments." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "safe_solver = pybamm.CasadiSolver(atol=1e-3, rtol=1e-3, mode=\"safe\")\n", + "fast_solver = pybamm.CasadiSolver(atol=1e-3, rtol=1e-3, mode=\"fast\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we can create two different simulations (one for each model, where this is set using the `solver` keyword argument) and we solve them. We then plot the results and print the solve time for each simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Safe mode solve time: 297.861 ms\n", - "Fast mode solve time: 100.307 ms\n" + "Safe mode solve time: 137.215 ms\n", + "Fast mode solve time: 92.051 ms\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8aba4910079445fb941fb99d01c24ea5", + "model_id": "4ddb64ca0e75408fa0455a81078720b6", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=2127.4337023841445, step=21.274337023841444)…" ] }, "metadata": {}, @@ -74,24 +117,15 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 2, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# load model and parameters\n", - "model = pybamm.lithium_ion.DFN()\n", - "param = model.default_parameter_values\n", - "param[\"Lower voltage cut-off [V]\"] = 3.6\n", - "\n", - "# load solvers\n", - "safe_solver = pybamm.CasadiSolver(atol=1e-3, rtol=1e-3, mode=\"safe\")\n", - "fast_solver = pybamm.CasadiSolver(atol=1e-3, rtol=1e-3, mode=\"fast\")\n", - "\n", "# create simulations\n", "safe_sim = pybamm.Simulation(model, parameter_values=param, solver=safe_solver)\n", "fast_sim = pybamm.Simulation(model, parameter_values=param, solver=fast_solver)\n", @@ -111,7 +145,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We see that both solvers give the same solution up to the time at which the cut-off voltage is reached. At this point the solver using \"safe\" mode stops, but the solver using \"fast\" mode carries on integrating until the final time. As its name suggests, \"fast\" mode integrates more quickly that \"safe\" mode, but is unsuitable if your simulation required events to be handled.\n", + "We see that both solvers give the same solution and that the \"fast\" solver, as the name suggests, runs faster. However, if the simulation time was longer the \"fast\" solver would not notice that the battery is discharging beyond its cut-off voltage and the solver would crash.\n", "\n", "Usually the default solver options provide a good combination of speed and accuracy, but we encourage you to investigate different solvers and options to find the best combination for your problem.\n", "\n", @@ -130,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -141,7 +175,7 @@ "[2] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", - "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n", + "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", "\n" ] } @@ -167,7 +201,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.11.6" } }, "nbformat": 4, diff --git a/docs/source/examples/notebooks/getting_started/tutorial-9-changing-the-mesh.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-9-changing-the-mesh.ipynb index 7cee8dd679..ee76da50d9 100644 --- a/docs/source/examples/notebooks/getting_started/tutorial-9-changing-the-mesh.ipynb +++ b/docs/source/examples/notebooks/getting_started/tutorial-9-changing-the-mesh.ipynb @@ -9,7 +9,7 @@ "\n", "In [Tutorial 8](./tutorial-8-solver-options.ipynb) we saw how to change the solver options. In this tutorial we will change the mesh used in the simulation, and show how to investigate the influence of the mesh on the solution.\n", "\n", - "All models in PyBaMM have a default number of mesh points used in a simulation. However, depending on things like the operating conditions you are simulating or the parameters you are using, you may find you need to increase the number points in the mesh to obtain an accurate solution. On the other hand, you may find that you are able to decrease the number of mesh points and still obtain a solution with an acceptable degree of accuracy but in a shorter amount of computational time. \n", + "All models in PyBaMM have a default number of mesh points used in a simulation. However, depending on aspects like the operating conditions or the parameters, you may find you need to increase the number points in the mesh to obtain an accurate solution. On the other hand, you may find that you are able to decrease the number of mesh points and still obtain a solution with an acceptable degree of accuracy but with a lower computational time. \n", "\n", "It is always good practice to conduct a mesh refinement study, where you simulate the same problem with a finer mesh and compare the results. Here will show how to do this graphically, but in practice you may wish to do a more detailed calculation of the relative error." ] @@ -23,8 +23,18 @@ "name": "stdout", "output_type": "stream", "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" + ] } ], "source": [ @@ -39,7 +49,7 @@ "source": [ "## Changing the number of points in the mesh\n", "\n", - "First we load a model" + "First we load a model, in this case the SPMe" ] }, { @@ -56,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can then look at the default number of points, which are stored as a dictionary whose keys are the variables for each domain" + "We can then look at the number of points that the models uses by default, which are stored as a dictionary whose keys are the variables for each domain:" ] }, { @@ -72,6 +82,10 @@ " 'x_p': 20,\n", " 'r_n': 20,\n", " 'r_p': 20,\n", + " 'r_n_prim': 20,\n", + " 'r_p_prim': 20,\n", + " 'r_n_sec': 20,\n", + " 'r_p_sec': 20,\n", " 'y': 10,\n", " 'z': 10,\n", " 'R_n': 30,\n", @@ -92,7 +106,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To run a simulation with a different number of points we can define our own dictionary " + "Note how the number of points is a dictionary where the key is the name of the spatial variable, and the value the number of points in the discretisation of that variable. To run a simulation with a different number of points we can define our own dictionary " ] }, { @@ -116,28 +130,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We then create and solve a simulation, passing the dictionary of points as a keyword argument" + "and pass it as a keyword argument when creating a simulation" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "sim = pybamm.Simulation(model, var_pts=var_pts)\n", - "sim.solve([0, 3600])" + "sim = pybamm.Simulation(model, var_pts=var_pts)" ] }, { @@ -145,7 +147,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "and plot the solution in the usual way" + "We can then solve and plot the simulation as usual:" ] }, { @@ -156,7 +158,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "897f0b16157c4d4683006a7a4beeb748", + "model_id": "57f6977be6e3499c85e6749f58cd75fa", "version_major": 2, "version_minor": 0 }, @@ -170,7 +172,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -179,6 +181,7 @@ } ], "source": [ + "sim.solve([0, 3600])\n", "sim.plot()" ] }, @@ -206,7 +209,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "and now we can loop over the list, creating and solving simulations as we go. The solutions are stored in the list `solutions`" + "and now we can loop over the list, creating and solving simulations as we go. The solutions are stored in the list `solutions`, similar to what we did in [Tutorial 2](./tutorial-2-compare-models.ipynb) for the various models." ] }, { @@ -244,7 +247,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can now pass our list of solutions to the dynamic plot method, allowing use to see the influence of the mesh on the computed voltage. We pass our list of points using the `labels` keyword so that the plots are labeled with the number of points used in the simulation" + "We can now pass our list of solutions to the dynamic plot method, allowing use to see the influence of the mesh on the computed voltage. We pass our list of points using the `labels` keyword so that the plots are labeled with the number of points used in the simulation." ] }, { @@ -255,12 +258,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e3b4ea06bd624d9eb817153870514a28", + "model_id": "875e8b946c8240c6933ba12767118950", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=3600.0, step=36.0), Output()), _dom_classes=…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…" ] }, "metadata": {}, @@ -269,7 +272,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -278,7 +281,7 @@ } ], "source": [ - "pybamm.dynamic_plot(solutions, [\"Voltage [V]\"], time_unit=\"seconds\", labels=npts)" + "pybamm.dynamic_plot(solutions, [\"Voltage [V]\"], labels=npts)" ] }, { @@ -286,7 +289,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In the [next tutorial](./tutorial-10-creating-a-model.ipynb) we show how to create a basic model from scratch in PyBaMM." + "This notebook concludes the \"Getting Started\" series, that demonstrated all the main features of PyBaMM. You may now want to explore more advanced features, so take a look at all the [examples available](https://docs.pybamm.org/en/stable/source/examples/index.html) in our website." ] }, { @@ -343,7 +346,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.11.6" }, "toc": { "base_numbering": 1, diff --git a/docs/source/examples/notebooks/initialize-model-with-solution.ipynb b/docs/source/examples/notebooks/initialize-model-with-solution.ipynb index aa7bea4d5c..b8f353896d 100644 --- a/docs/source/examples/notebooks/initialize-model-with-solution.ipynb +++ b/docs/source/examples/notebooks/initialize-model-with-solution.ipynb @@ -23,10 +23,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 21.1 is available.\n", - "You should consider upgrading via the '/Users/vsulzer/Documents/Energy_storage/PyBaMM/.tox/dev/bin/python -m pip install --upgrade pip' command.\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" + ] } ], "source": [ @@ -76,8 +81,9 @@ "outputs": [], "source": [ "# import drive cycle from file\n", + "data_loader = pybamm.DataLoader()\n", "drive_cycle = pd.read_csv(\n", - " \"pybamm/input/drive_cycles/US06.csv\", comment=\"#\", header=None\n", + " f\"{data_loader.get_data(\"US06.csv\")}\", comment=\"#\", header=None\n", ").to_numpy()\n", "# create interpolant\n", "param = model.default_parameter_values\n", @@ -160,7 +166,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7ad44c260ec446e0bd04a2f7dfbf8c65", + "model_id": "cd7fa37499ed49a7a8fa691a0ad9ffa6", "version_major": 2, "version_minor": 0 }, @@ -174,7 +180,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -241,7 +247,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aaf63750ed354d1987703fd2fdde83af", + "model_id": "22c047080ebe4a14a664affed4e97ba1", "version_major": 2, "version_minor": 0 }, @@ -255,7 +261,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -329,7 +335,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.12.3" }, "toc": { "base_numbering": 1, diff --git a/docs/source/examples/notebooks/models/MSMR.ipynb b/docs/source/examples/notebooks/models/MSMR.ipynb index 3f009a045a..370e8911b3 100644 --- a/docs/source/examples/notebooks/models/MSMR.ipynb +++ b/docs/source/examples/notebooks/models/MSMR.ipynb @@ -17,7 +17,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "zsh:1: no matches found: pybamm[plot,cite]\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } @@ -48,7 +47,7 @@ "$$ \\text{Li}^{+} + \\text{e}^{-} + \\text{H}_{j} \\rightleftharpoons (\\text{Li--H})_{j}.$$\n", "For each species $j$, a vacant host site $\\text{H}_{j}$ can accommodate one lithium leading to a filled host site $(\\text{Li--H})_{j}$. The OCV for this reaction is written as\n", "$$ U_j = U_j^0 + \\frac{\\omega_j}{f}\\log\\left(\\frac{X_j - x_j}{x_j}\\right),$$\n", - "where $f = (RT)/F$, and $R$, $T$, and $F$ are the universal gas constant, temperature in Kelvin, and Faraday’s constant, respectively. Here $X_j$ represents the total fraction of available host sites which can be occupied by species $j$, $x_j$ is the fraction of filled sites occupied by species $j$, $U_j^0$ is a concentration independent standard electrode potential, and the $\\omega_j$ is an unitless parameter that describes the level of disorder of the reaction represented by gallery $j$. \n", + "where $f = F/(RT)$, and $R$, $T$, and $F$ are the universal gas constant, temperature in Kelvin, and Faraday’s constant, respectively. Here $X_j$ represents the total fraction of available host sites which can be occupied by species $j$, $x_j$ is the fraction of filled sites occupied by species $j$, $U_j^0$ is a concentration independent standard electrode potential, and the $\\omega_j$ is an unitless parameter that describes the level of disorder of the reaction represented by gallery $j$. \n", "\n", "The equation for each reaction can be inverted to give \n", "$$x_j = \\frac{X_j}{1+\\exp[f(U-U_j^0)/\\omega_j]}.$$\n", @@ -188,7 +187,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -216,8 +215,8 @@ "colors = [\"r\", \"g\", \"b\", \"c\", \"m\", \"y\"]\n", "\n", "# sto vs potential\n", - "x_n = param_n.x(U_n)\n", - "x_p = param_p.x(U_p)\n", + "x_n = param_n.x(U_n, T)\n", + "x_p = param_p.x(U_p, T)\n", "ax[0, 0].plot(parameter_values.evaluate(x_n), parameter_values.evaluate(U_n), \"k-\")\n", "ax[0, 1].plot(parameter_values.evaluate(x_p), parameter_values.evaluate(U_p), \"k-\")\n", "ax[0, 0].set_xlabel(\"x_n\")\n", @@ -227,7 +226,7 @@ "\n", "# fractional occupancy vs potential\n", "for i in range(6):\n", - " xj = param_n.x_j(U_n, i)\n", + " xj = param_n.x_j(U_n, T, i)\n", " ax[1, 0].plot(\n", " parameter_values.evaluate(x_n),\n", " parameter_values.evaluate(xj),\n", @@ -238,7 +237,7 @@ "ax[1, 0].set_ylabel(\"x_n_j\")\n", "ax[1, 0].legend()\n", "for i in range(4):\n", - " xj = param_p.x_j(U_p, i)\n", + " xj = param_p.x_j(U_p, T, i)\n", " ax[1, 1].plot(\n", " parameter_values.evaluate(x_p),\n", " parameter_values.evaluate(xj),\n", @@ -251,7 +250,7 @@ "\n", "# exchange current density vs potential\n", "for i in range(6):\n", - " xj = param_n.x_j(U_n, i)\n", + " xj = param_n.x_j(U_n, T, i)\n", " j0 = param_n.j0_j(c_e, U_n, T, i)\n", " ax[2, 0].plot(\n", " parameter_values.evaluate(x_n),\n", @@ -263,7 +262,7 @@ "ax[2, 0].set_ylabel(\"j0_n_j [A.m-2]\")\n", "ax[2, 0].legend()\n", "for i in range(4):\n", - " xj = param_p.x_j(U_p, i)\n", + " xj = param_p.x_j(U_p, T, i)\n", " j0 = param_p.j0_j(c_e, U_p, T, i)\n", " ax[2, 1].plot(\n", " parameter_values.evaluate(x_p),\n", @@ -297,14 +296,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "At t = 275.026 and h = 2.68649e-11, the corrector convergence failed repeatedly or with |h| = hmin.\n", - "At t = 275.028 and h = 4.19765e-11, the corrector convergence failed repeatedly or with |h| = hmin.\n" + "At t = 274.817 and h = 2.46871e-10, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "At t = 274.836 and h = 1.27139e-11, the corrector convergence failed repeatedly or with |h| = hmin.\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -345,12 +344,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "67da37f9dcb64ac696aca8772d5ffce7", + "model_id": "71c516c9569941ff8dc0e1afb57a3a8d", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.106530343899824, step=0.06106530343899824)…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.106474917691876, step=0.06106474917691876)…" ] }, "metadata": {}, @@ -359,7 +358,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -399,12 +398,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0b056c49819644d6848340ae609978f2", + "model_id": "94a091a0fe204a53b0d2ce09ebcd7fc9", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.106530343899824, step=0.06106530343899824)…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.106474917691876, step=0.06106474917691876)…" ] }, "metadata": {}, @@ -413,7 +412,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -456,7 +455,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -465,7 +464,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -520,14 +519,15 @@ "text": [ "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", "[2] Daniel R Baker and Mark W Verbrugge. Multi-species, multi-reaction model for porous intercalation electrodes: part i. model formulation and a perturbation solution for low-scan-rate, linear-sweep voltammetry of a spinel lithium manganese oxide electrode. Journal of The Electrochemical Society, 165(16):A3952, 2018.\n", - "[3] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", - "[4] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", - "[5] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", - "[6] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n", - "[7] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", - "[8] Mark Verbrugge, Daniel Baker, Brian Koch, Xingcheng Xiao, and Wentian Gu. Thermodynamic model for substitutional materials: application to lithiated graphite, spinel manganese oxide, iron phosphate, and layered nickel-manganese-cobalt oxide. Journal of The Electrochemical Society, 164(11):E3243, 2017.\n", - "[9] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", - "[10] Andrew Weng, Jason B Siegel, and Anna Stefanopoulou. Differential voltage analysis for battery manufacturing process control. arXiv preprint arXiv:2303.07088, 2023.\n", + "[3] Von DAG Bruggeman. Berechnung verschiedener physikalischer konstanten von heterogenen substanzen. i. dielektrizitätskonstanten und leitfähigkeiten der mischkörper aus isotropen substanzen. Annalen der physik, 416(7):636–664, 1935.\n", + "[4] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", + "[5] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", + "[6] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", + "[7] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n", + "[8] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", + "[9] Mark Verbrugge, Daniel Baker, Brian Koch, Xingcheng Xiao, and Wentian Gu. Thermodynamic model for substitutional materials: application to lithiated graphite, spinel manganese oxide, iron phosphate, and layered nickel-manganese-cobalt oxide. Journal of The Electrochemical Society, 164(11):E3243, 2017.\n", + "[10] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", + "[11] Andrew Weng, Jason B Siegel, and Anna Stefanopoulou. Differential voltage analysis for battery manufacturing process control. arXiv preprint arXiv:2303.07088, 2023.\n", "\n" ] } @@ -539,7 +539,7 @@ ], "metadata": { "kernelspec": { - "display_name": "dev", + "display_name": "venv", "language": "python", "name": "python3" }, @@ -553,11 +553,11 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.11.8" }, "vscode": { "interpreter": { - "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c" + "hash": "9ff3d0c7e37de5f5aa47f4f719e4c84fc6cba7b39c571a05173422444e82fa58" } } }, diff --git a/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb b/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb index f9d41ffc54..47d618fb23 100644 --- a/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb +++ b/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb @@ -20,9 +20,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.0.1\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } @@ -103,10 +100,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "At t = 426.174, , mxstep steps taken before reaching tout.\n", - "At t = 186.174, , mxstep steps taken before reaching tout.\n", - "At t = 430.603, , mxstep steps taken before reaching tout.\n", - "At t = 190.603, , mxstep steps taken before reaching tout.\n" + "At t = 431.796, , mxstep steps taken before reaching tout.\n", + "At t = 191.796, , mxstep steps taken before reaching tout.\n", + "At t = 430.599, , mxstep steps taken before reaching tout.\n", + "At t = 190.599, , mxstep steps taken before reaching tout.\n" ] } ], @@ -154,26 +151,24 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 4))\n", "ax1.plot(t1, V1, label=\"without cracking\")\n", - "ax1.plot(t2, V2, label=\"with cracking\")\n", + "ax1.plot(t2, V2, label=\"with cracking\", linestyle=\"dashed\")\n", "ax1.set_xlabel(\"Time [s]\")\n", "ax1.set_ylabel(\"Voltage [V]\")\n", "ax1.legend()\n", "ax2.plot(t1, SEI1, label=\"without cracking\")\n", - "ax2.plot(t2, SEI2, label=\"with cracking\")\n", + "ax2.plot(t2, SEI2, label=\"with cracking\", linestyle=\"dashed\")\n", "ax2.set_xlabel(\"Time [s]\")\n", "ax2.set_ylabel(\"Loss of lithium to SEI [mol]\")\n", "ax2.legend()\n", @@ -196,14 +191,12 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -228,14 +221,15 @@ "text": [ "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n", "[2] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", - "[3] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", - "[4] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n", - "[5] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", - "[6] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", - "[7] Scott G. Marquis. Long-term degradation of lithium-ion batteries. PhD thesis, University of Oxford, 2020.\n", - "[8] Simon E. J. O'Kane, Ian D. Campbell, Mohamed W. J. Marzook, Gregory J. Offer, and Monica Marinescu. Physical origin of the differential voltage minimum associated with lithium plating in li-ion batteries. Journal of The Electrochemical Society, 167(9):090540, may 2020. URL: https://doi.org/10.1149/1945-7111/ab90ac, doi:10.1149/1945-7111/ab90ac.\n", - "[9] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n", - "[10] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", + "[3] Von DAG Bruggeman. Berechnung verschiedener physikalischer konstanten von heterogenen substanzen. i. dielektrizitätskonstanten und leitfähigkeiten der mischkörper aus isotropen substanzen. Annalen der physik, 416(7):636–664, 1935.\n", + "[4] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", + "[5] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n", + "[6] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", + "[7] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", + "[8] Scott G. Marquis. Long-term degradation of lithium-ion batteries. PhD thesis, University of Oxford, 2020.\n", + "[9] Simon E. J. O'Kane, Ian D. Campbell, Mohamed W. J. Marzook, Gregory J. Offer, and Monica Marinescu. Physical origin of the differential voltage minimum associated with lithium plating in li-ion batteries. Journal of The Electrochemical Society, 167(9):090540, may 2020. URL: https://doi.org/10.1149/1945-7111/ab90ac, doi:10.1149/1945-7111/ab90ac.\n", + "[10] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n", + "[11] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", "\n" ] } @@ -261,7 +255,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.3" }, "vscode": { "interpreter": { diff --git a/docs/source/examples/notebooks/models/SPM.ipynb b/docs/source/examples/notebooks/models/SPM.ipynb index 9b01b13a80..821580e3d7 100644 --- a/docs/source/examples/notebooks/models/SPM.ipynb +++ b/docs/source/examples/notebooks/models/SPM.ipynb @@ -104,7 +104,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The model object is a subtype of [`pybamm.BaseModel`](https://docs.pybamm.org/en/latest/source/api/models/base_models/base_model.html), and contains all the equations that define this particular model. For example, the `rhs` dict contained in `model` has a dictionary mapping variables such as $c_n$ to the equation representing its rate of change with time (i.e. $\\partial{c_n}/\\partial{t}$). We can see this explicitly by visualising this entry in the `rhs` dict:" + "The model object is a subtype of [pybamm.BaseModel](https://docs.pybamm.org/en/stable/source/api/models/base_models/base_model.html), and contains all the equations that define this particular model. For example, the `rhs` dict contained in `model` has a dictionary mapping variables such as $c_n$ to the equation representing its rate of change with time (i.e. $\\partial{c_n}/\\partial{t}$). We can see this explicitly by visualising this entry in the `rhs` dict:" ] }, { @@ -141,7 +141,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We need a geometry in which to define our model equations. In pybamm this is represented by the [`pybamm.Geometry`](https://docs.pybamm.org/en/latest/source/api/geometry/index.html) class. In this case we use the default geometry object defined by the model" + "We need a geometry in which to define our model equations. In pybamm this is represented by the [pybamm.Geometry](https://docs.pybamm.org/en/stable/source/api/geometry/index.html) class. In this case we use the default geometry object defined by the model" ] }, { @@ -202,7 +202,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Both the model equations and the geometry include parameters, such as $L_p$. We can substitute these symbolic parameters in the model with values by using the [`pybamm.ParameterValues`](https://docs.pybamm.org/en/latest/source/api/parameters/parameter_values.html) class, which takes either a python dictionary or CSV file with the mapping between parameter names and values. Rather than create our own instance of `pybamm.ParameterValues`, we will use the default parameter set included in the model" + "Both the model equations and the geometry include parameters, such as $L_p$. We can substitute these symbolic parameters in the model with values by using the [pybamm.ParameterValues](https://docs.pybamm.org/en/stable/source/api/parameters/parameter_values.html) class, which takes either a python dictionary or CSV file with the mapping between parameter names and values. Rather than create our own instance of `pybamm.ParameterValues`, we will use the default parameter set included in the model" ] }, { @@ -237,7 +237,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The next step is to mesh the input geometry. We can do this using the [`pybamm.Mesh`](https://docs.pybamm.org/en/latest/source/api/meshes/index.html) class. This class takes in the geometry of the problem, and also two dictionaries containing the type of mesh to use within each domain of the geometry (i.e. within the positive or negative electrode domains), and the number of mesh points. \n", + "The next step is to mesh the input geometry. We can do this using the [pybamm.Mesh](https://docs.pybamm.org/en/stable/source/api/meshes/index.html) class. This class takes in the geometry of the problem, and also two dictionaries containing the type of mesh to use within each domain of the geometry (i.e. within the positive or negative electrode domains), and the number of mesh points. \n", "\n", "The default mesh types and the default number of points to use in each variable for the SPM are:" ] @@ -310,7 +310,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The next step is to discretise the model equations using this mesh. We do this using the [`pybamm.Discretisation`](https://docs.pybamm.org/en/latest/source/api/spatial_methods/discretisation.html) class, which takes both the mesh we have already created, and a dictionary of spatial methods to use for each geometry domain. For the case of the SPM, we use the following defaults for the spatial discretisation methods:" + "The next step is to discretise the model equations using this mesh. We do this using the [pybamm.Discretisation](https://docs.pybamm.org/en/stable/source/api/spatial_methods/discretisation.html) class, which takes both the mesh we have already created, and a dictionary of spatial methods to use for each geometry domain. For the case of the SPM, we use the following defaults for the spatial discretisation methods:" ] }, { @@ -1118,7 +1118,7 @@ ], "metadata": { "kernelspec": { - "display_name": "pybamm", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1132,7 +1132,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.12.3" }, "toc": { "base_numbering": 1, @@ -1154,5 +1154,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb b/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb index 8e1b742c15..9bb8576a95 100644 --- a/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb +++ b/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -20,6 +20,13 @@ "text": [ "Note: you may need to restart the kernel to use updated packages.\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" + ] } ], "source": [ @@ -41,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -65,20 +72,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.5 C\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "0.5 C\n", "1 C\n", "2 C\n" ] @@ -130,33 +131,70 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading file '0.1C_discharge_displacement.txt' from 'https://github.com/pybamm-team/pybamm-data/releases/download/v1.0.0/0.1C_discharge_displacement.txt' to '/home/santa/.cache/pybamm/v1.0.0'.\n", + "Downloading file '0.5C_discharge_displacement.txt' from 'https://github.com/pybamm-team/pybamm-data/releases/download/v1.0.0/0.5C_discharge_displacement.txt' to '/home/santa/.cache/pybamm/v1.0.0'.\n", + "Downloading file '1C_discharge_displacement.txt' from 'https://github.com/pybamm-team/pybamm-data/releases/download/v1.0.0/1C_discharge_displacement.txt' to '/home/santa/.cache/pybamm/v1.0.0'.\n", + "Downloading file '2C_discharge_displacement.txt' from 'https://github.com/pybamm-team/pybamm-data/releases/download/v1.0.0/2C_discharge_displacement.txt' to '/home/santa/.cache/pybamm/v1.0.0'.\n", + "Downloading file '0.1C_discharge_U.txt' from 'https://github.com/pybamm-team/pybamm-data/releases/download/v1.0.0/0.1C_discharge_U.txt' to '/home/santa/.cache/pybamm/v1.0.0'.\n", + "Downloading file '0.5C_discharge_U.txt' from 'https://github.com/pybamm-team/pybamm-data/releases/download/v1.0.0/0.5C_discharge_U.txt' to '/home/santa/.cache/pybamm/v1.0.0'.\n", + "Downloading file '1C_discharge_U.txt' from 'https://github.com/pybamm-team/pybamm-data/releases/download/v1.0.0/1C_discharge_U.txt' to '/home/santa/.cache/pybamm/v1.0.0'.\n", + "Downloading file '2C_discharge_U.txt' from 'https://github.com/pybamm-team/pybamm-data/releases/download/v1.0.0/2C_discharge_U.txt' to '/home/santa/.cache/pybamm/v1.0.0'.\n", + "Downloading file '0.5C_discharge_T.txt' from 'https://github.com/pybamm-team/pybamm-data/releases/download/v1.0.0/0.5C_discharge_T.txt' to '/home/santa/.cache/pybamm/v1.0.0'.\n", + "Downloading file '1C_discharge_T.txt' from 'https://github.com/pybamm-team/pybamm-data/releases/download/v1.0.0/1C_discharge_T.txt' to '/home/santa/.cache/pybamm/v1.0.0'.\n", + "Downloading file '2C_discharge_T.txt' from 'https://github.com/pybamm-team/pybamm-data/releases/download/v1.0.0/2C_discharge_T.txt' to '/home/santa/.cache/pybamm/v1.0.0'.\n" + ] + } + ], "source": [ "# load experimental results\n", "import pandas as pd\n", "\n", - "path = \"pybamm/input/discharge_data/Enertech_cells/\"\n", + "data_loader = pybamm.DataLoader()\n", + "\n", "data_Disp_01C = pd.read_csv(\n", - " path + \"0.1C_discharge_displacement.txt\", delimiter=\"\\s+\", header=None\n", + " data_loader.get_data(\"0.1C_discharge_displacement.txt\"),\n", + " delimiter=\"\\s+\",\n", + " header=None,\n", ")\n", "data_Disp_05C = pd.read_csv(\n", - " path + \"0.5C_discharge_displacement.txt\", delimiter=\"\\s+\", header=None\n", + " data_loader.get_data(\"0.5C_discharge_displacement.txt\"),\n", + " delimiter=\"\\s+\",\n", + " header=None,\n", ")\n", "data_Disp_1C = pd.read_csv(\n", - " path + \"1C_discharge_displacement.txt\", delimiter=\"\\s+\", header=None\n", + " data_loader.get_data(\"1C_discharge_displacement.txt\"), delimiter=\"\\s+\", header=None\n", ")\n", "data_Disp_2C = pd.read_csv(\n", - " path + \"2C_discharge_displacement.txt\", delimiter=\"\\s+\", header=None\n", + " data_loader.get_data(\"2C_discharge_displacement.txt\"), delimiter=\"\\s+\", header=None\n", + ")\n", + "data_V_01C = pd.read_csv(\n", + " data_loader.get_data(\"0.1C_discharge_U.txt\"), delimiter=\"\\s+\", header=None\n", ")\n", - "data_V_01C = pd.read_csv(path + \"0.1C_discharge_U.txt\", delimiter=\"\\s+\", header=None)\n", - "data_V_05C = pd.read_csv(path + \"0.5C_discharge_U.txt\", delimiter=\"\\s+\", header=None)\n", - "data_V_1C = pd.read_csv(path + \"1C_discharge_U.txt\", delimiter=\"\\s+\", header=None)\n", - "data_V_2C = pd.read_csv(path + \"2C_discharge_U.txt\", delimiter=\"\\s+\", header=None)\n", - "data_T_05C = pd.read_csv(path + \"0.5C_discharge_T.txt\", delimiter=\"\\s+\", header=None)\n", - "data_T_1C = pd.read_csv(path + \"1C_discharge_T.txt\", delimiter=\"\\s+\", header=None)\n", - "data_T_2C = pd.read_csv(path + \"2C_discharge_T.txt\", delimiter=\"\\s+\", header=None)" + "data_V_05C = pd.read_csv(\n", + " data_loader.get_data(\"0.5C_discharge_U.txt\"), delimiter=\"\\s+\", header=None\n", + ")\n", + "data_V_1C = pd.read_csv(\n", + " data_loader.get_data(\"1C_discharge_U.txt\"), delimiter=\"\\s+\", header=None\n", + ")\n", + "data_V_2C = pd.read_csv(\n", + " data_loader.get_data(\"2C_discharge_U.txt\"), delimiter=\"\\s+\", header=None\n", + ")\n", + "data_T_05C = pd.read_csv(\n", + " data_loader.get_data(\"0.5C_discharge_T.txt\"), delimiter=\"\\s+\", header=None\n", + ")\n", + "data_T_1C = pd.read_csv(\n", + " data_loader.get_data(\"1C_discharge_T.txt\"), delimiter=\"\\s+\", header=None\n", + ")\n", + "data_T_2C = pd.read_csv(\n", + " data_loader.get_data(\"2C_discharge_T.txt\"), delimiter=\"\\s+\", header=None\n", + ")" ] }, { @@ -169,12 +207,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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2bt067c8//9Q6duyoVa5c2eJ32LZtW61+/fravn37tF27dmnVqlXTunXrZj4eHx+vlS1bVuvVq5cWGRmpLV++XLO3t9e+/fbbh6rLg2TnMxzceLAGRvN/zxeeGaS97DDvv+dGDdt4De6m/hf2Gay5z3LXVh+TdkWIzEibK22upuVMmyszbbNo6pzejBywmH+uVued4X48VdmNd4b78c8/1Rg5YDFT5/TO7yoKIfJQp06d+Pvvv1m/fj1t27YlNDSUBg0aWIR0Se/ZZ581/2xKNOHh4WGxL/3yuof166+/0rp1a5566imKFy9Or169+Oeff7h161aWr3H8+HEqVKhAhQoVzPtq166Ns7Mzx48fN+9zd3enePHUZBVubm6PXH8hRDb5+0NgIEREgKcnODqqbWQkNG0Khw5BLiduFSK3SJsrbW5Wvf7660yfPp1x48ZRr149jhw5wubNm83/Bi5cuEBMmvjenp6eLF26lPnz51O3bl0CAwNZu3atOTE2wKhRoxg0aBD9+/encePG3Lhxg82bN2NnZ2cus2TJEmrWrEnr1q15+eWXadGiBfPnzzcfd3JyYuvWrURFRdGwYUOGDx/OuHHjLJLjZKUuuWHWZ+1xc9xlfr7j6FdsuvEO6O4COkh2hOKpvzM/l+F4uHjQeWVngo4H5WrdhBB5T9rcgtXmFuhEZAXN1Dm9mTg9ma+nB3I2+gZPuzvw/ohXsLWTAVshcoS9ff4NKtjbP/QpdnZ2vPTSS7z00kt8/PHHvPXWW4wfPz7TWDw2Njbmn3U6XYb7jGmWNltZWaGlW86SdlldetHR0fj6+vLee+/x2WefUbJkSXbt2kW/fv1ITk7GPhvv8X7S1j2j+gsh8pi/P3TsCGFhKumYmxuUKwc1asDvv8MjJKsRjyFpc6XNfUwNHDiQgQMHZngsNDT0nn2vvfYar732WqbX0+l0fPLJJ3zyySeZlilZsiRLly69b72effZZwsLC7lvmQXXJFS++yMXrdthyAyNFUvdrNpjj214vD7YJkOzE8ZRg9r76E73X9GbE1hF0rNERvZU+b+ssRGEjba60udkkg7YPydbOliFjO+d3NYR4POl0UKxYftci22rXrm0Oyp4TypQpYzEbxGAwEBkZSatWrTIsf+jQIYxGIzNmzMDKSi2kWLlypUUZW1tbDIb7xyGrVasWFy9e5OLFi+a7kMeOHSM+Pp7atWs/ylsSQuQ2vR68vFKfr1mjts88A9byZ59IQ9pcC9LmiieWXo++RXOWhvWiKyv+26lLtwVro0aK1V1OPv0Bzy/4hoAXAui0qhMBoQG0rtKalhVbyuCtEJmRNteCtLlZJ+ERhBDiIf3zzz+8+OKLLF68mD///JOoqChWrVrF1KlT6dixY469zosvvsjGjRvZuHEjJ06c4L333iM+Pj7T8lWrVuXu3bvMnj2bc+fO8fPPPzNv3jyLMu7u7ty4cYPt27dz9erVDJeTeHt74+HhQY8ePTh8+DD79++nd+/evPDCCzRq1CjH3p/Ifzt37qRDhw6UK1fOIhOsiaZpjBs3Djc3N4oWLYq3tzenT5/On8qK7PnjD7WtWzd/6yFENkmbK0Qe+PhjXmcVHVmbwcEUQEdKihMtrH+hRHQrImKP0iWwCwATwybSalErqs6uKuEShCjkpM0teGTQVgghHpKDgwNNmzZl5syZPP/889SpU4ePP/6Yt99+mzlz5uTY67z55pv06dPH3JBUqVIl07uPAHXr1uWLL75gypQp1KlThyVLljB58mSLMp6enrz77ru8/vrrlClThqlTp95zHZ1Ox7p16yhRogTPP/883t7eVKlShRUrVtxTVhRuN2/epG7dusydOzfD41OnTuWrr75i3rx57Nu3j2LFiuHj48OdO3fyuKbigQwGCA2FZcvU1jTTICJCbdPEFROiMJE2V4g88OKLYGPDWvwZwXT0pKQ5aFqloWNX8iv8u3grfHUGw9FXAPis1WeE9wuXOLdCPAakzS14dFr6QBKPucTERJycnEhISMDR0TG/qyPEE+vOnTtERUVRuXJli0QO4slzv38L8p2dd3Q6HWvWrMHPzw9Qs2zLlSvH8OHDGTFiBAAJCQmULVuWhQsX0rVr1yxdVz7DPBAUBMOHQ3R06j53d5gxA8aNg6NHYfNm8PHJrxqKAkDaXQHS5j7uHukzHDcOPv0UgGSsmc0AxlZqw53zbSlLHHG40ZQ9nH/nMwz7P+bKkSbwWmf0z6xnQccF9Hi2B37L/Yi8HMnpQaclVIJ4okmbKyBn2lyZaSuEEEKIe0RFRREbG4u3t7d5n5OTE02bNiU8PDzT85KSkkhMTLR4iFwUFASdO6uZtOHhcP262np4qP0nT6py1arlbz2FEEIUbOPHmxMW2ZLCEGbj+HctqB7MM680p6juBvvwJDauNNXfHwXVginy2xwMBo3ea3szKWwSbaq0ISo+itn7Z2Mw3j+2pBBCiAeTQVshhBBC3CM2NhaAsmXLWuwvW7as+VhGJk+ejJOTk/lhCvIvcoHBoGbY+vrC2rXQrBk4OKjt2rXQujWkpKgEZJUq5XdthRBCFGR6Pfz8s/lpGC25fLcyk5Mnca7yeW63/kQd2DaVC1fimRTgSNLVcnRz/AaAj0M+ZtDmQQAM3TJUYtwKIUQOkEHbh3Tl5hWm7Z7GzPCZ+V0VIYQQosAZPXo0CQkJ5sfFixfzu0qPr7AwFRJhzBiwSvcnnZWVGswFKFdOdcaFEEKI+/H3h9WroUwZYnADYGB0JGe+gq1NLlPK+SzcLEvTkB8Y6OsFQFmtHjp0FpeZ6TNTYtwKIUQOkEHbh7T3r72M+nUUk3ZNIiklKb+rI4QQQuQKV1dXAOLi4iz2x8XFmY9lpEiRIjg6Olo8RC6JiVHbOnUyPp7yXyKZ0qXzpj5CCCEKv44doVgx3J4pBUDkU23Ra/BS5RYsXaFWbaz+tT5rVqs2Zln0F/hW92Wp/1Lz4G3gsUAWdFyAb3VfRmwdIaEShBAim2TQ9iG1q9aOp4o/xdVbV1l3ch0Go4HQ6FCWRSwjNDpUGiQhhBCPhcqVK+Pq6sr27dvN+xITE9m3bx/NmzfPx5oJMzc1C4rIyIyP79untlWr5k19hBBCFH7/reJo+W1P3N1hUvHJGNHBypW0aWNNs+Yn0DRr3hpwg1LlEogrtYoO1TuwLHIZAPbW9uy+uJsXFr5Av/r9iIqPIuxCWP6+JyGEKKRk0PYhWVtZ069+PwAm7pxI1dlVabWoFd2DutNqUSuJ3SOEEKLQuHHjBkeOHOHIkSOASj525MgRLly4gE6nY8iQIUycOJH169cTERFB7969KVeuHH5+fvlab/Gfli1RPepJYDRaHjMaITRU/dy6dV7XTAghRGH13yoOfd06zJgBwSer4sdawrff4nrUVUYOqIiOFO7ecuafSt+DlZH+wf2JvBxJYJdA9r61l3LFy3H0ylHeWv8WANvPbZfJTUIIkQ0yaJsNb9Z/E4CIyxFUKVGF8H7hXB99nfB+4RK7RwghRKFx8OBB6tevT/369QEYNmwY9evXZ9y4cQCMGjWKQYMG0b9/fxo3bsyNGzfYvHkzdnZ2+VltYaLXo3rUweDnB+HhcP262vr5wZUrqlzNmvlZSyGEEIVJmlUc/v4QGKgjwrYhnsZdOFYpTaee9jiTCECxE+/C770ZXHYNJ94/jX8tfzzKejDu+XHYWNlw9fZVACaGTZTJTUIIkQ0yaJsN5R3LU9S6KACN3BrRrHwzHGwdaFa+GWu7rpXYPUIIIQoFLy8vNE2757Fw4UIAdDodn3zyCbGxsdy5c4dff/2V6tWr52+lRSqDAUqWhMGD4cAB8PQER0e1jYhILVejRv7VUQghROGSbhWHvz+cGb+YELxYWmcSIc3HMLv0BPR6jZv/FoN1i/jyPT9qVLciKAiCjgfx3sb3eLHyixS3LQ6ArZUtLsVcZHKTEEI8JBm0zYawC2HcTrkNwMI/FpJsSDYfs9JZMcpzFFHxUQSEBkicWyFEnnrjjTdk6boQT4KgIBWrtlUrmDULYmOhbFkYMgRCQmDdOlXOyQlcXPKzpkI8tqTNFY+lDFZx6H288WIH3SL/x7XwE/T6Zxb16qmkYza2BujyKrdL7qdzZ413p23huQrPYWNlw43kGzRwa0CyMZmDlw5St2xdmdwkhMi2J7HdlUHbbIi5ruL8lC1Wlss3L7P+5HrzsaDjQfRc0xNQy0Akzq0Qj5833ngDnU53z6Nt27b5XTW+/PJL8yzJ/KbT6Vi7dm1+V0OIx09QEHTuDB4eliERmjSBL7+Ea9fgzBlVtnp10Onyt75CPAJpc7NG2lyRo1RcBLVqw9MTGjUCwIAVw5mBr9129n0YhJcX3E3W0zxhNnY9u6FV28CVNR+y6/wejl45qmLc9ttLn7p9MGLkSNwRouKjGB86XiY3CVFASbubNXnV7sqgbTa4FVdxftpVbQfA/EPzATVg23llZyo4VgBgU/dNEudWiLxgMKiEO8uWqa0h9/8AbNu2LTExMRaPZcuW5frrZsZgMGA0GnFycsLZ2Tnf6iGEyGUGAwwfDr6+sHYtNGsGDg5qu3at2j9iRGp4hFq18rO24nEkba60ueLJ4O8P06apG38NGoCvL2G0JJrKjGm4Bf3rnZnVYTtWVhC+pTwL6p/mjYGxEF+Fz6vt4fQgFePWRm/Dgo4L8KvpZ770Z2GfyeQmIbIiH9pckHa3IJFB22xoWbEl7s7uXEy8CMC2c9vYeGojw7cOp3319jjbOVPZuTJtnm4jcW6FyG1plwh37662Vauq/bmoSJEiuLq6WjxKlChBaGgotra2hIWFmctOnToVFxcX4uLiABVHdODAgQwcOBAnJydKly7Nxx9/jKZp5nOSkpIYMWIETz31FMWKFaNp06aEmjLBAwsXLsTZ2Zn169dTu3ZtihQpwoULF+5ZMuLl5cWgQYMYMmQIJUqUoGzZsnz33XfcvHmTvn37Urx4capWrcovv/xi8f4iIyNp164dDg4OlC1bll69enH16lWL637wwQeMGjWKkiVL4urqSkBAgPm4u7s7AK+++io6nc78XAjxiMLCIDoaxowBq3R/xllZwejREBWl/rAHNRtXiJwiba60ueLJYTDAyJHqZuCBAzB1KjGoyUt1lo4BX1/qznmb/m8bARg2VI//8+pGYVJ8SfRWevOl1pxYw7oT66jsXNm8r83TbXimzDMyuUmIzORTmwvS7hakdlcGbbNBb6VnRpsZ/Bb1G08VfwqAV5a9QnR8NPG349l4eiPT20w3N1SapuHztA9R8VHM3j9bBm6FyCmZLRH28FD786BBS8/Ly4shQ4bQq1cvEhIS+P333/n444/5/vvvKVu2rLncokWLsLa2Zv/+/Xz55Zd88cUXfP/99+bjAwcOJDw8nOXLl/Pnn3/y2muv0bZtW06fPm0uc+vWLaZMmcL333/P0aNHcckkbuWiRYsoXbo0+/fvZ9CgQbz33nu89tpreHp6cvjwYdq0aUOvXr24desWAPHx8bz44ovUr1+fgwcPsnnzZuLi4ujSpcs91y1WrBj79u1j6tSpfPLJJ2zbtg2AAwcOALBgwQJiYmLMz4UQjyhGhWji338znnVRp47amr4rTM+FeFTS5kqbK54s6W8S1qqFWwUbACJ/3G++Sfhpu3CcnODIEdi19DkANsctwKipwVyD0WCe3PRMmWcoY18GvU7P1rNbSTYk07ZqW5ncJER6BbDNBWl386Xd1Z4wCQkJGqAlJCQ80nVu3tS01cdWa5VmVtIIwPwoPaW0tvrYanO51cdWa+6z3C3KuM9ytygjxJPo9u3b2rFjx7Tbt29n7wIpKZrm7q5pHTpomsFgecxgUPsrV1blclifPn00vV6vFStWzOLx2WefaZqmaUlJSVq9evW0Ll26aLVr19befvtti/NfeOEFrVatWprRaDTv+/DDD7VatWppmqZp58+f1/R6vXbp0iWL81q3bq2NHj1a0zRNW7BggQZoR44cuaduHTt2tHitFi1amJ+npKRoxYoV03r16mXeFxMTowFaeHi4pmma9umnn2pt2rSxuO7Fixc1QDt58mSG19U0TWvcuLH24Ycfmp8D2po1azL5Laa637+FnPrOFvlHPsMcNmGCpoHlw91d01b/93fFnj1qn5WV2v71V/7WVxQYj9TuSpsrba4oFHL0M1y6VLUj16+bd6WMHqu5c07r4LZfM8QnquNLl2pffKF+tLXVtDLlbmiM02sdlnbQ9lzYo208uVEjAK3FDy00XYBOW31stbb59Gat2GfFNALQas2ppRGAFhIV8uh1FqKAKKxtrqZJu5vRdTUte+1uTrS5MtP2Ie3YAaVLQ8uW4F/Ln7MfnOXXXr9St2xdAK7evsqxK8cwakZzjFsPFw/m+6q4t3NfnisxboXICVldIpxm6UZOatWqFUeOHLF4vPvuuwDY2tqyZMkSVq9ezZ07d5g5c+Y95zdr1gxdmuRAzZs35/Tp0xgMBiIiIjAYDFSvXh0HBwfzY8eOHZw9e9Z8jq2tLc8+++wD65q2jF6vp1SpUnikWTJtuit6+fJlAP744w9CQkIsXrtmzZoAFq+f/rXd3NzM1xBC5IKgIBg/HooWhRYtICHBctZFYCBMngxPPQVGIzg7Q7ly+V1r8TiQNlfaXPHkcVOhEIiMNO/Sd/JjBsMJjmmI38tJhNOM607ladgQihWD5GTwbFyM1V1XEnE5As8fPWm/rD0AFxMvEtglEP9a/vhU9eG3Pr9R0q4kx68eB2D1sdUy21YIyPc2F6TdTX9dyL921zrPX7GQc3GBf/6BpCQ1vUVvpad1ldYcfPsgpaaVIjEpkY9DPmbn+Z2cvHoS3+q+BL0ehP8Kfyo7V+adhu/wbqN38Vvux4itI+hYo6NFvB8hRBaZlghntvTXtN9ULocVK1aMqlWrZnp8z549AFy7do1r165RrFixLF/7xo0b6PV6Dh06hF5v+f3g4OBg/rlo0aIWjWFmbGxsLJ7rdDqLfaZrGI1G8+t36NCBKVOm3HMtN9Mf8Jlc13QNIUQOMyUg69ABeveGLl2gZ0/1h/vPP0P79tCnD9y+De+/D3PnQr16KoGMEI9K2lxpc8WTp2VLcHeHSZNUoksrK2jQAP8KBwm82JnhB+fgSTioMVlcXODmTQgOhi5d/Dn5fkf2XApj+7ntTAybyBL/JTxX8Tnz5f9K/IuiNkXhjno+58Acgk4EMbvdbPxr+ef52xWiwMjnNhek3c3suvnR7spM24f09NOg18ONG2pCiymcnA5rFnRcgA4dVjortp3bxoXEC9QoVQP/Ff4Enwo2x7nVJMatEI8ug7v/Fkz703zx5pWzZ88ydOhQvvvuO5o2bUqfPn3u+YLft2+fxfO9e/dSrVo19Ho99evXx2AwcPnyZapWrWrxcHV1zfX6N2jQgKNHj+Lu7n7P6z9Mg2xjY4MhjzKcCvHYSzvrwjSrNiICPD3VjNrdu+HWLQgISJ2V0aBBPlZYPFakzc010uaKAkuvhxkz1Cisn59a2XHjBjRtij9rOJNcgZAJO1m6FCZMAHt7dZrBAD16QI3qeq4d9iLAKwB3Z3em7J5ijnNrWpFa360+jcs1xlqn5pJdvnlZVqQKUYDbXJB21ySv2l0ZtH1ItrZQpoz6uUsXyyR+HPcnsEsgbg6p/3mmh08n7EIYKzqvwL+WP0HHg6g6uyoDfxkIwNAtQ6k6u6o0TEI8rLR3/9Pf8TIa1RLhypVVuVyQlJREbGysxePq1asYDAZ69uyJj48Pffv2ZcGCBfz555/MmDHD4vwLFy4wbNgwTp48ybJly5g9ezaDBw8GoHr16vTo0YPevXsTFBREVFQU+/fvZ/LkyWzcuDFX3k9aAwYM4Nq1a3Tr1o0DBw5w9uxZtmzZQt++fR+qYXJ3d2f79u3Exsby77//5mKNhXgCpJ914e8PZ85ASAgsXQqm74Zq1eDwYfWzDNqKnCJtbq6RNlcUaP7+ljcJHR3Vc0BfxAav5+5SxMZAQICK1LNkSeoCj3Ll1D3GdWtVEu/gU8H4Lfdj1/ldDNsyjOcqPAcaHPz7ID90/IEWFVuQYkxBh473N74vE5vEkyuf21yQdjcr8qrdlUHbhxQUBLGx6ueRI+9N4sdxf84POc/nrT83nxN/J55Z+2Yx7+A8iXErRE7J6O6/6T+kn5/aP326KpcLNm/ejJubm8WjRYsWfPbZZ5w/f55vv/0WUEss5s+fz9ixY/njjz/M5/fu3Zvbt2/TpEkTBgwYwODBg+nfv7/5+IIFC+jduzfDhw+nRo0a+Pn5ceDAASpWrJgr7yetcuXKsXv3bgwGA23atMHDw4MhQ4bg7OyMVfq4SvcxY8YMtm3bRoUKFahfv34u1liIJ0BGsy70evDygm7doEQJtc/FRaXwBhm0FTlH2txcI22uKPDS3iQcMgRMM+GSkjB4t2F4t7/xbRjD2rVqQtN/YS+5dUtF7hkxAjpWV5ObIi5H0HJhS84nnGfXxV0cvXKUwC6B9K7bm609t9KxRkeMGIm7GceobaPy6x0Lkb/yuc0FaXezIq/aXd1/Wc+eGImJiTg5OZGQkICjo+NDnWswqBm1VlZw7hwMHgyzZqljRqP6/xMZCadPAzoDVWdXpaRdSU5fO8315Ovo0FHftT773t6H/wp/Ii9HcnrQaXQ6HX7L/czPJcateBLcuXOHqKgoKleujJ2dXfYvFBSk4jxGR6fuq1xZNWT+BTMelpeXF/Xq1WOW6QvkCXe/fwuP8p0tCgb5DHOI6Y8QD4/U2IImaf8IWbsW6tZVGWESEnL1D3pRuORIuyttbqEnbe7jLVc/w6AgNUvJ1xfu3oXNmwltNYFWIeMIpznNVo8Ef3+uXFGLPhISVESfSZPUeK+XFxiMBgJCA5gYNpFN3TfR5uk2Fn3fFGMKfdf2ZXHEYgD61u3L/A7zsdZLKh5RuDypbS5Iu5tWTrS5MtP2IZjCyXXpop6fPJl6LH0SP72VWgbye+zvNHmqCe5O7mhoHI49jPssd4sYt1Y6K0Z5jiIqPoqA0ABCo0NlOYgQWZV+iXBIiLpzUoAbMiGEeGh6PUybpmZXNG6sEo3Fx9876+LQIVW+USMZsBU5T9pcIZ5MpmSYvr7q5uBbbwEQc/QaAHXaVVBTag0GypSBsWPVad9/r7amCD+mJN4AJYqWuGey0vqT69ketd38fMEfCyg5tSSBRwNz770JUVBJmyuQQduHYmpsXnhBbdMO2sK9Sfz8a6llIGf/PUt0QrS53KXrlyhtX5oy9io4btDxIHqu6QnAxLCJtFrUSuLcCvEw0i4R9vKSgQohxOMnKEjFZdI0FbN24EAVEsHTU82wDQxUf8SbEj80bZq/9RWPL2lzhXjypE2GaWUFPj5QpAhul48AEPnqx6mzl4BBg1QC78uX1elp8yW1rNgSd2d3JoVNMicmg9TkZEbNiJuDG1O9pwJwPfk6rwW+xvLI5XnxToUoWKTNfeLJoO1DMDU2pljQ0dFw507q8YyS+PnX8ufMoDPM9JkJwPBmw3F3cufKrSt4LfKi04pOdF7ZmQqOFQDY1H0T4f3CJc6tEI+x0NBQWS4ihMg605JUDw81szY+Xs20NcWsnTIlddaFadC2SZN8qaoQBY20uULkgPTJMB0cwNubloTh7hzPpDW1MKIzlytSBD7/L8WLTgeVKqVeyrQi1ZSYLPxiOPG34xm4aSAuxVyIuxnHnJfnMPK5kazovAIbKxsA+q7ry7Vb1/LqHQshskna3Zwlg7YPwZTEb948lThT0+DECQgNVZkyhw9Xx9Mn8dNb6RnUZBDuzu6cunaK39/9nTfqvYFRMxJ0IgjHIo5YW1lT2bkybZ5uQ7PyzVjbdS2+1X0ZsXWEhEoQQgghnlTpl6Q2awZOTvD++3DgAHToAB9+qMrduAF//qnOa9YsX6sthBDiMZJRMsyOHdFjZEbpyQRv1uPHWsKv1TDnS/rpJ1VM0+Djjy0vZ1qRGnE5As8fPSkxtQQxN2Kw0lmxustq/GupG5FdnunCxu4bKWJVhDspd/D4xoNLiZfy4A0LIUTBIIO2DyFtEj9T/o+XXoJWraBnT9U43boF69ZlcG6aO4q91/Smf4P+jH5uNAAJSQmEng/Fu4o3Vjp1YU3T8Hnah6j4KGbvny0Dt0IIIcSTKP2S1LTSB9Tft08tB6pUCZ56Kl+qK4QQ+eXatWv06NEDR0dHnJ2d6devHzdu3LjvOXfu3GHAgAGUKlUKBwcHOnXqRFxcnEWZCxcu0L59e+zt7XFxcWHkyJGkpKRYlAkNDaVBgwYUKVKEqlWrsnDhwntea+7cubi7u2NnZ0fTpk3Zv3+/Rd0HDRpEjRo1KFq0KBUrVuSDDz4gISEh+7+QnGSavTRpUuqyU19fAPzPTCXQ4xMirOvjObABjo4qck9EBLzzjiq6ZInqK6dlWpEa0ieEgY0HAnB8wHHzgC2okAn9g/uTZEwC4O8bf+P+pTtz9s/J1bcrhBAFhQzaPiR/fxU27vZt9fzqVbWtVAkmT1Yh5Dp3VisZ7zk33R3FybsnA2CnV1nkvjv8He2XtueH33+g6uyqDPxFNV5DtwyVGLfisaVpWn5XQeQz+TcgxH2kX5KaXtqA+rt3q589PXO/XqLQku/cJ9vj/Pn36NGDo0ePsm3bNoKDg9m5cyf9+/e/7zlDhw5lw4YNrFq1ih07dvD333/jnybJj8FgoH379iQnJ7Nnzx4WLVrEwoULGTdunLlMVFQU7du3p1WrVhw5coQhQ4bw1ltvsWXLFnOZFStWMGzYMMaPH8/hw4epW7cuPj4+XP4v6Ovff//N33//zfTp04mMjGThwoVs3ryZfv365fBvKZvSzl7y81MjsEWLmuMe+P8ZwJnFe835kiZMUKd9+23qJVq1gtWr013WSo+XuxedancC4PjV4+Zjphi3Hi4ezPedD0DpoqVJMaYw6JdBTN09NbferRA55nH+zhUPliOfv/aESUhI0AAtISEh29dISdG0kiU1DTStcmVNCwlR+zRN0wwGTevQQe037bvnfEOKFhIVoo3dPlYjAC0sOkybFT5LK/JpEY0ANALQGs1vpM0/OF8jAG3u/rlah6UdNF2ATlt9bHW26y1EQZKSkqIdO3ZMu3r1an5XReSzq1evaseOHdNSMvjSzInvbJG/5DN8RCEh6g+O8PCMj+/Zo46HhGhamzbq59mz87KGopBITk7Wjh07psXHx+d3VUQ+elzb3GPHjmmAduDAAfO+X375RdPpdNqlS5cyPCc+Pl6zsbHRVq1aZd53/PhxDdDC//vO3bRpk2ZlZaXFxsaay3zzzTeao6OjlpSUpGmapo0aNUp75plnLK79+uuvaz4+PubnTZo00QYMGGB+bjAYtHLlymmTJ0/O9D2tXLlSs7W11e7evZuVX4GmaXnwGa5erWnu7qqtSf9wd9e01au11as1TadTfeLwcE07cULTihRRRXQ6dYn0Ugwpmvssd63D0g6awWiweH7XcFfrsLSDVnlWZe1SwiWt/rz6GgFougCd9supX3LnfQrxiKSvKzQtZ9pc60cf9n3yhIXBtWvqhmNUFJQtq/aHhqqJLj4+sGGDKuflde/5pjuKLSu2ZHHEYqbumcrarmtp5d6Kxt83JtmQzMG/DxJ3I45KTpV4p+E7vN3gbV5Y+ALvBr+LUxEnvNy90FtJ5kBReOn1epydnc0zDOzt7dHpdPlcK5GXNE3j1q1bXL58GWdnZ/SSDVWIe6Vdkrp2rWWIBKNRLfOpXBmaN0+daZs+uL4QgLW1Nfb29ly5cgUbGxus0ofbEI+1x73NDQ8Px9nZmUaNGpn3eXt7Y2Vlxb59+3j11VfvOefQoUPcvXsXb29v876aNWtSsWJFwsPDadasGeHh4Xh4eFDW1OEDfHx8eO+99zh69Cj169cnPDzc4hqmMkOGDAEgOTmZQ4cOMXr0aPNxKysrvL29CU8fMyCNhIQEHB0dsbbOvMuelJREUlKS+XliYmKmZXOEv79qe7p0gfr1oV07+OwzsLGB2rUxdOrCcJcEfH2LWTRZH34In3yiJucOHw4dO6q+tIkplGDnlZ3xW+6Hz9M+RMdHM6bFGPxX+BN8KpjALoGUcyzHjjd28OKiFzkYcxDfZb78/OrPdPPolrvvW4iHJH3dJ1tOtrkyaJsNppWKbdrAL7/A//4Hv/+uQs6ltW5dxoO2Jhk1TsmGZNo+3ZbNZzdzMfEiZezLMClsEj8e+ZHoePUC3j974+7szow2Myxi/ghR2Li6ugKYGzPxZHJ2djb/WxBCpGNaktq5s1qSOnq0CokQGakGbIODVdymP/+EmzfB2TnzUAriiabT6XBzcyMqKorz58/nd3VEPnlc29zY2FhcXFws9llbW1OyZEliY2MzPcfW1hZnZ2eL/WXLljWfExsbazFgazpuOna/MomJidy+fZt///0Xg8GQYZkTJ05kWLerV6/y6aefPjC8w+TJk5lgikWQFwwGGDkyNTmmTqfiIURFQb9+hP1bl+jwYiz70IBVmglGI0fCd9+pfnR0dMaTm0yhBIdvHc6GUxsA6B/cn8rOlQnsEmju99rb2DO25Vj8Vvph0Ax0D+pO3M04hjQbkie/AiGySvq6IifaXBm0zQZT8swWLdSg7Zo1qt1atkz1k5Ytg/794csv1WQX//uMq2bUOG0+uxk3BzcMRgOXb11mXOg4qpSowqbum3h56ctM8JrAwb8P0nllZ4sGTIjCxtSBdHFx4e7du/ldHZEPbGxsHrvZPkLkuI4dISAA5syxjFdbubIasPX3VwO7oP44kf9TIhO2trZUq1aN5OTk/K6KyAeFsc396KOPmDJlyn3LHD9+/L7HC5vExETat29P7dq1CQgIuG/Z0aNHM2zYMItzK1SokHuVMyXHXLYsdRrtK6+oju+GDcT4jIZwqJO4B0hd9eHgoCbkvvmmen7qVMaTm/xr+dOxRkdm75/N0C1DmfvyXN5p+I55hWnQ8SCGbx1unsxkMnTLUGJvxDK59WSZzSgKDOnrPtlyqs2VQdtsMK1U3L1btVVGI/TpA82aqZ83bFDH69SBESPuXf6RXmaN0/Wk61SYVYEbyTc49+853t/0PgDPV3qesc+PxW+5HyO2jqBjjY4SKkEUanq9vtB1IoQQIk8EBam1pGmX85QuDYMGqaU+pu/O0FC1ff75vK6hKGSsrKyws7PL72oIkSXDhw/njTfeuG+ZKlWq4Orqes9stpSUFK5du5bpLCdXV1eSk5OJj4+3mG0bFxdnPsfV1ZX9+/dbnBcXF2c+Ztqa9qUt4+joSNGiRc1/52ZUJn3drl+/Ttu2bSlevDhr1qzBxsbmvu+9SJEiFClS5L5lclRGyTFNg7YbN+L242wAIg/cplk7y1N791aRfs6cgc2b1SSnjOit9AxqMogv933J5jObebfRu0BqYrL21dtTvnh5LiZeZPGri+kf3J/jV48zZfcUYm/E8l2H77DR3//3JkRekr6ueBQFJpjV559/jk6nM8f+ycyqVauoWbMmdnZ2eHh4sGnTprypoMnevehdSjHDMIRNm9QgLcDYsbBrl1q5GBysJryMGaNWioSFPfiypsbJ3dmdzWc2o9PpOBJ3hBvJN5j20jRc7F3MdxRDo0MxGA2M8hxFVHwUAaEB5n1CCCGEeEwEBamwCB4eKlP39etq27y5mnm7bp0ql5ICO3aon198Md+qK4QQOa1MmTLUrFnzvg9bW1uaN29OfHw8hw4dMp/722+/YTQaadq0aYbXbtiwITY2Nmzfvt287+TJk1y4cIHmzZsD0Lx5cyIiIiwGhLdt24ajoyO1a9c2l0l7DVMZ0zVsbW1p2LChRRmj0cj27dvNZUDNkm3Tpg22trasX7++YN5cMS05jYxM3deyJTg5wZUrtPxrGe5EMWlLA3M/2USngzJl1M/r1kEmkSGA1DCCwaeC8Vvux67zuxi2ZRjPVXgONNh9cTdf+HxBi0otiHw/krpl6wKw6I9F+K3w42byzRx800IIkX8KxKDtgQMH+Pbbb3n22WfvW27Pnj1069aNfv368fvvv+Pn54efnx+RaRuN3FakCFy7hv/dFaQdXz55UrVXkZGwYgWULAmmlTqXLmXt0ukbpx3RqgNW37U+9d3qm8tN2DGBWnNr8Xrg6wBMDJtIq0WtqDq7KkHHg3LiXQohhBAiPxkMaoatKW5gs2ZqfWmzZuq5r69azmMwwOHDakDX2Rnq1cvfegshRD6oVasWbdu25e2332b//v3s3r2bgQMH0rVrV8qVKwfApUuXqFmzpnnmrJOTE/369WPYsGGEhIRw6NAh+vbtS/PmzWnWrBkAbdq0oXbt2vTq1Ys//viDLVu2MHbsWAYMGGCe4fruu+9y7tw5Ro0axYkTJ/j6669ZuXIlQ4cONddv2LBhfPfddyxatIjjx4/z3nvvcfPmTfr27QukDtjevHmTH374gcTERGJjY4mNjcVgKEATc9ImxzSNytrYwMsvA6CfPoUZLlMJDi+Fn5/l/UY/P9i7Fxo1UqeOGnX/lzKFEYy4HEHLhS05n3CeXRd3cfTKUYsQgVY6K75p/42qipUNm05vwvtnb/659U/u/A6EECIvafns+vXrWrVq1bRt27ZpL7zwgjZ48OBMy3bp0kVr3769xb6mTZtq77zzTpZfLyEhQQO0hISE7FX43DlNA00rWlQLCVE/vvKK2pYtq2mLF2uau7t6bnq4umra6tVZf4nVx1Zr7rPcNQIwPyrPqqwFHg3Ulv65VCv2WTGNADRdgE4jAG3DiQ1a+MVwrcPSDpouQKetPvYQLyaEEAXYI39ni3wnn2E2mf7ICA/P+PiePep4SIimTZqkfu7YMQ8rKIR43BT27+t//vlH69atm+bg4KA5Ojpqffv21a5fv24+HhUVpQFaSEiIed/t27e1999/XytRooRmb2+vvfrqq1pMTIzFdaOjo7V27dppRYsW1UqXLq0NHz5cu3v3rkWZkJAQrV69epqtra1WpUoVbcGCBffUb/bs2VrFihU1W1tbrUmTJtrevXstzgcyfERFRWX5d5Ann+Hq1Zqm02lahw6qLUpM1LQJE1I7v6tXa6tX39snrlxZnXr8uKbp9Wrf9u0PfrkUQ4o2dvtYjQC0Tac2aSmGlHvK/HvrX40AtE4rOmkOnzloBKDVnFNTOx9/Phd+AUII8eiy+n2t0zRNy5fR4v/06dOHkiVLMnPmTLy8vKhXrx6zZs3KsGzFihUZNmyYRQiF8ePHs3btWv74448Mz0lKSiIpKcn83BScPSEhAUdHx4ev8LVrUKoUAIZbSVStbUvt2ipp819/qSIdOsBHH8HEiXDkCDRsCBs3puYKyQqD0UBodCjdVnejWslqhL4Rio3eBoPRQOUvK3Mn5Q5Xbl0BoHn55izyW4S7szsvLHyBM9fOsKzTMrzcvSTWrRCiUEtMTMTJySn739ki38lnmE3LlkH37mqKkoPDvcevXwdHR5W1+7vvICREJSobMCDv6yqEeCzI93Xhl2efYUbx1k2OH4eaNTEYVJjAmBgVVaHlf3nJwsLURN1t26BuXTh06MH5M0OjQ2m1qBXh/cJpVr6ZZVWOBzFw00BibsSY9+l1egyagaeKP8Xmnpup41In/SWFECJfZfX7Ol/DIyxfvpzDhw8zefLkLJWPjY2lbNmyFvvKli1LbGxspudMnjwZJycn8+ORs2mm+WXqbyQwYwb88gukjSHfuTN8/rkKsD5njorZk3YVY1borfS0rtKaeb7zCP8rnE4rOxF+MZwtZ7ZwMfEiNUrVQIcOO2s7wv8Kp87XdXCd4Ur4X+FcuXUF75+9JVyCEEIIUVhlFDcwLdP+EiVUZlSAl17K/XoJIYQQ/v4qo9iECamBak08PSEoCL0evLygWze1XbcOqlaFVq3UgC3AH3/A4MEPfrmWFVvi7uzOpLBJGLXUYLmm5GRGzYibgxvxH8YT3i+cVpVbAXDp+iVaLmjJrgu7cuZ9CyFEHsu3QduLFy8yePBglixZkqtB1kePHk1CQoL5cfHixUe7oLV16oyXhAT8/dUM2rSX7dPHMrbtihXg45P1pGRppY3l4/mjJ+2XtQfgYuJFArsEcmLACeq41CHZmMy129eoXVoFxJ/gNQEPFw86r+wsA7dCCCFEYZNR3EAToxEmT4bKlVUSsuRkqFgRqlXLl6oKIYR4Aq1bp5JiNmumgtZOm6b263RqFlNQah80o7yapoUhc+eqRSP3kz73S/jFcOJvxzNw00BcirkQdzOOOS/PwcnOiWblm7Gl5xbaPt2WIvoixN+J56WfX2LdiXW583sQQohclG+DtocOHeLy5cs0aNAAa2trrK2t2bFjB1999RXW1tYZBlx3dXUlLi7OYl9cXByuaae5plOkSBEcHR0tHo/MyUltExIAdaNxxgy1q2hRtW3USAVXb9VKrW4cOFDtX5eNtsK/lj9nBp0hpE8IY1uOBWCJ/xL8a/lT3rE8N5Jv8KzLszjYOHDs6jEALiZcZNVrq2hfrT0DNg5gyZ9LCI0OxWAsQIHshRBCCJExvV79cREcTIbZXIKDYfp02LpVlW/bVnWUhRBCiNyWUbLM115Tx/79F9q0MS8zzSyv5vTpUKmSOmXAgAevSE0/manE1BLE3IjBSmfF6i6rzYnJQCUnG/fCOJIMSTQv35w7KXfwX+nP94e/z5VfhxBC5JZ8G7Rt3bo1ERERHDlyxPxo1KgRPXr04MiRI+gzCGzTvHlztm/fbrFv27ZtNG/ePK+qraQbtAV46im1/fBDtV21CipUSO1jzZ+v9n/5pcVNxyzTW+nxcvciwCsAd2d3puyeglEzEnYhjOj4aL7t8C2R70fiYu8CwPe/f0+tubXYe2kvsTdj6bmmJ60WtZKQCUIIIURh0bGjmsW0d69aburoqLaRkamB8n/5RZVt1y5fqyqEEOIJEham4tmOGQNW/w0pVKqkgtRqGjRubF5mmlFRADs7mDJF/RwfD2vWPPhl005mGthYzYo6PuC4xYAtqPww/97+F4BW7q14o+4bGDUjb294m4k7J5LPaX2EECLL8m3Qtnjx4tSpU8fiUaxYMUqVKkWdOipQeO/evRk9erT5nMGDB7N582ZmzJjBiRMnCAgI4ODBgww0TWPNK6ZB2/h48y7TKsaDB8HeXu37+2+oU0c937BBHW/f/uFi26aXfmnIjugdANxMvsmgXwZx5dYVBjUZRDGbYkTFR3H11lUA5vvOJ7xfuIRMEEIIIQqDoCAV/G/8eLiiEo9SurSKH3j6tBqwPXVKxRS0sYHWrfO3vkIIIZ4cMf8l/aqTLsFXx45qe+SIuVxmRQG6dFHju6BywWSFaTJTp9qdADh+9bjF8aDjQVSdXdUcVnDSrkmEng/Fv6Ya2P045GMGbhooK1CFEIVCviYie5ALFy4QE5OaBdLT05OlS5cyf/586tatS2BgIGvXrjUP8uYZZ2e1TTPTNu0qxlu3VF6Qs2fh9ddV27Vhg1rN2LZt9mLbppV2aUjAjgAAvH/2JvJyJIFdApnpM5OSRUvi6pAaNuLz3Z9jZ23H2q5r8a3uy4itI6ShEkIIIQqijIL/hYdD8+Zq5q0p1tKGDWr7wgtQvHi+VVcIIcQTJrNkmX5+amvKNObmdt+8mjodvP22+nnHjtSx3qzIKDmZKTFZHZc6tKjQgkpOldjVdxceLh6sObGGfvX6oUPH1we/puvqriSlJGX9BYUQIh/otCdsbUBiYiJOTk4kJCRkP77t66/DypXQqxe8+aaaZvtfOIehQ2HWLMviVlb35hAZMgRmzszey5sYjAZCo0Pptrob1UpWI/SNUGz0NoRGh9JqUSt29d3F4M2D+T32d4yaEWsra8a2HEsJuxIM3jKYmT4zGdRkEHqre0NRCCFEQZAj39kiX8ln+JAMBjXD1sNDBf9Lu5bUaFQd4shINdu2dWvVy/3yS/jgg/yqsRDiMSHf14Vfnn2GmbVVmqbCJFy8CGXLwqVLGNA/sFn77Te4eRNefBF+/TXrIdpNg7S+1X0Z5TmKnmt6UsGxAs52zmw8vZHALoH41/LHqBnxW+5H5OVIJrWeRO81vblrvEsr91aseX0NTnZOOfwLEkKI+8vq93WBnmlbIAUFpcaP+/lnlWmsalVzoFrTipC5c6FpU/WzTqfGeHMitm1aeis9rau0Zp7vPML/CqfTyk6EXwzn3L/nAPgs7DMOxxzm+w7f82rNV0kxphCwI4DBWwYDMHTLUIlxK4QQQhQkmQX/A/V89Gi1ZCc4OHXZTocOeV5NIYQQT7CMkmXGx8PXX8OdO6pMrVqg12c5r2aRImrwduPGrFcj7QrUlgtbcj7hPLsu7uLolaPmAVtQiclGeY4iKj6Ko5ePMrn1ZBxsHAiJDuGFhS8QeyM2h39BQgiRM2TQ9mGYliuWLauev/++am08PNT+oCBzbNtffoHYWChZUt2I/PxzdcqCBSokXdOmKotmdmPbppU+k2a/9f0AOBJ7hMAugfSt35ceHj3QocPayjr1vJr+1HGpIzFuhRBCiILifsH/0u7ftElNUapXDypXzpOqCSGEEGb+/iopZkSESpJZogQMHJgah33nTpWdO4OipryaEREq6o+TE7z6qjptxAi4e/chqvFfcrKxLccCsKn7Jk4POm2RnCzoeBA91/QEYGLYREZsG4GjnSOORRz5I+4PPH/w5PQ/px/5VyKEEDlNBm2zymBQo6y+vvDGG2rf7dvQrJla5+HrCyNGoMdgvpN4/jx89JEauD18WLVj4eFw9apKBB0dDZ99ljPVS5tJc/Gri3F1cKWhW0P8avphMBoYsW0EvtV9OTPoDGXsywAQdCKIhNsJ1Hetz7vB77L93HaJcyuEEELkp/sF/0u7PyJCbU293Pu5dEnFbmrTBipWBFtbcHWFTp1g377s1TMlRd2JfvlldS1bW9XrbtwYxo5VfwQJIYR4vPn7w7RpamlpgwZquenVq2pU1mhUYQX/W17q769yZ4aEwNKlKq8mqHyb3bvD8uVqQcnJk6mrU7PKtAIVoETREhbh/0whFCo4VgDUoG54v3AaujXketJ1yhYrS1R8FM/9+BwH/z746L8TIYTIQTJom1VplyuWKKH2mRKRpV2uGBaGv7+KWQswahRcu6Z+TkmBHj3UcpBff1X7AgIePUyCiSmTZo9nezD35blsPL0Rv+V+zDs4j+j4aDpU78CgXwZx9dZV3mnwDkX0RQi7GMbh2MNcuXUF75+9JVyCEEIIkZ88PdUg6IABap1o2iU5RiNMnqziBR78r2Pp75/xddKaPVsF3T93Tg3cDh8OLVqohGaenrBixcPV8fx5aNRIxfU/fBheeklNjXrjDbCzU8uLatRQvXMhhBCPL4MBRo5UE5gOHFArUUuVSm2b3N1V+/BfW6bXg5eXCoUQEHBvvs1nnlGnffSRirbwMDJKTGYwGhi+dTjtq7fH2c6Zys6VafN0G5qVb2ZO0F3Eugj1Xetz5dYVvBZ6sfXs1pz4zQghRI6QQdusSrtcsYyaqUpsmtg3puWK/5UzxbadPVsVL1dOPQ8OVitG7O3V8+bNLdqxHJM2ZMLAXwYC0D+4P5GXIwnsEkibqm1INiRTwq6E+ZwapWpQvVR1CZcghBBC5IegIDXYGRurBkNbt4by5WHJEsvgf6++qtaO1qyZ2sO9nyZNIDRUDaJ+/70a+A0MVNOd9Hp47z1IymIG7evXwccH/vhDddTPn1cx/idNUgH7w8LgxAk1OHzjxqP8NoQQQhR0mcVhNw3a3rxpnthkknYB69q1auGqg4PaHjyofr5xAz755OGqorfSM6PNDIJPBeO33I/wi+FsObOF6Pho4m/Hs/H0Rqa3mW6ehWuls2J0i9FcSLjAxFYTaV25NTfv3sR3qS/LIpY92u9FCCFyiAzaZlXa5Yqm2HFRUanHTcsV/ytnim27dKkapF2xQjVECQnw2mswbJgazO3a9Z52LMeYQibM9JkJwNyX53J60Gk61ujI8K3D8a3uS9yION5r9B4AJ/85yaG/D9HArQEDNg5gyZ9LCI0OlZAJQgghRG4zxc03TTtavFj9TREbCz17qhmxkZFqsNX090enTllLse3vDy+8cO/+li1VQtV//00Nt/Ag06ertas9e8LUqWq6VHpVq8L69VC7dtauKYQQonDKLA77Sy9BsWJw+bJlOe6fb9PWNjVswldfwdmzD1ed9Lle2i9rD8DFxIsWiclAzcL99/a/AIT/Fc76rut5/ZnXuWu8S/eg7swMn/lwLy6EELlABm2zyjQKO2mSigcHqvG5cyd1uWLlyqocqQk1w8NV0aQk+OEHtWrw0CEV0/bKFfjgA3V83brcqbbeSs+gJoNwd3Zn85nN6HQ6wi6EER0fzZiWY9Bb6fkr8S+eKv4UHi4e/HP7Hw7FHCL2Ziw91/Sk1aJWEjJBCCGEyE0ZTTvq0QMuXoTt21WcQFdXNYPV21tlOwXo0uXRX9vGRm2tre9fzuTHH9V23LgHl7W1zV6dhBBCFA6ZxWG3s4P27e8tx4Pzbb71ltoaDPDhhw9fpbS5XkzJyZb4L7knMVnV2VXNg7oTwybyzDfP0Ll2Zz5oojrow7YO48NtH6Jp2sNXQgghcogM2maVaRQ2OFi1JEWLqv1r16YuV5w+XZX7j79/6p1Cb2+1gvHOndRL/vRTapD1L7/Mudi291Q93VKRHdE7ALiZfBO/5X4Enwrmq3Zf8b+W/7M4z6mIEzPazMDDxUNCJgghhBC5JbNpR3o9vPgizJmjZtzu2aPu8iYnq9AIHh6P9roXLqgg+25uWbvW+fPw118qZEO1ao/22kIIIQq/tBObjEbLY6YQCdbWKo76fx6Ub/PoUbXV6WD1ati58+GrZcr1EuAVgLuzO1N2TzHHuTUlJqvjUocWFVpQyakSu/ruwsPFgy6ruvB8peeZ3HoyAFP3TOWNdW9w13D34SshhBA5QAZtH4a/v1qWGBEBt2+rfd26pS5XzCAZyP/+p9qx5s1VOARPTxXDFlQIuaVLoWxZqFdP5RxJTs6lqqdZKhKwIwAA75+9zTFuO9boyEfbP6JD9Q582/5bABKSEhi+dTgl7UrSqFwj3g1+l+3ntku4BCGEECInPWjaUdq4+cuXq59ffz1roREyc/cu9OqllgJNmWJx0zlTplj+5ctn/3WFEEI8PtJObPLzU8tM4+Nh7lyVlBJUNu4//zSfcr9x3rQLWE0zbocNu7dclquXbvLSrvO7GLZlGM9VeA402H1xN1/4fMFzFZ8zJyYbuW0kIz1HsqDjAvQ6PT/98RMdl3fkZvLN7FVCCCEegQzaPix/f5XIo3lz9bxtW/juu9TMY+mkDZNw5YpKrDxmDNSvr+Kyh4ZCXBz8/rvqC1WqlHszbk1LRX7t9Stl7MvgWd6TkwNP4l/L3xwy4aMWHxF8Ohh3Z3eGNB0CwKI/F3Hg7wNcuXUF75+9JVyCEEIIkZMeNO3ItL9YMdj6X1br11/P/usZjeoPkp074e231eCtEEIIkR1pJzZ5ekKJEjBwIBw5klpm4kTzjxmN816/bplvc/p0dYqjowot+PPPj1C9NJOXWi5syfmE8+y6uIujV45axLk1JSaLio8i7EIYb9R7g3Vd11HUuii/nPmFF396kSs3r2S/IkIIkQ0yaJsd69alrtvYvFnFPqhaNdPRVn9/GDJE/dy/P5QsqQZpTby94dIl9XO5cioPSW6GSmhdpTXzfOcR/lc4nVZ2IvxiOOf+PQfAxJ0TCT4VzIw2M2hZqSU6dNjb2JvPb+TWiFqla0m4BCGEECKnZHXaUUyMmrH07LNQq1b2XstohDffVEt9evaEefOyfq6rq9qa/mgRQgghQHV4p01TK0AaNFAzbePj4dNP1fE1a1SsgzTF047zOjqm5ttcsUL1l7dvV0m7AUaPhhs3HqF6/01eMsW43dR9E6cHnc40MZlpdWn76u35rc9vlCxakv2X9tNiQQui/o3K8DWEECI3yKDtwzJld3Z3V8+9vdVtQQ+P+462mibizp2rEkK7uqqGSa9X4eRmzFDHp01TeUhGjFDB13NL+sya/db3A+BI7BFzuIThW4fjW92X2OGxdKrVCYCDMQc5fuU4TZ9qyoCNA1jy5xJCo0MlZIIQQgiRXXq9+gMgOBgaN07t7KafdmQKjdC9e/Zex2iEvn1h0SIV3mnhwntTd99PpUrw1FMqQdrp09mrgxBCiMePwQAjR6qO7IED8P774OQEQ4eqpGSgMnCn6eCaFrCGhKj7iCEhMHUqjBoFrVqppm7+fBUSNyZGHXsUpslLACWKlkBvlRoWKKPEZKbVpc3KN2P3m7up6FSRU/+cwvNHT/6I/ePRKiOEEFkkg7YPI21257lz1b7jx1WW57Vr7zvaappEs3mzWgUZG6sGaqdNU8e/+AJKl1YNko8PREWpvCS5KW1mzcWvLsbVwZWGbg3xq+lnDpcwpuUYitkWI9mQjJuDGxUcKxCdEM3eS3uJvRlLzzU9abWolYRMEEKIx5DBYODjjz+mcuXKFC1alKeffppPP/1UMinntKAg1dnVNDh8WC0rLVEiddpRYCA0bQo7VCJR89Sjh2EasP3pJxVa4eefsxbHNr1+6iZv2qWumcqtQP1CCCEKlswSahYrBq+8on7+++97Orh6PXh5qfuI165Bly5qLlTakAn16qmyn3+u8mc+ipYVW+Lu7M6ksElZSkxmWl1as3RNwvuF4+HiQeyNWJ5f+DwhUSGPVhkhhMgCGbR9GGkbo2efVfsuXVItjJWVWreRyWhr2tg9I0eqfZUqqT6Ys7N6fvWqWqk4cKB6vm5drr8jc2bNHs/2YO7Lc9l4eiN+y/3YEa06hjeTb+K33I/gU8HMeXkOk1pPsji/hF0JvmjzhUWjJoQQ4vEwZcoUvvnmG+bMmcPx48eZMmUKU6dOZfbs2fldtceHaQWPqZdqSuDSoIE6PmWKmo60YoUa1G3RQv0B8TBMIRF++glee00t+cnOgC2om9M1aqhrjRmjEpmlFxWlZggfO5a91xBCCFG43C+hZtobjZmE10k7N2rtWjUnysFBbffuhVKlVP5MUz86u7KTmGzE1hEYjAbKFS/Hzr47eaHSCyQmJdJ2SVtWHl35aBUSQogH0GlP2HSZxMREnJycSEhIwNHR8eFOXrZMrdO4fl21IpUrq0Hc0FB44QW139FRre/o1i3DSwQFqdUicXGW+21t1YSUV19Vuc3eeUeFBAoMVH21vBJ0PIjhW4cTHR9t3lfZuTLT20ynY42OVJ1dFQ8XD7yreDN482BzmW51unHm2hmi46NZ1mkZXu5eFktOhBAiOx7pO1s8Ml9fX8qWLcsPP/xg3tepUyeKFi3K4sWLs3QN+Qzvw2BQMfE9PFQvNe3sJKNRDXxGRqpQBE2bqmwsX38N7733cK8TEAATJqi/XQYPVmtN0/PzS53O9CDnz6u4T3/8oeI9tWkD5cvDrVsqaP/u3eo1IiLU+xNCFAryfV345dtnGBqqYhqEh6uR1rTu3FFLSm/ehJkzU5O9ZPF0UNF8+vZVP4eFqfuXj+J+fd60cW7DL4bj+aMnIX1C8HL3Um8n5Q691vQi8FggOnR82fZLBjUd9GgVEkI8cbL6fS0zbR9G+uzOdeuq7eHDlvtN5TLg76+Wdbi6Qv36auvrqxoqa2sVo33qVNWuNW2q7jjmZmzbe+r3X8iEX3v9Shn7MniW9+TkwJP41/I3h0z4qMVH/HruV9yd3RncdDA6dCyLXMaBvw9w5dYVvH/2lnAJQgjxGPD09GT79u2cOnUKgD/++INdu3bRrl27TM9JSkoiMTHR4iEykdlyUrBcwbNkiRqw1evVrNyHFR2ttjduwGefqQHc9I+0Wb4fpFIlFbPwxx/VQO+WLSre0w8/qBvYo0bBqVMyYCuEEE+K+yXUtLVVmcUgtb+czv0m6gJ06pT685Ah977Ew8pKYjKAWqVV0s/Vx1ab87jYWduxvNNyBjQegIbGB5s/4KNfP5LQUUKIXCGDtg8jfWPUtKnav26d6lANH66Ot2x538vY2qqVj7//rmLbvvKKaqBaq7jonD2rQiXs3av6WZ99lptv6l6mIO3zfOcR/lc4nVZ2IvxiOOf+PQfAxJ0TCT4VzIw2M3i+0vMAFLctbj6/WslqPF3iaTqt7MTQLUMlUZkQQhRSH330EV27dqVmzZrY2NhQv359hgwZQo8ePTI9Z/LkyTg5OZkfFSpUyMMaFzIP6qWa9q9dq7Y+PlCmzMO/zsKFKrTC/R5vvPFw17SxUdOefvlF/TGTnAyJiWpw+bPPQD53IYR4cqSNBejnZxnup3FjlcASYPVqNfM2nfRzo9Iz7be3V83MwoU5UOX7JCYDNRu39te1AZhzYI5FHhe9lZ7Z7Wbz2Yuqoz5l9xTeWPcGdw13H71iQgiRhgzaPoz0jdE5NYjJjh0qGG14uFoamIVgtP7+qStD+vdXURW2bEmdaFOnDmzapH4OCFBhFfKafy1/ArsEEnE5As8fPem3XiUfORJ7hMAugXSs0ZHhW4fjW92XyyMv81b9twA4fe00v0X9BsCsvbMkUZkQQhRSK1euZMmSJSxdupTDhw+zaNEipk+fzqJFizI9Z/To0SQkJJgfF00dNXGvrPZS9+5V2+7dc79OQgghRHb4+6vYfhERKpFmiRIqWYtpVaperwZyN26859T7TdQ1GmHyZBWZcPx4tW/MGHWf8FFllJgMUpOTGTUjbg5uxH8Yb05EZsrjotPpGNNyDD++8iN6nZ6f/viJDss6cCP5xqNXTAgh/iODtg/L1Bjt3Qvff5+6/6mnVGvStKlaupiFUdaOHdV29mw1ccbTU82sLVNG9dOmTlXHa9fO+zAJJqalIyF9Qlj86mJcHVxp6NYQv5p+5nAJY1qOwVZvS9zNOEoVLQWARurykI+e+0gSlQkhRCE0cuRI82xbDw8PevXqxdChQ5k8eXKm5xQpUgRHR0eLh8hEVnqp5cqpGbnFiqkbxkIIIURB5e+vwuXodCqh5ty5aqA2PFyNugJ8/vk9p2U0Uff6dbX181P7p09Xk56qV1f5YT799NGrmz4xWfjFcOJvxzNw00BcirkQdzOOOS/PwcnOiWblm92TnAygb/2+rO+2Hnsbe7ac3YLXQi/ibsQ94JWFECJrZNA2Ozp2VJ0nT0/V2QKYOBE++kgtYfT1VdmVHzDKauqrLV0KV66ohqpCBXj7bXU8NFRtjx7NnzAJJnorPV7uXvR4tgdzX57LxtMb8Vvux47oHQDcTL6J33I/NpzagN5Kj291X75++WscbVVHfcruKVRxroLP0z4M2DiAJX8ukZAJQghRCNy6dQurdLFW9Xo9xkcNJicUvV51boOD1fLRtJ1bUy/12WdV2VdfVX97CCGEEAWVwQAjR6r+8IEDKgO3k5PKLmYK9XPwYGp4oDTST9R1dFTbiAi18jQpCfbsUYO3AF9+CSdPPnqV068uLTG1BDE3YrDSWbG6y2qLOLeapuHztA9R8VHM3j/b3J99udrLhPQJobR9aQ7FHMLzR09O/3P60SsnhHjiyaBtdpgSh8yYAV27qn2/qXAAFolDwsLuexnTHcXwcPX85k0VGnfyZBWvB6B4cVi1Sv2cX2ES0krbqAXsCADA+2dvIi9HMsFrApdvXuZ/Lf/He43fY0mnJYCadfvl/i/Zem4rsTdj6bmmp4RMEEKIQqBDhw589tlnbNy4kejoaNasWcMXX3zBq6++mt9VezwEBanOraap5aMDB6rlpJ6easnN8uWq0wtwnzjCQggh4Nq1a/To0QNHR0ecnZ3p168fN27cf6n6nTt3GDBgAKVKlcLBwYFOnToRF2c5S/LChQu0b98ee3t7XFxcGDlyJCkpKRZlQkNDadCgAUWKFKFq1aoszCDo6ty5c3F3d8fOzo6mTZuyf//+DOukaRrt2rVDp9Ox1jTQWVjcL8HmM89ALZXYi4kTMzzd3x/OnIGQEDWxacIEtX/8eBUhqFUr+OADNYn37l018zYn8n+lXV06sPFAAI4POG4xYBt0PIiqs6sy8Bd1fOiWoRb92SZPNWHPm3uoUqIK5/49h+ePnuy/lPFnLIQQWSWDttmRNnGIt7f6+ddfU1sMU+KQDO4gpufvn9oYeXur0LiapkIkVKyoloWY7iY2b56lCby5ztSo/drrV8rYl8GzvCcnB56kWslqANRxqYNRMzL/0HwqO1dmTIsxAOY4Qc+UeYZVr62SkAlCCFHAzZ49m86dO/P+++9Tq1YtRowYwTvvvMOnObEm8UkXFKTCKXl4WCZsadBAHZ8yRd3B/ecfKFs29e8NIYQQGerRowdHjx5l27ZtBAcHs3PnTvr373/fc4YOHcqGDRtYtWoVO3bs4O+//8bfP3WgzmAw0L59e5KTk9mzZw+LFi1i4cKFjBs3zlwmKiqK9u3b06pVK44cOcKQIUN466232LJli7nMihUrGDZsGOPHj+fw4cPUrVsXHx8fLl++fE+dZs2ahU6ny4HfSD54UIJN05LSDRsyHW3V68HLC4oUUZOWTM2kKVyCh4dK6G1tDZs3q0UpOcG0urRT7U4AHL963HzMFOPWw8WD+b7zAZj78tx7+rPVSlVjz5t7aOjWkKu3rtJqUSuCT+VQBYUQTySdpuXEvanCIzExEScnJxISErIfZy80VN3mCw+HevXUrJg7d9QU2WbNVAvSsqW6Rejl9cDLGQxQtapa9Xj0qOqzvfMOnDih+m7JyerY5MnqzmIWL5snTA2Yb3VffJ72YeAvA5nvO58NpzYQfCqYFZ1XMOrXUdQuUxsXexcW/rEQADtrO0Z5jmLr2a2c/fcsyzotw8vd656snUKIJ1uOfGeLfCWfYQZMDb+Hh1oumnY2ktGoQiNERkLDhmqt6JAhMHNmPlVWCPGkeJTv62HDhj30640dO5aSJUs+9HkZOX78OLVr1+bAgQM0atQIgM2bN/Pyyy/z119/Ua5cuXvOSUhIoEyZMixdupTOnTsDcOLECWrVqkV4eDjNmjXjl19+wdfXl7///puyZcsCMG/ePD788EOuXLmCra0tH374IRs3biQyTVLJrl27Eh8fz+bNmwFo2rQpjRs3Zs6cOQAYjUYqVKjAoEGD+Oijj8znHTlyBF9fXw4ePIibmxtr1qzB7yHimed7m5u2n9ys2b3Ht24FHx/18/79KjRQBrLSTO7cCQkJUKWK6kPb2eXMWzAYDVSdXRUPFw/Wdl2Lpmnm50GvB+G/wp/Iy5GcHnQanU6H33I/83NTX/ZG8g06r+zMlrNb0Ov0zPOdx1sN3sqZCgohHgtZ/b6WmbbZkTZxSHBwaisyerRqpNq0ARcXVS4LTGESjh5Vz2vUgFu3YP361BuQN2+qAVuAdety9u08irThEkxLRfoH9yciLoLALoGUKVaG6Pho/tfyf/xz+x/KO5bnRfcXuZNyh092fsLeS3u5cusK3j97S7gEIYQQT4b7LR9NG2bJ1OD36pXnVRRCiIcxa9Ys9u3bx++//56lx+zZs4mPj8+x1w8PD8fZ2dk8YAvg7e2NlZUV+/bty/CcQ4cOcffuXbzTrGSoWbMmFStWJPy/+HXh4eF4eHiYB2wBfHx8SExM5Oh/nbfw8HCLa5jKmK6RnJzMoUOHLMpYWVnh7e1tLgMqjnz37t2ZO3curq6uWXrfSUlJJCYmWjzyVWYJNg0GFU5w9OjU0dW0Sb3TyUozmZAApUvDuXMqPHxOSZ+cbN7BeUTHR9Ohegf8V/gTfCqY6W2mo7fSY6WzYnSL0UTFRxF2ITU0ooOtAxu6baBP3T4YNANvb3ib8SHjecLmywkhcoAM2maHaZR1wwZ47TWV2RlUKssWLeD2bZVZ7CFGV9OHSXB0VI2UjU1q6J8yZdT2yy/zP7ZtWmljAA1pOgQdOuq41MHNwY1z/54DYOLOiQSfCubLtl/yfuP30aGjiL6I+RrPlHmGqiWq0mllJ4ZuGSqJyoQQQjy+TMtH//0Xli1TM5PSxj4yLSu9e1f9EVC/fp5XUQghHtaaNWsICQnJ0sMup6ZF/ic2NhYXFxeLfdbW1pQsWZLY2NhMz7G1tcXZ2dlif9myZc3nxMbGWgzYmo6bjt2vTGJiIrdv3+bq1asYDIYMy6St29ChQ/H09KRjx45ZfNcwefJknJyczI8KFSpk+dxcYeonBwer6bDh4SppS4UK0Lq1it9+544q+9NPkEnM4QdFWTDtN6WXmTRJDfLmlMwmJkVejiSwS6A51q3BaODf2/8CsP3cdov+q43ehgUdFzC25VgAPtn5CW+tf4u7hrs5V1EhxGNPBm2zq2NHNZu2aFEVLR3g1Ck4f15lDvP1fegAtP/7n7ox2by5GqD19FQh7sLC1LjwlSsqtk/TpjB8eP7Htk3LFANoZtuZBHYJJPJKJJ4/etJvfT8AjsQeIbBLIB1rdGTEthH4VvclZngMnWuppUhHrxxle9R2AGbtnSWJyoQQQjy+Tv+XUfrll1Mzq1StmnpHNs0SW/r0gcIa21AI8cRYsGABTk5OWS7/7bff3jOImZGPPvoInU5338eJEycepeoFwvr16/ntt9+YNWvWQ503evRoEhISzI+LFy/mTgUfhr+/Cu0TEaE6tD17qlFYNzdYvBj27FGx/+7cgVGjMryEm5vapm0O0zLt9/dXTeidOyqSUI6+jf8mJs30UeGJ5r48l9ODTpsHbE2Jydovaw/AxLCJ9/RfdTodn774KfPaz8NKZ8WPR37kleWvcD3pes5WVgjx2JJB2+wKC4PLl2HbNhVk1jQd9oMPVGIR09LGsLD7XycN043J8HA1QPvGG6oBmj9f5SEBSEqCvXvVncTPPsvxd5Uj0s68XfzqYlwdXGno1hC/mn6EXQgjOj6aMS3H4GTnRJIhiZJ2KpaVRupykaFNh0qiMiGEEI+foCCVBrtoUbU6JyEhNbNK586qo/vxx6nle/TIv7oKIUQW9enThyJFijy44H+6d+9OsWLFHlhu+PDhHD9+/L6PKlWq4Orqek9Sr5SUFK5du5ZpqAFXV1eSk5PvCdMQFxdnPsfV1ZW4uLh7jpuO3a+Mo6MjRYsWpXTp0uj1+gzLmK7x22+/cfbsWZydnbG2tsba2hqATp064XWfZCZFihTB0dHR4lEg+PvDyZPg6qqStGzfDhcvqjatefPUdu6HHzKciZRZlAVQzydPhsqV4fnnYc4clVJm3bqcS0pmorfSM6jJINyd3dl8ZrM5QZwpr0sdlzq0qNCCSk6V2NV3V6b913cavcO6ruuwt7Fn85nNvLDwBWKuPzhpuRBCyKBtdpnWbNStq7KCvfuuej5vnlrmaBrEjXm4L2N//9S7hP37p4ZJ0OvVihJQs21BZdMsSGES0jLNvO3xbA/mvjyXjac34rfcjx3ROwC4mXwTv+V+bDi1AWu9Nb7VffnW91tK2JUAYOa+mTjbOeNd2ZsBGwew5M8lEjJBCPHEKlmy5EM9SpUqxfnz5/O72iI9g0EtlenQQS0L3b1bzUDSNPj5ZzUjqU8f1bkFFS+pfPn8rbMQQjyCGzduPFLM1TJlylCzZs37PmxtbWnevDnx8fEcOnTIfO5vv/2G0WikadOmGV67YcOG2NjYsN30nQucPHmSCxcu0Lx5cwCaN29ORESExYDwtm3bcHR0pHbt2uYyaa9hKmO6hq2tLQ0bNrQoYzQa2b59u7nMRx99xJ9//smRI0fMD4CZM2eyYMGCh/qdFRh79kBsrMqy/eKLqkNr8uabaqQ1OTnD2LYZRVm4fl1t/fzU/unTVbnatWHoUHXeoEEqUmFOSh/jdtf5XQzbMoznKjwHGuy+uJsvfL7guYrPsbbrWnyr+zJi64h7+q2+1X0J7RNKGfsy/B77O81/aM6xK8dytrJCiMeOTnvComHnWEbNtJkx//5bjbSmXY7i5qYGbENC1KBuNi49ezZ88glUq6b2WVnBSy+pSwI8/bRKUHb+PNjaZv+t5IWg40EM3zqc6Pho877KzpV5o94bjA8dT3i/cJqVb8bm05tpt7SduYwOncUMXHdnd2a0mWFeliKEeLzlexbkAsLKyopZs2Zlaemppmm8//77REZGUqVKlTyo3f3JZ5hG+qzaQUFqEDd9ID5nZxUfafFimWkrhMgzOfV9HRUVxcCBAwkNDeWOKX4pqn3S6XQYcinGW7t27YiLi2PevHncvXuXvn370qhRI5YuXQrApUuXaN26NT/99BNNmjQB4L333mPTpk0sXLgQR0dHBg0aBMCePXsAMBgM1KtXj3LlyjF16lRiY2Pp1asXb731FpMmTTK/3zp16jBgwADefPNNfvvtNz744AM2btyIj48PACtWrKBPnz58++23NGnShFmzZrFy5UpOnDiRaZgInU7HmjVr8PPzy/LvoEC1ucuWqRBA16+Dg8O9x197Ta0u8fJK7eCmk1Ez6e4OffuqPrKbm5qVe/u2mjP1118q5ODEiTn/djLrz05vM92ibxp+MRzPHz0J6ROCl7vXPdc5e+0s7Za04/S10zjbObP29bW84P5CzldYCFGgZfn7WnvCJCQkaICWkJDwaBdKSdE0d3dNa9RI03Q6TevQQdOaNNE00LR+/TStbFn186pV2b508+bqEuHhqcfmz1f70j5cXTVt9epHezt5IcWQov169letzNQymuf3nlpySrK29M+lGgFo15OuawajQeuwtINWeVZlbdLOSRoBmB/lZpTT5u6fq/ku8dUIQBuyeYgWEhWipRhS8vttCSFyUY59ZxdyOp1Oi4uLy3J5BwcH7ezZs7lYo6yTzzCNpUtVw339euq+lBRNCwlRxzZuTG3cixfXtJs3862qQognT059X3t6emrNmzfXli9froWEhGihoaEWj9zyzz//aN26ddMcHBw0R0dHrW/fvtr1NN+3UVFRGqCFhISY992+fVt7//33tRIlSmj29vbaq6++qsXExFhcNzo6WmvXrp1WtGhRrXTp0trw4cO1u3fvWpQJCQnR6tWrp9na2mpVqlTRFixYcE/9Zs+erVWsWFGztbXVmjRpou3du/e+7wfQ1qxZ81C/gwLV5oaE3NuZTevrr9VxW1tN+/ffTC+TtpmcMEH1k9P2hd3dVV84KEg9t7HRtGPHcuMNqf7s2O1jNQLQNp3alGFf9N9b/2oEoA3cODDT/uqVm1c0zx88NQLQbD6x0Rb/sTh3KiyEKLCy+n0tM20fxapV0KULlC0Lq1eriOjvvqsCq9+8CY0aqWC0p09bLgfJgqAg6NRJ/fzrr9CkiYrXM2bMvWUbNIDff1c3Kv0LwQRUUwwg3+q++Dztw8BfBjLfdz4bTm0g+FQwKzqvYNSvo6hTpg61ytRi2p5p5nPtbey5dfeW+bnMvBXi8VagZoyIbJHPMI30M23TCw9XIRIA3noLvvsuT6snhHiy5dT3tYODA4cOHaJGjRo5WDuRFQWqzTUYVJJNDw9Yu1YtGzUYVM6XS5dU2IQDByAlBWbNgsGD73u5oCAV+t3XV/WJ69RR3e9Jk1S4hFWrYOFC9fMLL6jJu7mRxzM0OpRWi1qZV4pa1PF4EAM3DSTmRmqIxMz6q7fv3qbXml6sPr4agImtJjKm5Rhz3FwhxOMtq9/XEtP2UZQpo7Y6nUomYopre/MmdO2qMok9ZDIyE39/mDBB/eztnRrb1t5eNUhpl3zUr6/6fsOHZxjHvcDxr+VPYJdAIi5HMPCXgQD0D+5PRFwEgV0CKVOsDNHx0YxuOZoTV09QyakSL1V+CYBbd29hbaUC88/0mYmHiwedVnbikx2fsCximcS9FUI8to4de3Dcs2nTpj2wjMhHpswqw4fDkiVqENfUcBuN8OmnqT3MN97Ip0oKIcSjady4MRfTho0TT6b0gWknT1bx/Vq1UvHcw8PBzk6V/frrezOOpWEKCe/rq8Z/mzVTEReaNVPPfX1h5Eg19mtvDzt2QG6FAm5ZsSXuzu5MCpuEUUuts2liklEz4ubgRvyH8YT3C880OVlRm6KsfG0lw5sPB2BsyFje3vA2dw13c6fiQohCSWbaPgpTnJ74eDXVdd06lYgsTewmQMW7nTnzoS9vujlZrpwK9TNpEuzaBc89pwZuu3a9t22bMAHGjcvuG8pbBqOBsAthrDuxji/3fUn7au0Z03IMx68ep9/6frSr2o7NZzabZ95WdKqI0Whk18VdAJQqWopez/bi20PfcjslNeK8zL4V4vFRoGaM5LMKFSqwe/duKlasmOHx6dOnM2bMGJKTk/O4Zvcnn2EaQUHqBu+VK6n7KlZU+8LDVcdW06B6dThxInemCAkhRCZy6vv67NmzvPvuu/Ts2ZM6depgY2NjcfzZZ5991KqKTBTINjejtq9SJbVv50745Re1b+tWlcAlA1ldqBISAocOwYgRUKKEakpdXHLhLaVZOTq6xWhqla5F7a9rY9SMxN2MY3WX1ea+qFEz4rfcj8jLkZwedBq91b0rcOfun8sHmz/AqBnxruJN4GuBONk9OI+BEKLwkpm2ecHNTW2PH4dr1+DLL6FuXbWvZEl1xxDU/qCgjK9xH6abk+HhsHmz2lelirpJ2aWLGrAtUULtr1ZNbcePVwO6hYHeSo+Xuxcz284ksEsgkVci8fzRk37r+wFwJPaIxczbKd5T2Nl3J5+2+hSAf27/w6x9s8wzbzd133Tfu5lCCFGYtWjRAm9vb66k7fT8Z8aMGYwZM4affvopH2omssS0rrNZM9WQV6qk9l+4oJbS7NsHpqXEffvKgK0QotC6cuUKZ8+epW/fvjRu3Jh69epRv35981Y8YTp2VOEDPT1Vgs2QEDh7Fj76SN2sdHdX5WbPzvQSMf9FG6hTJ+Pjpv0xMSrKQv368O+/8MEHOfc20kq7ctTzR09KTC1BzI0YrHRWFgO2oBLw+TztQ1R8FLP3z85wVeiAJgNY13UdxWyK8eu5X3nux+c4H38+dyovhChUZKbtozBNhTUF1PHwUIFlK1WC2Fho3FjdUaxTB44ezVZsW1D9vPffh7i41H329rBokerrjRihJuaYWFurScCdOz/a28trppm3lxIvMWLbCBq5NWJdt3WsiFxB96DuXB99HXsbe/yW+/Fn3J8kJCVwPek6Bk01fM9VeI6lnZbi5uDGCwtf4My1MyzrtAwvd68M72gKIQq+AjljJJ+kpKTQoUMH4uLiCA0NNf8+Zs6cyahRo1i0aBHdu3fP51reSz5DshbX78IF9bOVFVy8qJbZCCFEHsqp7+vatWtTq1YtRo0aRdmyZe+J0VnJdNNK5LgC2eY+aJqsafWqTgfnzqUO4j7EJdLOtPXygsOHVU4Yg0Ethn3llRx+T/8x9V9XH1vNnANziP8w3mKGbNDxIIZvHU50fLR53/1WhR6OOYzvUl9ibsTgUsyF9V3X07R809ypvBAiX8lM27yQNk5PdDR06ABJSaqlABVYfcYMNYMmm7FtQcW3vXABXF1TZ9Ru3ar6dSNGqDuJJn37qljuXbpka3JvvjLNvO3xbA/mvjyXjac34rfcj2u3rwGwLGIZfsv9CD4VzJv13yT+TjxBrwfRrmo7AHZf3E3Vr6pSZloZwv8K58qtK3j/7E3V2VVl1q0QotCztrYmKCiIokWL4uvry507d5g1axYjR45kwYIFBXLAVvwnLEz9nTBmjGq8Qf0N4eUFPXqovxUuXVL727WTAVshRKF2/vx5pkyZQtOmTXF3d6dSpUoWD/GEedA0WV9ftdW01JWq6ZhCwk+adG94QKNRLWCpXFmVA5Woe8QI9fN776lohrnB1H/tVFtlED9+9bj5mCmEgoeLB/N95wMw9+W5910V2sCtAfvf3k/dsnW5fPMyXou8WHW0kCyjFULkChm0fVT+/ipmLUD//ipj2PLl6rmVFdjaQq1a6nlMTIaXyApbWzUR5/Rp9fzmTRg2TMW3Tdu3+/lnta1atfAkJsvIg5KVVSupRq+93L2wtrLmqeJPUbt0be4a75KQlIBjEXWnYtzz46hTpg6dVnZi6JahkqhMCFGoFS1alE2bNpGYmEjDhg3NA7Y9e/bM76qJ+3lQhzVthvW33sr9+gghRC568cUX+eOPP/K7GqKgMIUUjIzM+Hja/T/8ALdv31MkfU6z8HC4fl1t/fzU/unTLRe1jh+vJjz9/bfqF+em9MnJDEYDw7cOx7e6L0GvB7Hh1AYqO1fmnYbvsLbrWnyr+zJi64gM+6XlHcsT1jeM9tXacyflDl0CuzApbBJP2AJpIcR/ZNA2J3TsqLZz56oBXFdX9dxoVLNva9dWz00NVjb5+6tEYwA+PnD+vEpMdvSoupNob69m2YIa3I2Ohs8+e6SXzFf+tfw5M+gMIX1CGNJ0CDp01HGpg5uDG05F1LKT9kvaE3wqmJk+M7mVcovG5RpTrWQ1EpMSAfhq/1fsvbQXgFl7Z9FqUSuZeSuEKJTWr1/P+vXr2bFjB++99x5nz57Fz88PJycn87H169fndzVFRh7UYZ2vZuBQogS0b583dRJCiFzSoUMHhg4dSkBAAKtXr7Zoo6SdegJlNE3WYFAxD5YsUSOq7u4qxOC1a7B0aYaX8fdXkQgjIlQoBEdHtY2MhBUrVEqZZcvUZQ0GKFoUFixQURd+/DE131lu0FvpmdFmBsGngvFb7se8g/OIjo+mQ/UO+K/wJ/hUMNPbTEdvpcdKZ8XoFqOJio8i7ELGK3GLFynOuq7r+KCJCsr7v9/+R++1vbmTcifD8kKIx1e+xrT95ptv+Oabb4iOjgbgmWeeYdy4cbRr1y7Tc2bNmsU333zDhQsXKF26NJ07d2by5MnY2dll6TVzJc6PKVZd6dIqXaWvrxq4/e47FUzn/HkVkHbVqkcONGt6KRsbNTC7aZOaddulixrI3bkTbt2Cp59W8d11OtW4+d8bMqfQySgmkL21PYteXURp+9K0WtSK8H7h1HetT5PvmhB5ORIj6g+D8sXL89f1v5jTbg5bzm5hw6kNTPCaQLWS1XAr7kbLii0l7q0QBVCBjM2WT6ysHnyfVafTYShgSyzkMwSSk1VntFw5mDYNXnghdTqQ0agGdS9fhpEjYerU/K2rEOKJlVPf1/drrwpiO/U4KbBtrikZp68vNG8O336r+sgmLi6qM/vzzyqx9++/Z5qQ0xQSPiZGNZ9Xr6rm878hBUCNAc+YofrAw4bBzJnw1FNqwNeUyDtX3mYG/dXKzpWZ3ma6RQzb+NvxlJhagoGNB9Kpdqf79kW/PvA1H/zyAQbNgGcFT9a8vgaXYi659yaEEHkiq9/X+Tpou2HDBvR6PdWqVUPTNBYtWsS0adP4/fffeeaZZ+4pv3TpUt58801+/PFHPD09OXXqFG+88QZdu3bliy++yNJr5lpDtmqVGjktWxZWr1bTXhs0SD3eqBH880+2k5GlFRQEnVTYHObNUzF8KlQAZ2fYuFEl57xxQx2vXVsN4p4588gvWyCYgr3HXI/h9LXTBIQG4Fvdl4ZuDQnYEcCvvX7ly31fsuHUBsrYl6FU0VJcun6J68nXAahZuiY9PHowKWwSt1NSl97cLyC8ECL/FNjOh8iyJ/4zDApSs4jS9iZdXdU6zipVYNw4+PVXtf/UqdTg9UIIkcee+O/rx0CB/gyDguDdd1WibpNKldS+PXtUjAMbG3Wjc+fO1AC1D7ikaSx4zJjU/OCTJqnLBQZC27YqB8ypUyrf2ZIlufgeUf3V2ftnM3TLUOa+PJd3Gr5jMSAbdDyIgZsGEnMjNXTig/qiv577lddWvUb8nXgqOVViXdd11HWtm7tvRAiRqwrFoG1GSpYsybRp0+jXr989xwYOHMjx48fZvn27ed/w4cPZt28fu3btytL1c60hM6W0dHWF2FjLY76+8OGHquExpbR8RKtWqUbHFA4BVPD1zp3VDcr0VZgwQfULHzeZ3c18o94bjA8dT3i/cKqVrMagXwaxLHKZuYyznTPxd+LZ1H0TJYqW4LOdnxF8OpghzYbQsUZHmXkrRAFRoDsfIkue6M8wfW/y7Fk1HShtjPsSJeDff9Xs29DQfKuqEEI80d/Xj4kC/RmaloyWKwfvv6+mvrZsqWYWGY0qOG1oqApW26WLinmQhct5eMDatal5PiH1cpGRas7UwYMqlILRqC7bpUsuvk/UwG3V2VXxcPFgbde1WOlU5UzJyVyKuWCls+L4gOMcv3qcSWGTCD4VTGCXwEwHbk/9cwrfpb6cvnYaext7fvL7yZwATQhR+GT1+7rAxLQ1GAwsX76cmzdv0rx58wzLeHp6cujQIfbv3w/AuXPn2LRpEy+//HJeVjVjpg7YiRNqhLRMmdRjwcFgShLzCMnI0nrtNRWzx2TGDPWy06erxJvpBQSovuPjxhT39tdev1LGvgye5T05OfCkOVFZHZc6lChaghvJNyhfvDwOtg7o0BF/Jx6AsSFj2XVhF5FXVJxBiXsrhCiI1q9fz927d7NcftOmTdzOIJGHyEMGg5ph6+urepPNmkGPHnDxImzfrlbjuLqq5TEAb7+dr9UVQohH8dVXX3HnTtbjbc6bN4/r16/nYo1EgRMWpladzJih2kMvr9SloFZWMHq0GrAFtXL10qUsXW7MGMsB27SXi4pS5Zo2VeUA3nkHLlzIyTd2r/QxbsMvhhN/O56BmwbiUsyFuJtxzHl5Dk52TjQr3+yByckAqpeqzr639vFSlZe4dfcWnVd1JiA0AKNmzN03I4TIV/k+aBsREYGDgwNFihTh3XffZc2aNdQ2Je5Kp3v37nzyySe0aNECGxsbnn76aby8vBhj+gbOQFJSEomJiRaPXGFKMvLNN2qEtFkz1Ukzcfkv7szp0zn2kp07pyYmGz4cevdWA7ZFi6p27uOPU8uWKwcDBqjVJo8bvZWe1lVaM893HuF/hdNpZSeu3b4GwLKIZfgt9yP4VDBvN3ybG8k3WPnaStpVVXGTD8ccZuS2kdy5e4exLccCMPfludQpU4dOKzsxdMtQQqNDM208hRAiL7z66qvEx8dnuXzXrl2JyaGbhCKbTL3Jtm3VtB5TZhS9Hl58EebMUcti/vpLZU/pJLNlhBCF19ChQx9qEHbUqFFcSbtMXjz+TH+X1KmT8XHT/po1VXs5b16OXM5Ubtw4aNwY4uOhVy/1ErnJv5Y/gV0CibgcgeePnpSYWoKYGzFY6axY3WW1xYxaTdPwedqHqPgoZu+fnWnfs0TREmzqsYmhzYYCMGHHBDqt7MT1JLkBIsTjKt/DIyQnJ3PhwgUSEhIIDAzk+++/Z8eOHRkO3IaGhtK1a1cmTpxI06ZNOXPmDIMHD+btt9/m47QjlGkEBAQwwTSymUaOLxkxGFT2r8uXoXVrWLdO3eJr0QJ271bBZaOjVczbHIhrm/Zlq1ZVE3WOHoW5c9XdQ71exfDp2tWyQXJ1VWUeh8RkGckoXIK7kzszfGaQlJJE96DuJHyUQM+gnhyOOUz8nXhu3r1pcY0utbuw/+/9lteQmLdC5IsCvcwvD1lZWdGuXTuKFCmSpfLBwcGcOHGCKlWq5HLNHuyJ/QyHDoVZsyz3pc2Mcv26Sn1tKpvF2PxCCJFbHuX72srKijp16mBtbZ2l8hEREZw8ebJAtFOPkwLd5prCCYaHqwlOYJlV7No1GDgQxo9XM5NcXNSU2Ez+9snocmmFh6uQCGmjE545A/XqqUTen3xiOckpt5hysqw+tpo5B+YQ/2E8TnZO5uMZ9l+z0Pdc8PsC3t34LsmGZGqVrsWa19dQo3SN3HwrQogcVGhj2np7e/P000/z7bff3nOsZcuWNGvWjGnTppn3LV68mP79+3Pjxo0MM5UmJSWRlJRkfp6YmEiFChVypyH75BPVyLRoobI/16mjbumZOm1vvQXff59jcW1N0iYm+/VXaNJETeAxTUB2d0/Nf1K+vJrUs3KlCrHwODI1jOtOrOPLfV/Svlp7xrQcw7+3/6X9sva0qNCC3Rd3E+AVwPjQ8SzrtIzgU8EsjViKhvrvULJoSXrU6cHsA7OZ+/JcNp/ZzIZTG5jgNYFqJavhVtxN4t4KkQcKdOcjD/Xt2/ehz5k2bRqlS5fOhdo8nCfyM0zbMH/3nbqDmj4zil6vAu4BHDsGtWrlW3WFEAIe7fs6o0kyDzJ48GCcnZ0f+jyRuQLd5qYPQrt27b2JOq2tVYKWESNUeITFi1UohSxc7n4xbdPOl/rpJ+jTR5X/7TcVUj4vhEaH0mpRK8L7hdOsvBplNsW49a3uS4fqHegf3N/c93xQjFuA/Zf247/Cn0vXL+FYxJHFry6mQ40OefOGhBCPpNAO2r744otUrFiRhQsX3nOsYcOGeHt7M2XKFPO+ZcuW0a9fP65fv44+C7NXc7UhW7ZMZQerWPH+gXKGDIGZM3P0pU3jxWnZ28OiRepOYr9+ljNura1VdTt3ztFqFDgZ3bm0t7Zn0auLuGu4S/eg7lwffR17G3u8f/ImNDoUwDx4C/Beo/d4vtLzvLnuTW6npMaIlNm3QuS+At35EFnyxH2Gpl6kKYV12t6kqRcZEaEyZJ8+Dc8/Dzt25HethRDiyfu+fgwV+M/QlKCzYUM4dEiFEHr1VVizBn75BRo1Uvtffx2WL1fBaPfufeDlfH1VDFtT02u6RzpkCHTsmJrvzKRPHzV4W64cHD6sFsPmtvTJyTRNMz8Pej0I/xX+RF6O5PSg0+h0OvyW+5mf32+iUOyNWF5b9Rq7LqjE7GNajOGTVp/I5CIhCrhCkYhs9OjR7Ny5k+joaCIiIhg9ejShoaH0+O9uWu/evRk9erS5fIcOHfjmm29Yvnw5UVFRbNu2jY8//pgOHTpkacA215ni2i5dqpZ06HRqTUbjxmr/K6+o7Zdf5nhWsP/9T82o9fRMnWG7davqI/btq5JTm+4+Pv00pKSorJmPY3KytEyJykL6hLDUfykTvCZwO+U2P/3x0z1xb0OiQ9DQWNt1LcObD6eodVEAvjn4Dd1Wd8OlmIpLvKn7JsL7hUvcWyGEEPcyxbL93/9UKITgYDVQGx6u7qL6+qrjphj3776bj5UVQggh8pC/v4rzfuSISsbyyy/Qv79K5r16Nezbp9rJPXvUzc19++DgwfteLjBQ3Qv19FRRhzw9YfNmdXzWLBVCoWpVy37v3Llqgcvff0O3bqpvnNvSJyebd3Ae0fHRdKjeAf8V/gSfCmZ6m+norfRZjnEL4Orgyvbe2xnUZBAAk3ZNwmexD1duSsxoIR4H+TrTtl+/fmzfvp2YmBicnJx49tln+fDDD3nppZcA8PLywt3d3TzrNiUlhc8++4yff/6ZS5cuUaZMGTp06MBnn32W5aU1uXr3MbPZNT//DG+8oQLPlimjjh89mqOxbSH1TmP9+uqO4cWLKlSC0QhxcarvOGECmHKxVa2qGqgzZ3K0GgVeZnFv/Wr5MWvvLPPM2/ZL27P34l6uJ1/HoKU2lK3cW+Fb3ZfZ+2dL3FshclmBnzEiHuiJ+wxNq26uXwcHB9U4p1/+afKAeH1CCJGXnrjv68dQofgMTcFo585VM4vc3CynwpqC0Xp7q9h/b7wBCxbc95Km0Ljr1qn5Ue3bq3un6WfeBgam5nY5flzNrbp5Ez78ED7/PFfftVlGfdHKzpWZ3mY6/rX8sx3jFtRkpLc2vMWtu7d4qvhTLO+8nBYVW+TSOxFCPIpCGx4ht+V6Q5Y2jt38+SqO3Z490K6dupvYv79KV9myZY7HtjW9/Pvvq0FaEzc3FeMWYMAAlaw6rQkTVOjdJ0lGcW/bVm3LwF8GMt93PhtObSD4VLA57u34F8azLHIZp/45Zb5GyaIl6VyrM/MPz5e4t0LkkkLR+RD39cR9hllNtAJqLeekSflWVSGESOuJ+75+DBWKzzD9zU2wbCednNSo64QJKv5fkSIqKcsD4vRnJ8btypUqEgOoCcBduuT4u824rkYDs/fPZuiWocx9eS7vNHwHvZX+kWPcAhy9fJROKztx8p+T6HV6Jr44kVHPjcJKl6+LrIUQ6cigbSbypCHLKGN0WpUqwfnzKoxCt245/vLJyeol7OzUxJ74eNi+Xc3Cbd8ezp1TOU/SepwTkz1IZjNvZ/jMICklie5B3Un4KIGeQT05cOkAicmJ3Lp7y+Iar1R/hTZPt2HktpEWcW8rOVXizfpvyiCuENlUKDof4r6euM/Q1GssV07dRX3qqdQZREajmjkUEqJCKEVHqzj4QghRADxx39ePoULxGaa/uZnZipSAAFi/Xi0hnTIFRo16qMumZ5rAm37e1KhRMG2aygeza5datZoXcivGLcD1pOu8u/FdlkYsBaBt1bYs8ltkDvcnhMh/hSKm7WOrY0e1ffvt1Li2U6eqfU5OqgMHqfHscpitrVptYmr3li+HYcPguefU82PHVLyftLp3V8tFnkRp494OaToEHTrquNTBzcENpyJOALRf0p7gU8G81/g9bt29xbqu6xjbciwli5YEYP2p9Qz8ZSC2elsA1r6+lsmtJ3P55mXGh46ne1B3Wi1qRdXZVQk6/pgHEhZC5Jk7d+7kdxVEeuvWqbWWe/ZAz56qB1mlCkyerKb5hISocq+8IgO2QojHWnJyMidPniQlLwKGisKjZUuVjGXSJNUB7dxZTY8ND4eEBGjRQo2gTpiQOvo6b55lVu0MxMSobZ06GR837TeVM5k8Gdq0gVu3VNOc/nhueZgYt1Y6K0a3GE1UfBRhF8IeeO3iRYqz+NXFfNfhO+ys7dh8ZjPPfvMsW85syYN3JoTISTJomxtatlRTXRcvVlNbw8LU7NuyZVVDlJysGqKFCx/Y+GSXv7+aPWttrXKcnD+v7hzu36/GkZ9/XiUkM0lJUTNtV63KleoUeHorPV7uXsxsO5PALoFEXonE80dP2i9rD8DhmMOsfG0l1UpWA+DFyi8yodUEmpdvThn7MthZ2wGQkJQAwOuBrzN6+2galWtEiwotqORUiV19d0nyMiHEIzMajXz66ac89dRTODg4cO7cOQA+/vhjfvjhh3yu3RPOFFy+WTPVC6xUSe2/cEFlCd27Vy2DgdQQCUII8Zi5desW/fr1w97enmeeeYYLFy4AMGjQID7Pq8ChouDS61WylQ0boHdvNbPo559VKMGePWH3bli0SCUk27RJTXqKikrNLpYJU07wyMiMj5v2m8qlrc6KFVCzporC8Mor6t5rXvCv5U9gl0AiLkcw8Bf1d0H/4P5EXo68JxRCrdK1AFh9bHWW+pE6nY63GrzF/rf280yZZ4i7GUfbJW0ZunkoSSlJufemhBA5SgZtc4NeD2++Cbdvq9gE+/bBjRupt/cOHlSdt6goNaCbS157TYUMMpk2TfUVTTNuz55NDSNk8iTPuDVJO/N2qf9SJnhN4HbKbX764yeu3b4GqCDvfsv92HR6EwObDOROyh2CugTRt15fAJIMqiEMuxDGpeuXOJ9wnjUn1hB5Rf21MGvvLJl5K4TIlokTJ7Jw4UKmTp2Kra2teX+dOnX4/vvv87FmTziDQS3v9PVVwfQ++kg1tCEh6iZu8+aqzJ07qmfYunV+11gIIXLF6NGj+eOPPwgNDcXOdKMK8Pb2ZsWKFflYM1Fg+PurmbS3b6uZRc7OanVqZGTq7NvRo9XS0f+SlPPNN/e9ZNoJvEaj5TGjUe13dYVLl1QohbRzp5ydVaKyUqVUV/3119Wkprxg6nvO9JkJwNyX53J60GmLAdug40HU/ro2AHMOzHmofqRHWQ8OvH2AAY0HADBr3ywaf9eYI7FHcv7NCCFynAza5pZqakYmFy6oBqhECRVY1mTePLXN5fUXnTur9hBg5EhVnbQzblu1soxlm5KiArAHPeHjiKaZt908ujHuhXEZ3gGNiIsgsEugefZt6yqtuXrrKq7FXAHwruKNnbUdUfFRAMwIn8GN5Bv0frY3oBpkDxcPOq3sxCc7PmFZxDKZfSuEeKCffvqJ+fPn06NHD/T61JhmdevW5cSJE/lYsydcWJjqXLZtq6bshIaq/V5e0KOHunN6Td3444MPVCMshBCPobVr1zJnzhxatGiBLs133TPPPMPZs2fzsWaiQDH1lzdtUrleQkJU+ED//wYrTROePD1Ty0VFZXo50wTe4GAVjSg8XOU6Cw+Hpk3V/tjY1MhFVata9nmfflqVsbODjRvVatW8yv6jt9IzqMkg3J3d2Xxms8X/G1NyMqNmxM3BjfgP4wnvF46HiwedV3bO0sBtUZuizHl5Duu7rqeMfRkiLkfQ5LsmfLbzM1KMEr5EiIJMBm1zi2ndxTvvqI5ZgwYq0Gzjxmq/qaOdS3Ft0/rf/9RdR1O7uGGDis7g66saqjt3oGTJ1PLFi8OAASqKg1AeJu5tV4+uAKx5fQ2xw2MZ6TnSfJ2rt67y058/AbDgyAKqlaxGUeuiFnFvn/7qaRnEFUJk6tKlS1StWvWe/Uajkbt37+ZDjQSgYtmCakC7d7+3R/jXX2prbw+9euVPHYUQIg9cuXIFF5d7Ex7dvHnTYjBKPOFM/eUSJVRybi8v1Uc2GNSNzy++UMeffVbNttU0mD//vpf091cTdSMi1Fivo6PaHjwIjRpZDuR6eKgJTmkHbps1UytVrazghx/U4ti8kj7GbfjFcOJvxzNw00BcirkQdzOOOS/PwcnOiWblm7G261p8q/syYuuILPcXO9ToQOT7kfjV9OOu8S5jQ8by3I/PEXk5k5gSQoh8J4O2ucUU13biRBXX9sABlUX6vffU8cuXcz2urYnprqNpfDg0VE0G6tBBNWzBwdCvX2pyssREdReyUiWZcZtWVuPedqyhEtFFXo6keJHi7LqwC4BVr61i0ouTcHd2B+Dg3wf5Yu8X5lAK/2vxPz7x+kSSlwkh7qt27dqEZRBaJzAwkPp5lfJYWAoKglmz1M/ffZdxj3DaNHW8Q4d7YxMJIcRjpFGjRmzcuNH83DRQ+/3339O8efP8qpYoaDKKZxAUpG54tmoFAQFq31tvgenvmx9+gKT7x2P194czZ1KjE7m6qslK+/apQVkHB7Vdu1btHzHCsjvu5wfffqt+/vxzmDIlB9/zA6SNcev5oyclppYg5kYMVjorVndZbREyQdM0fJ72ISo+itn7Z2d54NalmAtBXYJY5LcIxyKO7L+0nwbfNiAgNIBkg8zaEqKg0WlaXk36LxgSExNxcnIiISEBR9MoZW755BMYP15lwJw6VS3x2LtXLZ00GtV282bVonh55W5dUEnGune3jM9TubLqT06fDi4uKqxQYqI6Vry46neuXGkZQkEoBqOBsAthxFyP4fS10wSEBuBb3ZdRnqPouaYnFRwr4GznTPDpYACuj76OvY09fsv9OPT3IW7evUmyIZnbKbctrluueDkcbR25lXKLpf5LmbJ7ChtObWCC1wSqlayGW3E3WlZsid5Kn1G1hHis5Ol3diGxbt06+vTpw+jRo/nkk0+YMGECJ0+e5KeffiI4OJiXTLHfCojH/jM0GFQHs06d/7N33mFRHV0Yf5elCFJVEGwsiBoL9lgQLFFRFCzYYoklJsaoscZYo2As0VijRqMRSxQLJRqxFzAg2CuKigqCCnaKisjunu+P+e4uC4suurALzu957rN757Zz98LMnTNn3sO0+FxdWU/QwIC19T16AOfPAw8fsv3v3mWNL4fD4egZ2qqvo6Ki4OXlhUGDBmHTpk347rvvcP36dURHR+PEiRNo0qSJFq3m5KbEtblCAk9vb6b9PmOGMgFLVBRL6hkdzaKMbGyYzNC2baxTqwEREcz/GxPDHLV5iYlhkbjquuO//Qb89BP7vmIFUzYqLoR+Zsj1EKw6uwppU9JgVcZKsT00LhSTDk9CYlqiokxiLcESzyUqjt33cT/jPkbtG4W9t/YCAOra1sWf3n+iVbVWWrsXDoejHk3rax5pW5Tk1bW1tAQ8PZUjiUIGTGFKZRGTNzHZpEksAHjrVuawffSIqTlY/b89yMxknzw5mXoK0r312OSBe+n3EJUcheMJx/FNo28AKJOXhd0Kw3dNv0N6djqODT6G9d7rAQAGIvbv+DDzIW48u4Gk9CRMOzYNEmsJl1DgcDgKunfvjr179+Lo0aMoW7YsZs2ahbi4OOzdu1fvHLafBIKW7YwZ+cX0Xr1iHVHBYduqFXfYcjicUo+7uzsuXboEqVQKV1dXHD58GHZ2doiJieEOW44qgp7BlStMi4CIOWsfPABCQlhSTyEkVuhDvychWW6E9DGCPG5ehHJ1aWYmTwZmzWLfx41jSofFhdDP7FWnFwAg7mmcYpugcetq54p13kwuQsiVoqnGrUAVyyrY8+Ue7Oi1A7Zmtrj25BrcN7pj+J7hePr6qXZvisPhfBDcaVuUCDo9gYEsG5hIxJy3W7eq7rdiRbHpEPTuzSJnDQ1Z37JCBdZIGRiwhmnxYia+bmPDygAWmdunD4vU5RRMbt3bQN9A+Lf1h525Hf66yLK5q0teVteuLv699S/sy9pDTnL85fMXJrtNhqOVIwAgMikSK8+sVETjjv58NKa1mpZPQoE7cTmcTwsPDw8cOXIEjx8/xuvXrxEVFQVPT09dm/VpIvT0XrxgUzb9/FTF9L77TrmvoM/H4XA4pZzq1atj/fr1OHPmDK5fv46tW7fC1dW1SK/5/PlzDBw4EJaWlrC2tsbw4cPx8uXLdx7z5s0bjB49GuXLl4e5uTl69eqFR48eqeyTlJSErl27wszMDHZ2dpg8eTKkUtXkTREREWjcuDFMTEzg4uKCTZs25bvW6tWrIZFIUKZMGTRv3hxnzpzJt09MTAy++OILlC1bFpaWlmjdujWysrLy7Veq8PVlsgcA6zPnTUhmYABMmwakpbHvUVHAtWsanVrojscWINkqlAv75cXPD5gyhX0fMwZYtUqjy2oNj2oekFhLMD9yPuQkh0wuw6TDk+Bd0xuh/UKx99ZeOFk74bsm332Qxi3A5Ev61euHuNFxGN5oOAAg4FIAaq2qhbXn1vJ+JYejY7jTtigRdHp+/RXYuJGNEEZGsizS7dqxfSwtmeZtXjGdIkSIuBWJmHkACwjKHXH7zTeAra3qcf3784jb95E3+rawycv61euHRR0XIdA3EABQ1qgszI2V2oerz67GgpMLYGJoAomVBJUsKmFuu7lcB5fD+YRITk7GfSGpFYAzZ85g/PjxWPee5BycIkIQjO/ShU1NmT2bRQr5+7NB2/bt2fb69YFmzXRnJ4fD4RQTGRkZapfMzEy8LcJMxwMHDsS1a9dw5MgRhIWF4b///sOIESPeecyECROwd+9eBAUF4cSJE3j48CF8fZXTy2UyGbp27Yq3b98iOjoamzdvxqZNmzBLCMEEkJCQgK5du6Jdu3a4dOkSxo8fj2+++QaHDh1S7LNz505MnDgRs2fPxoULF9CgQQN06tQJjx8/VuwTExODzp07w9PTE2fOnMHZs2cxZswYGBh8Al124XeYODF/QrLt29nAKMCSewPA2rUanVadbK5ATg6beWpryy6lrisuEjGFBkEm4YcfWOBTcZE3Odnac2uRmJYIn5o+8N3pi7BbYVjsuRhiA/EHa9wKlDcrj7+6/YWTX59E/Yr18TzrOb7f9z0a/dkIxxOOF9Edcjic90IaYGNjU6ilXLlylJiYqMmpi5309HQCQOnp6cVzwZAQItZ9I1q3jigjgygykqhWLVZWpgzR0aPse3h48diUy7SKFZXmAUQODkSTJxOJREQ+PkRTpqhuB4h27SpWM0sNIddDSLJcQvCDYjGba0ZB14IoPCGc4AeKSY4hmVxGPoE+ZP+bvaIs8GogwQ9kNMdI5XhhcV7hTK5/uJLjMkeKuhdFPoE+BD+Qf4Q/BV4JpPCEcJLKpLr+CTicQlPsdXYJwN3dnbZs2UJERCkpKWRhYUEtW7akChUqkL+/v46ty0+pfoZCG29qSuTuTpSeThQTwxpQkYho0yYisZjts3u3rq3lcDicd6Kt+lokEpGBgUGBS7Vq1WjWrFkkk8m0ZDnR9evXCQCdPXtWUXbgwAESiUT04MEDtcekpaWRkZERBQUFKcri4uIIAMXExBAR0f79+8nAwIBSU1MV+6xZs4YsLS0pOzubiIh++uknqlu3rsq5+/XrR506dVKsN2vWjEaPHq1Yl8lkVKlSJVqwYIGirHnz5jRz5swPuX0FJbbNDQ9nbeX/f3cKCSGSSPJ3RL/6in1aWRG9eqXRqUNClH3b6GjWHZ8/nzXduU8tkbB91SGXE82YodzXz4+VFRfq+pFOy50o5HpIgdslyyWK7YUlR5ZDK06tIJtfbRTn67a9G117fE2bt8XhfNJoWl9rlIjMwMAAy5cvh5UgdvpuJzBGjRqF2NhYODs7f5RDuSjQiTj7hAnKrNLqKFeOiaoHBrJw1mLk7VvA0ZFJIiQmAs+eAU2asDwqoaFMD/7iRdWRR7EY2LGDSS1wCoemycv2xe/DuBbjsPzUcqRPTceg0EE4++AsUl+lYs+Xe/Ag4wFC4kJwLOFYvmt0cOqAenb18Of5P1WSnDlaOeLrRl/zZGacEkWJS6hRDNjY2ODUqVOoVasWfv/9d+zcuRMnT57E4cOHMXLkSNy9e1fXJqpQap+hkIDM1RUYPBjo25fNqJk2DahTh82iOXOGhfJUrcoa2U8hWorD4ZRYtFVfb9myBTNmzMDQoUPR7P8zDM6cOYPNmzdj5syZePLkCRYvXozJkydj+vTpWrE9ICAAkyZNwgshIhOAVCpFmTJlEBQUhJ49e+Y75vjx42jfvj1evHgBa2trRbmjoyPGjx+PCRMmYNasWfj3339x6dIlxfaEhAQ4OzvjwoULaNSoEVq3bo3GjRtjea7+3saNGzF+/Hikp6fj7du3MDMzQ3BwMHr06KHYZ8iQIUhLS8OePXvw+PFjVKxYEb///ju2b9+OO3fu4LPPPsO8efPg7u6u8e9QYtvcgtrU6dOVbeqFCyxrtjA1dMMG4OuvNTp9aCiLqk1MVJaZmQE//8xkD2JjWTRuWBibWZor2FqFefOAmTPZ9wkTmLRgcTXtMrkMK8+sxIRDE7C6y2p81+Q7iA3ECo1b75re8KnpgxFhI7C6y2ocvH0QYbfCENw3uFDJyXLz7PUz+J/wxx9n/4CMZDAQGWBIgyHwb+uPqlZVtXyHHM6nhcb1tSYeYJFIRI8ePdLYY2xubk537tzReP/iRCejj8LI4bffsmE+NzeiqCg2xAcQmZmxTx1FSOUOBp40SRkU3LQp+960KdHChTzitigoKPp2/n/zad/NfQQ/kPsGdxL5iWj8wfEEP1BmdiYREa06vYrgBxq+ezh9FfqVykho7mXoP0Np7P6xZDrXVKXccZkjj8Tl6D0lNmKkCClbtiwlJCQQEZGPjw/9+uuvRER07949KlOmjA4tU0+pfYaaRgUBRH//rVNTORwORxO0VV9/8cUXtHPnznzlO3fupC+++IKIiLZs2UK1atX6qOvkZt68eVSzZs185ba2tvTHH3+oPWbbtm1kbGycr/zzzz+nn376iYiIvv32W/L09FTZ/urVKwJA+/fvJyKiGjVq0Pz581X22bdvHwGg169f04MHDwgARUdHq+wzefJkatasGRERxcTEEAAqV64cBQQE0IULF2j8+PFkbGxMt27dKvC+37x5Q+np6YolOTm55La5eWevpKWx0Fhh9kpQEPterhzb7/+/naZIpWyCq60t646/fau6XSZjp3dyYvsWxIoVyuZ98OD85ylKpDIpSZZLyCfQh2Rymcp6jiyHfAJ9yGm5E0llUpLJZeS9zZvsf7OnrZe3flR/7/rj69RzR09FP9LkFxMau38sPchQH8XO4XDej6ZtrkbjQnK5HHZ2dhp7jDMzM/UyylZneHiwcNatW9koYWQk0KIFG0k0MQFevwaMjYFNm4pN1zY3vr6qyckAYMQI4NIloGlTpncbFMQibHPz5Zc8OdnHUlDysunHp6Pr9q4AgAspF7Crzy50r9UdABD7OBZykiPgYgAAYEmnJdjScwv29N8DgOngWhhbKK6x6fIm/H7md8hJjopmFVHBrAJmesxUq4MbdC0IEYkRPKEZh6PH1K1bF2vXrkVkZCSOHDmCzp07AwAePnyI8uXL69i6T4jcCci2b2ezZm7eZAlUAgOZODzAIoK+/FJ3dnI4HE4xEx0djUaNGuUrb9SoEWJiYgAA7u7uSEpKeu+5pk6dCpFI9M7lxo0bWr+H4kb+f8HV7777DsOGDUOjRo2wbNky1KpVCwEBAQUet2DBAlhZWSmWqlVLcPSjry/Tg8/KYsnGrK1ZUs/YWBb+2rs3m83y/DnrmJ45wzqsGiIWs+XJE9bnNTJS3S7kO0tIYN31ghg7Fti8mZ1ryxagRw/g1asPueHCUxiN2903duN8ynmkvkrFoH8GfVTek9q2tRHaLxQxw2PQxrENsmXZ+P3M73Be4YyxB8biQcaDIrhbDocDFCIRWVhYmKIx4RQSsZhN3cjKYlkvFy4EqlcHfHxYpmmAKaO/r4UoQoTkZAJ9+wJSKXPetmwJnDsHNGoErFmj3EcuZ/vNnq0TX3OpoaDkZYITN0uahS2Xt8BQZAhHK0dMPjwZ3bd3x4XUCwCAuKdxkJMcv538DfZl7fEq5xUOf3UYIX1DAAAmYhMYiAyQLcvGo9eP8PT1U8yNnAtTQ1M4WjrC3twekUMjUcG0AvoG90W7ze0Ujtzqv1fHnBNzuBOXw9EjFi5ciD///BNt27ZF//790aBBAwDAv//+q5iGyikG8iYga9cOqFWLdSZ79AD++YdtHzyYjYpyOBzOJ0LVqlWxYcOGfOUbNmxQOBWfPXsGGxub955r0qRJiIuLe+fi7OwMe3t7laReAJNHeP78Oezt7dWe297eHm/fvkVaWppK+aNHjxTH2Nvb49GjR/m2C9vetY+lpSVMTU1RoUIFiMVitfsI53BwcAAA1KlTR2Wf2rVrv9O5PW3aNKSnpyuW5OTkAvctEdSowT7372cDoOHhwI0bbGA0d0Kyzz9nn3/+WajTC+Ot9erl3yaTKU9/7Ni7+7eDBwO7dwOmpszU9u2ZM7g48K3ti+C+wbj6+CrGHBgDABgRNgKxj2MVUgiCZEJD+4YAgIBuAYgZHgNXO1f03tX7gxNWt6jSAuFDwnH0q6NoVbUVsmXZWHlmJZxWOOHrPV8j7kmctm6Tw+EIaBq6KxaLqVKlSjR9+nSKj4//6FBgXaGzaZqBgWwORYUKqlMmK1dWXR8/vnjtysOuXUSGhqomGRoyiYScHDZlxM6OyNpadZ+qVQsWbud8HAVJKMw9MZcclzmS+wZ38t7mrSKhkP4mXSWZWURiBB2MP0gj945UK6EgSCdYzLcg20W29OL1C1oQuSCfpIJkuYR2xe6i8IRwLqvAKRZK7dT6j0QqldLz589VyhISEgolZVRclMpn+L4EZEOHsu1iMdHr17q2lsPhcDRCW/X1nj17yNjYmOrXr0/Dhw+n4cOHU4MGDcjExIT27t1LRER//PEHTZgwQRtmE5EyEdm5c+cUZYcOHdIoEVlwcLCi7MaNG2oTkeVuX//880+ytLSkN2/eEBFLRFavXj2Vc/fv3z9fIrIxY8Yo1mUyGVWuXFmRiEwul1OlSpXyJSJr2LAhTZs2TePfocS3uZpKDw0Zwj7NzYkyMz/49ALqLvOuxGQC0dFKtQYXF6LidJNIZVJaFrOM4AdafWa1ok+WWzIh6l4UwQ8UnhBORKRIeC1IKHwMcrmcjt45Sq03tlbpL/oE+tCxu8dIXpyZ2jicEoim9bXGTtukpCTy9/cnZ2dnMjAwoNatW9OWLVvodQnrjOisIRNaCAcHJqKzdSsrk0qJ+vRRtg4ODu8W0SkGgoKU5vTtq9S49fFh30Ui1kcVHLpCv5Tr3BYdUplU4Sj1j/D/aB1cvwg/WnN2DXXd1lWtE9dygSXBD9RobSNqtq4ZOS5zpKh7UdT0z6b59uXauJyipMR3Pjil7xlKpawn5+PDGszcKanT0ohatFA2ot9/r2trORwOR2O0WV/fvXuXpkyZQj179qSePXvS1KlTFXrsRUXnzp2pUaNGdPr0aYqKiqIaNWpQ//79Fdvv379PtWrVotOnTyvKRo4cSdWqVaPjx4/TuXPnqGXLltSyZUvFdqlUSvXq1SNPT0+6dOkSHTx4kGxtbVUcqXfv3iUzMzOaPHkyxcXF0erVq0ksFtPBgwcV++zYsYNMTExo06ZNdP36dRoxYgRZW1tTamqqYp9ly5aRpaUlBQUFUXx8PM2cOZPKlClDt2/f1vg3KPFtbkFtbEwMGyB1d2f5YEQiInt71tauX/9Bp5fJWFlICDudtzc7vaMjSz8jjMO+z3EbF6d0+FaokN8hXJTk1bglIgpPCCf4gU4mncznoJXKpIr+4LKYZVrrt8Ukx1DPHT1J5CdS9BHr/VGP1p1bRy+zX2rlGhxOaUPrTtvcHD9+nAYPHkxly5YlKysr+u677+jMmTMfZGhxo7OGTColqliR1eYnT6qWr16tOqwXHl68tqlBXcStRMKibL292WJnx3zMufcxMGDtK6do0cSJG3QtSNFoxyTHkEwuo8ZrGxP8QGlZaUREFJ0UrTjmmz3fkNsGN7VOXPiBmq9vTiI/EdkusiWHxQ6UlpWmNhqXO3E52qTEdz60RKNGjRSRtQ0bNqRGjRoVuOgbpe4ZahoFZGNTvNlJOBwO5yMp6fX1s2fPqH///mRubk6WlpY0bNgwyswVhZmQkEAAKDxXXysrK4tGjRpFNjY2ZGZmRj179qSUlBSV8yYmJpKXlxeZmppShQoVaNKkSZSTk6OyT3h4ODVs2JCMjY3J2dmZNm7cmM++lStXUrVq1cjY2JiaNWtGp06dyrfPggULqEqVKmRmZkYtW7akyMjIQv0GJf0ZElHhE5I1bVro0wu+4MhI5qR1d2f929xOWk0TkxERpaQQNW7MzClTpnhnoIZcDyGRn4h8An0oOimaNlzYQPADeW31IpGfiEKuhyj2y9tnlCyXKLZrgxtPbtCosFFUdl5ZlWCgUWGj6HLqZa1dh8MpDWhaX4uIiD5UWiEzMxM7duzApk2bcOrUKdSrVw+XL1/+0NMVCxkZGbCyskJ6ejosLS2L9+ITJgDLlwNeXsDPPwN37wKTJyvFdQTGjweWLSte29QQHMy0bgFg0iSgbVsmw+vuzrThRSKgVSv2vXFj4MIF5bHbt/O8K8WJTC5DZFIkUjJTEP88Hn4RfvCu6Y2f3H7CoH8GoaplVViXsUZYfBgAIGZ4DJpVboYeO3rg1P1TePL6CTKnZeLqo6twC3ADAAyoNwDXn17HpdRLaq/pVsUNMfdj4FndE6/evkJyRjJGNB6BuZFzkSXNUuwnsZZgUYdFsC1ri5TMFDhYOMCjmgfEBmK15+VwcqPTOluP8Pf3x+TJk2FmZgZ/f/937jt79uxiskozSt0z3L6dadhmZgLm5qxMJmOa9LduAePGAW/eAGPGACtX6tZWDofDKQTarK/T0tJw5swZPH78OF9elMGDB3/UuTkFU2ra3DlzWOKU3Dg5AYsXs4RlMTEsSZlYzNrgCxdYAhYNCQ1l/dvERPWnFxAuEx7O+sLv4uVL1v/dt4/1k3/7DZg4kX0vakLjQjHp8CQkpiUqyhzMHbCqyyoVjVvvmt7wqemDEWEjsLrLahy8fRBht8IUWrjaIu1NGjZe3IjVZ1fjzos7ivJmlZthaIOh+LLel7Axfb+mNYdTmtG0vv4opy0A3L17FwEBAVizZg0yMjKQk5PzMacrcnTakEVEsEQl9vZAaqqy3MEB+OorYNEiti4SMY+pr/Yqzg8lKIj1TaVSZVm1aqw/KuS7OXOGibDfu6d67OrVwKhRxWcrR4m6htvM0AzTPaZj/YX1Cifuvvh98Gvrh9kRs3Hy65P4NepXFSeuubE5DsQfQJfALgCAqpZVkZyRP8FBdZvqiga5o3NHZOVkITkjGdt8t2H8wfE4l3JOZX9HK0d83ehr1ChXgztxOe+k1HQ+tIRMJsPJkydRv359WFtbF8s1Hzx4gClTpuDAgQN4/fo1XFxcsHHjRjRt2lSj40vdMzx2DOjQgWW4bt0a8PBgnUaApZQWHLXHjgFffKE7OzkcDqeQaKu+3rt3LwYOHIiXL1/C0tISolxeK5FIhOfPn2vDXI4aSk2bKwyQ7t/PEnk7OKi2t5mZgKUl0KIFcOoUMHKkasZsDZDJAD8/YO5cdhlPT+XpBYTLBAYC/fu//5xSKYu/Wr2arY8cyV4LiiMfqRDE8yDjAX488iOaOjTFnv57QERwWekCVztXhPYLhe9OX8Q+jkX8D/EQiUTovr07zj08h8Wei1HZsrJW+2VykuN4wnGsO78O/9z4B1I5cyqYiE3Q/bPuGOg6EJ1dOsNYbKyV63E4JYkiddpmZWUhKCgIAQEBiIyMhJOTE4YNG4ahQ4eicuXKH2V4UaPThkwmA1xcgLp1gfPngUqV2BCcuzuL0PHxAbKygOrVAbmcZabO23LogOBgoG9fZnp8PDN58mTViFtvb2b+iBGsURKcvD/9BCxcqFv7P1XyRt9uvLQxnxN3ZuuZ+L7p96jzRx3ISY5Hrx7Bv60/ZkfMLjAa93jCcXTf0R0AUM2yGpIy8me0rWxRGQ8yH8Cnpg/CboXBrqwdDEQGiBsdhzXn1mDOiTkq0bjcicspiFLT+dAiZcqUQVxcHJycnIr8Wi9evECjRo3Qrl07fP/997C1tUV8fDyqV6+O6tWra3SOUvUMQ0NZ2EzuUUqJBFiyBKhTh6WjlslY5zI5WS/acA6Hw9EUbdXXNWvWRJcuXTB//nyYmZlp0ULO+yg1ba4Q7BQTwxyzgHJWS0oK8Pw5m9GyeDHw44+AhQUrL1v2oy+Tm6go5iueORNo317Vb1wQRGxy7aRJ7HunTsCuXcz5W1zkjqztVL0TxhwYg3Xe67D31l6VyNrQuFCM2T8GKS+VM38l1hIs8Vyi1chbAHj86jG2XdmGjZc24urjq4pymzI26F2nN/rV7Yc2kjYwNCgGDzeHowcUidP21KlTCAgIwK5du/D27Vv4+vpi+PDhaNeunVaMLg503pCFhgK9erHv69YBZmb5JRJEIlbDazIPo5hQ108VIm6bN2fbW7YELl1SjcoF2Pbjx9mtcnTH+5y4ANC0UlMs81ymIqnwvmjcl29fYsaxGQi4FIDKFpXxMPMhCKrViqmhKbKkWWjj2Ab/3fuPSypwNEbndbYe0rRpUyxcuBDt27cv8mtNnToVJ0+eRGRk5Aefo9Q8w9BQoHdvNkrp5gZMn840goiAkyeBGjXYyCYAhIToxWwZDofDKQzaqq/Lli2Lq1evwtnZWYvWcTSh1LS5QrCTqyuwezdb8uoZGBoC27ax9vjOHWDDBuDrrz/qMgYGym3BwcDgwSymSkAYp9Wkid+zhwULv37NxnTDwgBHx0KZ91Gom3npZO2ExZ6LVSQTOrt0xoHbBxDQLQC1bWtjfuT8IpFMECAiXEy9iG1XtmF77HYVh7GtmS18a/uid53eaOPYBkZiI61fn8PRF7TutK1Tpw5u3ryJRo0aYfjw4RgwYACsrKy0ZnBxoRcNmaBtmxsHBxaSOmuWsjHSE21bAZkMmDdPKS80YQIzb906tpw7BzRtypa1a1WPNTQEli4Ffvih+O3mqCe3E9fBwgFPXz3F5KOTPyga9/N1n+NC6gWkTUkDAGy4sAGTjkwCAJQRl8Eb2Zt8188tteBezR1yuRwPMh9wSQWOCnpRZ+sZBw8exLRp0/DLL7+gSZMmKJsnqkSbv1OdOnXQqVMn3L9/HydOnEDlypUxatQofPvttwUek52djezsbMV6RkYGqlatWrKfobpenTpBPABYtQoYPVoXVnI4HM5Hoa0219fXF19++SX69u2rRes4mlCq3puEwdImTdgs1c6dgZ49gX/+AQ4cYJ3O8+eBgQOBrVtZqGxMzAdfxtsbmDaNOVhXrWK+YABYsIAF9cbGAvPnM+erpkqG58+z2agpKUDFisC//yolBosDmVyGlWdWYsKhCVjdZTW+a/IdxAZiyOQyhWTClFZT4L7RHeFDwtFW0hY5shy02dQGt5/fxvZe29FW0rbI+lwyuQwn7p3A9qvb8c+Nf/As65lim00ZG/jU8kHPz3qio3NHlDUuXBQ1h6PvaN1pO3bsWAwfPhwNGjTQmpG6QC8aMmEeRvnybLhNkEiIjmbROatWsf0qVgQePNC76ZXqdG4NDYGGDZkcQt++LKp2926mDSQEDgNMPmHt2uIRZOcUng+Nxn1XgrMXU15g943dGLZnGACgimUV3M+4n+/ahgaGkMqlaFapGc4+PItypuVgLDYuUFKBR+N+GuhFna1nGOQKA8mtE0hEEIlEkMlkWrtWmTJlAAATJ05Enz59cPbsWYwbNw5r167FkCFD1B7j5+enNllaiX6GBc2flMmAw4eZ0F16OstAsn27zszkcDicj0Fbbe6GDRswZ84cDBs2DK6urjAyUo2W69at28eayimAUvfepK7jKWQM69GDLZcvsz6zTMa+169f6MuoG4c1MwM2b2YOXQG5nF0yNlZzJcPkZOYQvnIFKFOG+ZeFibfFQW4H7e4vd8NAZICIxAi029xOMYtS0Ljdc3NPvujcopJLyEuOLAcRiRHYdW0X9tzcgyevnyi2lTEsgw7OHeBT0wdda3RFZUv9luTkcDSh2BKRlTT0oiGTyYDKlYFHj9iUytRU9dE6gF5JJOQmOBjo04d979uX6fSsXcuib7OyWPsaH68coczN55+zrJq2tsVrM6fwaBqNW9gEZ9nSbPx8/GesOb8GdSrUQeqrVDzPUp+UwsnaCQlpCfi80ucQQYRHrx7xaNxPCL2os/WMEydOvHN7mzZttHYtY2NjNG3aFNHR0YqysWPH4uzZs4gpIJqlVEbavishyuTJrPMIAAEBwLBhOjWVw+FwPhRttbm5Bxfzou3BRY4qpe69SRg0Xb0asLHJn5AsJoZJFrVuDfz3HwuJFRKCFhJBMvfYMZacLCqKqSDl3WftWnaZZcvYLFJNHLeZmWxcd/9+tr5oEZPiLa5Aptwat9PcpyHuaRyG/zscXi5eOHj7IIL7BgOAYp9xzcehw98d4N/WH2cfnEVYfBjGtxiP7rW6F0v/SiaXITo5GqFxodh9c3e+IKJG9o3QtUZXdKnRBc0qN+P9PU6JRKtO28aNG+PYsWOwsbHR6OLu7u7YuXOnXiYl05uGTJBIaNSICcHmne4hMGqUMv2knqFu4BNgU0hcXJhTN3fEbdOmTEIBYOVbt7Jb5pQstJXgLLekgqWJJYKvB6NvMJtGV79ifdx6ekutrAIA1LOth2tPrsHG1AbGYmPcGH2DJzgrpehNnf2J4ujoiI4dO+Kvv/5SlK1ZswZz587FgwcPNDpHqXiGc+YotYEEJBLg++/ZXEq5nJXp6UArh8PhaEKpqK8/cUrdMxQGTTMzAXNzVpY7IZmVFdC1KzB1KvDrr4C1NfDwIWBqqtVLAuqjcQujcSuVMheAMKl2xAj23aiYZFvVadw6mDtgVZdV6F6ru0o07un7p+EW4Ab/tv75+nrFFXkrQES49uQa/r35L/69+S/OPDijkj+lnGk5eFb3hJeLFzpV74SK5hWLxS4O52PRqtPWwMAAx48fR7ly5TS6uJubG65cuaKX4vN605AJo4ZiMWt4BJyc2NDbpElAUhJrKdLS9E4iQSB3xG2XLmz08NAhNm1EiLitXJkNgFasyIKLc9OgARvNLF++2E3naImikFTInJaJi6kX0XpjawBA1xpdkZCWgOtPrqu1oZplNSRlJOHzSp9DLBIj5WUKT3BWStCbOvsTZcCAAUhOTlZJRDZhwgScPn1aJfr2XZT4ZygkEDU1Zbp6+/YB16+zMJx9+9g+lSsDxsaaz5XkcDgcPaTE19ec0vcM88oTFaQn7+cHbNrEyrdsAb76SmuXBFR1b318mMN19Wrg4MHCadwCwO+/M+etXM4Cm4KCgOJ6VEK/7UHGA/x45Ec0dWiKPf334L97/6Hd5nYq/bLT90/jyesn8K7pDZ+aPhgRNgKru6zGwdsHizRR2ft4/OoxDt4+iH3x+3Do9iGkZ6erbG/s0BidqndCp+qd4FbVjScz4+gtWnfaikQiaKqkIBKJEB8fz5227yK3RMLKlcxraWfHtqWmsqieW7fY+r//stZBT3lXxO1PPwHNm7MI21atmBqEjQ3w4oVyPwMDNjg6b17x2s0pGopKUsHc2BxH7hyB51ZPAEDLKi1x4+kNvHjzogBLgCYOTWBoYIjUl6kFSioU92gxp3DoTZ39iXL27Fm4ubnB398fffv2xZkzZ/Dtt99i3bp1GDhwoEbnKNHPMHcCssGDmR6QkKlk1y5lUlGRqHA9Ng6Hw9FDtFlfv3r1CidOnEBSUhLevn2rsm3s2LEfdW5OwZToNlcdBbXD06cDdeqwKNsLF1i0UL9+wI4dLFdMrsHmj7nk7t0sN4uwHhrKmnpB01YkKrzGLQDs3cvkEl6/Zufdtw+oWvWDTf4gcksmNHFoAr8Tfjj61VGsOL0Ce2/thV1ZOzSv3By7v9yNV29fwfJXSwT6BqJfvX7ovr07zj08h8Wei1HZsrLOgmCkcilO3T+F/fH7cfD2QVxMvaiy3dzYHF84faFw4lYvV73YbeRwCkKrTtt79+4V2oAqVapArIfRJnrVkAkSCV5eTJvnzz+B3L+1EIXbvTtrMfQYIeLW2Jj5nu/fB44eZbcXFsaCkwBl4s+DB5lDd9kyICeHbWvdmuVhq1BBZ7fBKSK0JamQNxr3SuoVtNrIxKZ8avrg1rNbuPnsplobWlZuiVMPTsHWzBZiAzHiRsch7mkc5v03r9h1mjiao1d19idKWFgYpk2bhvj4eDg5OWHixIn49ttvNT6+RD9DTSN8/P2BWbN0YSGHw+FoDW3V1xcvXkSXLl3w+vVrvHr1CuXKlcPTp09hZmYGOzs73L17V4tWc3JTotvcgsg74yUsjM14WbCAfd+1i0XXXr7MOqFyOdteu/ZHXVKIrO3UiWnYrlvHnK25I2s/VOMWYP1ib28Wr1WpEpuxWtw539VJJjhZO2Fow6GKPliLKi0QkxwDtwA3hA8Jx/Os5xizfwxSXqYojtGXIJjUl6k4cucIDt45iMN3DuPp66cq251tnBUO3C+cvoCFiYWOLOVweCKyAtGrhkzoDFpbMwkEAUdHYORI1vjExTGhm/v3lZG4ekpoKDP7iTLRo0ISQYiybdqUOXLd3QF7e9ZI5aZMGWDNGmDIkOITZucUPx8qqaBJNO6CyAWYfnw6WldrjWdZz3DtyTWV8xrAAHLI8XXDr1GrQi2sObdG5dpcB1e/0Ks6m/NBlOhnqE7Y7tkzoG5d1ri1b880fgIDgf79dWsrh8PhfCTaqq/btm2LmjVrYu3atbCyssLly5dhZGSEQYMGYdy4cfDlsxKKjBLd5r4LddryTk4sEaivrzIhmZsbEB0NTJzIxGY/AnXjtLkv+bEatwCL1+rShfmYLSxYAFPHjh9ldqGRyWWISIxA/5D+qFGuBiKGRiD4ejAGhA5A5rRMmBmZoceOHoh9HItFHRahb3BfdHbpjAO3DyCgWwBq29bWyyAYOclxMeUiDt05hMN3DuNk8klI5cqpwYYGhnCr6obO1Tujk0snNLRvCANRwUkUORxtw522BaBXDZlMBlSvDjx+DDRsCIwerRSAjYpiKSUv/j/Ef9w45TRMPUYmA375hS1CXhZA6bw9eZLdyrlzbGSxSRMWoJQXiYRNE6lTp9hM5+gQTSUVCpvgzKqMFcJuhcFnO5MXsS9rj9RXqiMFxmJjNKzYEGcensFXrl8hOC44nw6uPowcf6roVZ2tR0ilUkRERODOnTsYMGAALCws8PDhQ1haWsI8d9YMPaBEP8O8kbZEbPplUBDg7MxmyHTsyBOQcTicUoG26mtra2ucPn0atWrVgrW1NWJiYlC7dm2cPn0aQ4YMwY0bN7RoNSc3JbrNfRfCIOr+/SzYycGBzVQFmBTC3bvA8OHMWbt0KZMefPAAMDH5qMvKZEzJcMIEpmH73XcsklabGrdpaSw5d0QEYGgIbNjAlCCKm9xyCZ2qd8KYA2Owznsd9t7ai7BbYdjZeyd+OvoTXO1cMaXVFLhvdFdE3uaN1tXH/lNmdiYiEiNw6M4hHLpzCLef31bZblfWDp2qd4KXixc8q3uivBlPusMpWrjTtgD0riETRg3d3VkCsrt3gcmTWTbM3IjFbBivShWdmFlYgoOZ5JCLC9P3mTaNzWARpBGaNgWmTGH7ELHI2s2b85/H15cFHJctW/z3wNEtRZHgLGNqBg7ePoi+wX1hIjaBVC6FjGQq57QpYwMrEyu8lb/Fzl47sSh6Efbe2gv/tv48+lYH6F2drQfcu3cPnTt3RlJSErKzs3Hr1i04Oztj3LhxyM7Oxtq1a3Vtogol+hnmFbZbtw74/nvWq/rvP9awFVbIjsPhcPQUbdXXtra2iI6ORo0aNVCzZk2sXLkSnTp1wo0bN9CkSRO8evVKi1ZzclOi29x3UVB2sLyhrvb2wNu3wPPnTN+2X7+PvnRxaNxmZwPDhjHfNADMn89yvhT3zFN1cgkSKwmWdFqCcqbl0G5zO8Vsx9yRt3mTlR2IP6B3kbd5ufviLg7dZg7cYwnH8PLtS8U2A5EBmlduDu+a3vCu6Q1XO1eI+DRgjpbhTtsC0LuGTBg1rFYNSEpSljs4AL/9xhqeDh1YWYcOwJEjurHzAwgNZYOduWV6y5VjbWhUFDBoENOMl8uBp09ZAzhpEptSUrYsILzPWVgAf/wBDBzIJRM+ZbSZ4Ozsg7NIfZWKyKGRkEOOdefWYVvstnzXrGxRGU0cmuDQnUPIlmUryrmEQvGhd3W2HtCjRw9YWFhgw4YNKF++PC5fvgxnZ2dERETg22+/RXx8vK5NVKHEP0NhFLJWLdYDk8nYzJikpMKH03A4HI4eo6362tPTE0OHDsWAAQPw7bff4sqVKxg7diz+/vtvvHjxAqdPn9ai1ZzclPg2tyDyek5371aGuk6dCsydC1y6pNS8BZiE0dGjWrm8phq3AOvnengAM2cyEzw8NHPeyuXsVn77ja2PGQOsWMGSdhcnQp9rz409WHF6BbrW6IrpHtMR9zQOw/8dDi8XLxy8fVAl8jZ3srLxzcdj983deh95m5u3src4mXQSB28fxP7b+xH7OFZle1XLqvCu6Y3utbqjnVM7GIuNdWQppzTBnbYFoHcNmTBqeOIEGwmsVInV1O7uTI/nxAnAz0+5/0eKqhc3Mhkwbx4LJjYxYXK9t24xLaAff2T72NqyxsjUlKlB2Njkl/kFWEbN4GCgWbPivguOvvKhCc7GtxiP5aeWK3Sa3APcEXM/BqF9QpH6KhWhN0Jx9G7+l7xuNbvBwdwBW65s4RIKxYTe1dl6QPny5REdHY1atWrBwsJC4bRNTExEnTp18Pr1a12bqEKJfoYFJR4DVIXtOBwOpxSgrfr63LlzyMzMRLt27fD48WMMHjxYEXkbEBCABsWdbekTokS3ue9D8Jx27cqyeDVsCMyYASxcqPSc9ujBvKqCs/b2bSZHqKXLv0vjVtgnb9BSYXVuV6wAxo9n3/v1Y7NOjXXkI1QXeetg7oBVXVYpIm/zJisTQZQv8vbg7YMlauZicnoy9sfvx774fTh696hKv8/C2AJdanRBr9q94FXDC+bG+iVLxik5FKnTNi0tDcHBwbhz5w4mT56McuXK4cKFC6hYsSIqV678UYYXNXrXkAmjhg4ObLpHTAzw8GH+FsHEhM2baNuWaeeVMNQlKQNUZRFCQoCbN4Hp05lj9swZwMoKSE9XPaZBA2DPHuYA5nByo6mkwtAGQ1V0mvbe2gsAimRmwkuHtYk1zIzNkJaVhtdSpSOsjLgMypmVg5zkCOodxCUUihC9q7P1ABsbG5w8eRJ16tRRcdpGRUWhV69eePToka5NVKHEPsPcncO0NBY6Y2PDRhCvXGHZqvv00bWVHA6HozVKbH3NUVDqn2FoKDBqFEuWIpDXcyokJQNY6OqCBVq7fEEat4JpvXuzBNxRUUx+18aGSR0UdmLO9u2sn5yTA3h6snPrSi5Q6F89yHiAH4/8iKYOTbGn/x7sjN2pkqys+/buOHjnIDpX74w9/fcoIm8DfQNhJDbC4H8Gl8igl6ycLBxPOI49N/dg7629SH2pzI9SxrAMOrt0Rr+6/eBT0wdljbmmI0dzisxpe+XKFXTo0AFWVlZITEzEzZs34ezsjJkzZyIpKQlbtmz5aOOLEr1syEJDgV692PfZs5nObatWbD0qijU0+/ax7wDL4tWkiW5s/QgKSlIGAFu3stmnVlZMAmHaNODnn5XOWxsb4MUL1WM8PNjMmHLliu0WOCUMTSQVJFYSDGs0TCWZWY8dPRQSCjHDY/BG+gbtNrdDGcMyyJHlqOjgVrKoBLcqbgiLD8Mb6RvleUvIi4i+o5d1to7p168frKyssG7dOlhYWODKlSuwtbVF9+7dUa1aNWzcuFHXJqpQIp9h7mmYLVuy0URjY5ZNs3HjDxOs43A4HD2nRNbXHBU+iWe4bRvT2QsIYA7bvPoDmZmAcO/29kzOyMhIa5fPq9RgYKAsq1eP7XPtmvIVIScHaNOGBf1u385isDR5dTh0iDl5X79mMr6CE1iXvCtZmRAEkzfy1r+tP/wi/NCqaitEJUdh/4D9sDG1wbz/5um97m1e5CTH2QdnERoXipC4ENx5cUexzczIDD41fTDAdQC8XLxgJNbe3xyndKJpfV1ohZSJEydi6NChiI+PR5kyZRTlXbp0wX///fdh1n7q+PoC/v7su78/E3eNigLu32frjo7MoykwZYpu7PxIxGKm9LBzJ1s3NmbTRQDWGLVpwzRup01jI5JmZiwbJ8AUIUaOVD1fZCRLDNq5M/DyJTicfIgNxGgraYv+rv3RVtIWvev2xu0fbiN8SDjGNx8PEUSoZ1cPX0i+gKOVIyYfnozu27sj7FYYvnT9EgBQx7YOlsYshX1Ze7yRvsGxIcewu99u+H7GnLEPMx8iOC5Y4bAd22wsjn51FK52rui9qzdC40J1dv+c0snixYsVkbZv3rzBgAEDIJFI8ODBAyxcuFDX5pUOIiPZbJfOnZkoHcBCa5o2Zb2zadOAhAS2H4fD4XBUePToEb766itUqlQJhoaGEIvFKguH81EIM3tr11Z6QGUyJju4fTvTEwBYNFBqKgt+0iJiMZM7CAtjY7gxMczBmpjIJubs28cCf8ViFptVsybb58kTlqLGxYWVv49OnYBjx5ij9tQpdqu6nkzlW9sXwX2DcfXxVYw5MAYAMCJsBK4+uorxLcYDAOrZ1YOc5FgQtQASKwk2XtoI75reCBvAtIbT3qThYeZDxD5hurHLTy1Hu83t4LLSBUHXghCRGIHtV7cjIjECMrlMrR26wkBkgOZVmmNhx4WI/yEel767hBkeM1Ddpjpe57zGzms70X1Hd1RaWgnjDozDhZQLujaZUwoodKStlZUVLly4gOrVq6tMy7x37x5q1aqFN2/evP8kOkRvRx9lMtYAPXrEPJsiEbBxo6pEgoGBMkT18GGgY0ddWKoV1MkllC8PPHumjK795RfmvBV+ii+/ZP7s9u1ZA5YbAwNg+HBg1Srdaf5wSh7qdJoEHdwGFRug6/aucK/qjpPJJzGuxTiFDm5uCQUrEysYi43x5LXyj9nMyAz96vTDuZRzSH2Ziu29tqOtpK3ejx7rI3pbZ+sYqVSKnTt34vLly3j58iUaN26MgQMHwtTUVNem5aNEPkMhSaggsO7pyXphhoZsuxDFExgI9O+vS0s5HA5Ha2irvvby8kJSUhLGjBkDBweHfFnXu3fv/rGmcgqgRLa5hUVdUrK80oKGhkCXLsC//zKZIyE5mRZRp3Hr6AgsXcpisnInLxs3jjls/f3ZpNnCyCVcvcpeQ1JTWU7UY8eUfmtdoS5ZWWeXziqRt2G3whTJoGOGx4CIVCJv8+rebry4EedSzqlcJ2/yZ7cqboi+H62YRakv0blEhPMp5xF4NRCBVwPx6JXSu97YoTFGNB6B/q79YWlSSv8nOR9Ekckj2NnZ4dChQ2jUqJGK0/bIkSP4+uuvkZycrPG51qxZgzVr1iDx/zVd3bp1MWvWLHh5eRV4TFpaGmbMmIHQ0FA8f/4cjo6OWL58Obp06aLRNfW6IZswAVi+HGjUiGW/7NwZ6NmT1fgHD7JWQFA1b9SI1fjFnU5SiwhJypYsATIylOWCjq3gvJ03D1i/HqhShQUgi0TMefv112xWjDC4CrA+9K+/Mgcud95yNEGTZGabe25GBbMKCrF9dRIKj189Rvcd3VHVsiqSM/LXg45WjljaaSmXSygkel1n64CcnBx89tlnCAsLQ+0SkpSyRD5Df3/VJKCAaiYRQS8vPJyFvnA4HE4pQFv1tYWFBSIjI9GwYUPtGcfRiBLZ5n4Igke0SROWlEzoN//zD3DgAJsZc/486zQaGLA+dJUqWjdDJmOTbo4dA+bOZX3VVq3y+5VPn1a+NrRuDXTvzrryixczB2xehYe83L7NApeSkgBnZ3aeatW0fjsfhLogGImVBEs6LUG2NBsDQgcgfWo6BoUOwtVHVwER4Grnit1f7lbo3o5vPh4rTq+AXVk7GIgMEDc6DmvOrcGcE3NUdHANDQwhlUuV17GWYFGHRbAta6s3jlypXIrDdw5j8+XN2H1jN97K3gIAyhqVxeAGgzG2+Vh8VuEzndnH0R+KTB6hW7dumDNnDnJycgAAIpEISUlJmDJlCnoJuqwaUqVKFfz66684f/48zp07hy+++ALdu3fHtWvX1O7/9u1bdOzYEYmJiQgODsbNmzexfv16vU9+pjHCqPOVK6yBOXAAGDFCmf0ydxrKixdZhE8JRiwGZs0Cnj9n/eMKFVi5kHjs1i322agRG8EkYoOmgq688OdmYaE8Z0YG06a3tWUzWbOUdTyHo5bcEgqz2sxSyCcE+gbCv60/sqRZ2HJ5CwxFhu+UUPjrwl9wsnbCEs8lEEGEyhaVIRYpXxgeZj5Er129MGLvCL2c7sMpGRgZGen9jJYST2io0mFrYACcPcuctK6urIMYHMy05gUdPQ6Hw+GoULVqVXxArmsOR3N8fZnm3qVLqv3mGzdYduvTp1mIa5kybKbqpk1FYoZYzMZu/fzY2O7ChexygsrS9Olsv9yvDbt3M39yaiqT5m3X7v2SCS4uwH//MYft3btMVjB3hK8u8a3tq1Z+zsHcAVYmVgCArtu6IuxWGIY1GobEtERM95gOA5EBYh8ziYQd13bAu6Y3QvqGIOVlClacXoHpx6ajvXN7uFd1h62ZLUQQobxpeQDA1p5bETM8BhVMK6BvcF+029wOA0IHoN3mdqj+e3XMOTFHZxILhgaG6FKjC3b23okHEx9giecS1CpfC69yXmHNuTWovbo2Om/tjMN3DvN6kqMZVEjS0tKoQ4cOZG1tTWKxmKpWrUpGRkbUunVrevnyZWFPlw8bGxv666+/1G5bs2YNOTs709u3bz/4/Onp6QSA0tPTP/gcRYZUSlSxIhFAtHIl0fjxRCIRkbc30cmTRF5eROXLs+0AUYUKRFlZurZaa0ilRP7+7NZMTIjq1WPfv/lGecsAkbs7kZMT0dy5bL1ZM/ZpZaW6H0BUtizRr78SZWTo+u44JZWQ6yEkWS4h+EGxmM01o/n/zad9N/cR/EDuG9xJ5CeiXbG7SLJcQj6BPiSTy2j/rf0EP5DFfAuV4+EHqrK0CoVcD9H17ek9el1n64h58+bRkCFDKCcnR9emaESJeoZSKZG9vbIREYmIfHyIoqOJ0tKIWrUiMjNj5SH8/5fD4ZQutFVfHzp0iDw9PSkhIUE7hnE0pkS1uR9LeDhrq1evJgoMZOtSqXJ7dLSyPZdIiGSyIjUnJET52uDnxy579ChbF14bhH28vNj2gACimBjVfd5FcjKRi4vylu7dK9Jb+iAK6jsFXQuiwCuBBD9QZnYmyeQy8gn0Ifvf7Al+oJjkGMp4k0HwA9kuslX0pyITIwl+ILe/3ChHlkM+gT7ktNyJgmKDSOQnooq/VSSHxQ6UlpVGCyIXkOlcU5VrS5ZLaFfsLgpPCKfAK4EUnhBOUpn0/TeiReRyOR27e4y6be9GIj+RwrYmfzahoGtBxW4PRz/QtL4utDyCQFRUFK5cuaLQ0uvQocNHOY9lMhmCgoIwZMgQXLx4EXXq1Mm3T5cuXVCuXDmYmZlhz549sLW1xYABAzBlyhSNRe31fsqIIJHQuTNw+TLQsCEwYwab8x8WxsJI//iDhZxKpUz0ddo0XVutVdTp3QKqWrY7dgDDhjGphGnTgJ9/VsopCNq4uSlThuVvGz+eSRRyOIXhYyQUTt8/jSevn6CxQ2M8z3qOhLQElXMv8VyCiS0nFvMdlRz0vs7WAT179sSxY8dgbm4OV1dXlC1bVmV7qCbZLYqREvUMw8KUGTCHDAG6dcsvWAew6SGzZhW7eRwOh1OUfEx9bWNjo6Jd++rVK0ilUpiZmcHISDWL+vPnz7ViLyc/JarN/VgE/fnMTMDcnJUJegUpKUxzr2tXwNSUTb88coQJyxYh6nRunZyYDEL37krJhClTAHd3pcpSTg6Lnr19m92WkF9NHQ8esO23bwPVqwMnTuhe4zYveftOgoZtp+qd8une5s4bcvXRVbgFsGm1McNj0KJKC+y/tR9dt3eFf1t/zGozS5FXxN7cHp9X+hxTWk2B+0Z3hVZu15pdkZaVhuSMZGzz3YbxB8e/Vyu3OCUV7r64ixWnVmD9hfUK6YfPKnyGOW3noFedXjAQlVz5S07hKDJNW21z9epVtGzZEm/evIG5uTkCAwML1Kf97LPPkJiYiIEDB2LUqFG4ffs2Ro0ahbFjx2L27Nlqj8nOzkZ2drZiPSMjA1WrVtXfhiwigs2RsLEBXrxQlgtOWoGyZYFXr1gjlJgI2NkVt6VFSkF6twBz6F6+zGar5k5WJjhv3dyA+vWBtWvzn9fYGJg4kTWmghwDh1NYCnoRaeLQBH4n/HD0q6NYcXoF9t7aC7uydmheuTl2f7kbL7NfwmqhFRraN8Sl1EuK8zW2b4wRTUbgm8bf6IWYvj7xSXU+NGTYsGHv3L5x48ZiskQzStQzbNuW9X4qVmSNi7Mza1Sio1U7gDwBGYfDKYV8TH29efNmjfcdMmRIYU3jaEiJanM/FqHfHBMDtGih3mMKAJ9/zqSO+vZlkgpFjEzGTOvfH6hRg303MlKae/Iki8eKjQXi44E9e/KbnVtGXx3JyczJm5AA1K7NXl1sbYv81j6Yd+neljMth3ab2+Hk1yfxa9SvOHX/FJ68fqJI/uwf4Q+/E37YP2A/vGp4ITM7E5a/sr/tmOExqGtbF5a/WsLWzBYtqrTA7i934/T90ypJz96nlasLJ+6TV0/w++nfsersKqS9SQMANLRviHlfzIOXi1e+BI6c0keROW1///139ScSiVCmTBm4uLigdevWGke+vn37FklJSUhPT0dwcDD++usvnDhxQm2kbc2aNfHmzRskJCQozr906VL89ttvSElJUXt+Pz8/+Pv75yvX24ZMUC23sWG6taNHs8jarl2ZV3LuXKbd06ABS04GMP2eP//UqdlFRUHOWyGaVoiuze283bgR+PJLNhcmd3RuboyMgD59gGXLSp2/m6MD1L2IOFk7YWjDoYqMqS2qtFCMDIcPCceFlAuYcXwG3kiV+qSmhqaY1WYWprpP1cFd6CefVOejlFJinuGBAyzTdF54AjIOh/OJUGLqa06BfFLPMHe2r8GDmVPW25sJydapw/rPFy6wKFsiFr3z8CHrSBYDQq40b2/WjY+LY8myvbxYNz44mO0n7DNuHAsE9vdnScrCwtg+BTluExOZRu79+0DjxuzVRJ8fuRD0sufGHqw4vQJda3TFdI/pqF2hNur8UQdykuPRq0fwb+uv6D81q9wM7gHuiLkfg6hhUWhVrZWiPwWgwOhcwbErOHLfFY07ovEIzI2cq+LELc4EZ+lv0rHs1DIsjVmKzLeZAID2Tu2xvPNy1LOrp/XrcfSHInPaOjk54cmTJ3j9+jVsbGwAAC9evICZmRnMzc3x+PFjODs7Izw8HFWrVi204R06dED16tXxpxonZJs2bWBkZISjQmIuAAcOHECXLl2QnZ0NY2PjfMeUuEhbgNXwQpat8uWZZzK3RML48Sz6Z+xYto9IxBy59evryuIipyDnrZUVS1wmOG/nzQPWr2fJQaOi2E9DxBrIDRvYNBNZLi1ykYg5b//6SzWhGYdTWGRyGSISI9A/pD9qlKuBiKERCL4ejAGhA5A5LRNmRmbosaMHYh/HYlGHRegb3BedXTrjwO0DaFm5JU49OAUCq44bVGyA1V1Wo1W1Vjq+K93zSXU+Sikl4hm+fMmcs8+esZktLVoA+/YB16+zEcGwMGDXLmDLFmVojIaD0xwOh1NS0FZ9vX//fojFYnTq1Eml/PDhw5DJZPDy8vpYUzkFUCLaXG0i9JtNTYEmTVh7ff06y/yVu+0+dAh4+5bJEI4bV6zm5Y2idXAAVq1SlUvYvZvlThPGhVu10kwu4eZNoHVr4PFjFsW7fz+TBdR31AW8AEDTSk2xzHMZBv0zCFUtq8K6jDXC4sMUMxdD+4XCd6cvzj44i9RXqQVG5+Z27L4vGlcEkV5IKjx9/RS/Rv2KVWdWIVuWDQORAb5v+j382/qjvFnxDDRwihdN6+tCC2bMnz8fn3/+OeLj4/Hs2TM8e/YMt27dQvPmzbFixQokJSXB3t4eEyZM+CDD5XK5ipM1N61atcLt27chl8sVZbdu3YKDg4Nahy0AmJiYwNLSUmXRe3x9WQNjYMA6kAcOMNEbIbJ2+XLmsBVqbiLmyC3F2QfFYiYf+Pw5G30UpA3S09nnrVvss1Ej1igSsX63G6urUbEi+6xenX26uLCfl4j91JUrA6tXMz0hDudDEBuI0d65PdZ6r0XM/Rj02tULz7OYZtv2q9vRY0cPhN0Kw8IOCzH56GR41/TGDI8ZAID5Hebj1g+3MLj+YADA5UeX4b7RHV9s/gInEk/o7J44+omTkxOcnZ0LXDgfwM8/s/bWzIxlmD55kqV0JgL+/ps1JkOGsA7g4sXcYcvhcDjvYOrUqZDJ8mdsl8vlmDqVzybiaBFfX9Y5zMpiETvW1qzNjo1lYaq9e7Mw17dv2f5//VWsfWZfX+Z4DQ8Htm4F7O2Zb7lHDya9m5jIAoMB5md2cgKePgVq1mSTe548YdG3Li7MAZyXWrWYq8DCgl1j8GAgl6tEb/Gt7YvbP9xG+JBwBPoGInxIOIJ6B+Hp66fw2OSBe+n3EJUcheMJxzH/i/lY6rkUYbfCUGVpFey9tReLOi6Cg7kDfHf6Yu+tvRjTbAwAIPZxLOQkx4KoBbA1Y3oR9ezqIfZxLADgyesnmO4xHQYiA9SuUBsA0Mi+EfZ8uQeLOi7CvfR7OJZwDOdTzqNi2YpwMHdA2pQ0LGi/AI9fPcbsiNkYEDoA7Ta3Q/Xfq2POiTnYfnU7IhIjIJPnr/MKQwWzCljsuRjXR1+Hb21fyEmO1WdXo/bq2gi8Gggdq5pydEihI22rV6+OkJAQNGzYUKX84sWL6NWrF+7evYvo6Gj06tWrQMkCgWnTpsHLywvVqlVDZmYmAgMDsXDhQhw6dAgdO3bE4MGDUblyZSxYsAAAkJycjLp162LIkCH44YcfEB8fj6+//hpjx47FjBkzNLK/RI0+jh8PrFgBODoCSUlAp06s5g8NZQ5cR0fg3j3mfZTLgaAg1jB9AgiRt7NnAyYmTC8oNhb45hvWFgu4u7MpI9nZTI5w716WY8baGkhLy3/eWrWUeeA4nA+lMLpNsY9jEf9DPPbc3KN2xBkAWju2hn9bf7SVtC22e9AXSlSdXUysWLFCZT0nJwcXL17EwYMHMXnyZL3rEOv9M7x2jc1UkcuBpUtZQtCCdPF4AjIOh1OK0VZ9bWpqiri4OEgkEpXyxMRE1K1bF69evfpISzkFofdtblEgJCTbv5918Bwc1OvRGxmxCJ3Tp9k0TR2QWzKhSRPAzw84epR1+cPCgB9/ZGPDhZVLOH6c9V9zctg5fvut2G9NK7wv+bOhgSGkcqnKMeqic/fF74NfWz/MjphdYDTu6jOrMebAGKzushqjPh+lFUmFJZ5L4Fu7AD2LQhKeEI4fDvyAa0+uAQC8XLywpusaOFo7auX8HN2jcX1NhcTU1JTOnj2br/zMmTNkampKREQJCQlUtmzZ957r66+/JkdHRzI2NiZbW1tq3749HT58WLG9TZs2NGTIEJVjoqOjqXnz5mRiYkLOzs40b948kkqlGtufnp5OACg9PV3jY3RGeDgRQCQWs09hMTRUXReJ2Ge1akSvXuna6mIlJITI1lb15wCI2rdXfvfzY58ODkRz57LvzZqxzwoV8h8LEHl7E929q+u745RkpDIphSeE0/gD40nkJyLvbd4UnRRNGy5sIPiBvLZ6kchPRCHXQyjkegiJ/ETkE+hDR+8cJfiBJhycQJJlEoIfFEu7Te3ov8T/dH1rxUqJqrN1zKpVq2jo0KG6NiMfev8MO3ZUVv4BAaztlUrZEh5OFBhItG8f2x4YqGtrORwOp8jQVn1dsWJFOnbsWL7yI0eOkK2t7Ued+108e/aMBgwYQBYWFmRlZUVff/01ZWZmvvOYrKwsGjVqFJUrV47Kli1Lvr6+lJqaqrLPvXv3qEuXLmRqakq2trb0448/Uk5Ojso+4eHh1KhRIzI2Nqbq1avTxo0b811r1apV5OjoSCYmJtSsWTM6ffq0yvaUlBQaNGgQVaxYkczMzKhRo0YUHBxcqN9A79vcokDoM8fEsPWQECKJJH8Hr3599jlihE7NVWeekxPRrl2s3MeHSCYjio5m28LD2bq3N5G9PdHWrcpXldxs3ao839q1urgz7SP0pwKvBFJ4Qjhl52SrrAfFBpFkuWp/yWyuGc3/bz69eP2CHBY7UMXfKhL8QP4R/gQ/UExyDMnkMmq8tjHBD5SWlUZERNFJ0YpzxCTHUMabDIIfyHaRLfkE+pBMLlPsI/ITkXegN7lvcCfHZY4UdS+KfAJ9FNcR7JPKNPdTqSNbmk2/nPiFjH8xJviBzOeb08aLG0kul2vj5+XoGE3r60I7bbt06UKNGzemCxcuKMouXLhATZo0oa5duxIR0b///kv16tUr7KmLhRLVkEmlRBUrspp35Uqi8eOZg9bbm+i//5jn0dxctcb/+WddW13sSKVE/v5Elpb52+aRI4kaN2bfN20iMjUlMjMj+uUXVubmRjR7tnrHrZER0bx5RG/f6voOOSWdkOsh+V4oHBY7UMj1EJLKpCRZLsn3MhCeEE4yuYzab25PZvPMSOwvVhzrtdWLLqVc0vVtFQslqs7WMXfu3CELCwtdm5EPvX6GBw+qbwAkEtarEsjdc+JwOJxSirbq6xEjRpCrqyvdvn1bURYfH0/169en4cOHf6yZBdK5c2dq0KABnTp1iiIjI8nFxYX69+//zmNGjhxJVatWpWPHjtG5c+eoRYsW5ObmptgulUqpXr161KFDB7p48SLt37+fKlSoQNOmTVPsc/fuXTIzM6OJEyfS9evXaeXKlSQWi+ngwYOKfXbs2EHGxsYUEBBA165do2+//Zasra3p0aNHin06duxIn3/+OZ0+fZru3LlDv/zyCxkYGKj0u9+HXre5RYVUqvR2BgWx/rKPD3PipqcTubuzDqAQ6GRhQfTypc5NPnqUBR+5ubH+Zm7fs0zGbsHJie0bEsICkN71qkKk7OMaGhIdP66TWyt2cjt2/SP88/W54Adquq4pRSZGkuMyR3Lf4E7e27xVHLQyuYx8An3IdpEtwQ+UmZ2Zz4lLRPTi9QuCH6jx2sb5+m1B14LIdK6pynUdlzlqxYkb9ySOWm1opThvn1196NnrZ9r8GTk6oMictikpKdShQwcSiURkbGxMxsbGZGBgQB07dlSMSh4/fpwOHTr0YZYXMSWuIRs/ntW8nTuzmtrLi3kSTU3VR98aGxPlekH6lCjIeWtnxz7r1WOfv/yidN7u3KlsvydNYp+VKyvLhEHZc+d0fXecko7wQrH18layX2xP3tu8SSaXUXhCeL4XBqflTiSVSSnkegg5LHbI9+IhjPB+FfoVJb5I1PWtFSklrs7WIQsXLiRHR0ddm5EPvX2GMhnrDQkhLkIbe/Ik6ymJRKw3lLfnxOFwOKUUbdXXaWlp1KJFCzI0NCSJREISiYQMDQ2pXbt29OLFC+0Ym4fr168TAJUZoQcOHCCRSEQPHjwo0E4jIyMKCgpSlMXFxREAivl/1Ob+/fvJwMBAJfp2zZo1ZGlpSdnZ2URE9NNPP1HdunVVzt2vXz/q1KmTYr1Zs2Y0evRoxbpMJqNKlSrRggULFGVly5alLVu2qJynXLlytH79eo1/B71tc4uakBDWnpuaMidtWhobcBXa86AgFvgk9Jk3bdK1xUTEzBZ8zMLs0KNHVV9DhH28vJSTgmJiVPcRkMuJBgxg+5Ur92nOHM0bnVtQNO7cE3NVnLgiP5EiGvdk0sl8TlwiolWnVxH8QKvPrCYiUkTjCjMr3Te4E/xA+2/tpwWRC/I5cSXLJRRyPeRd5r/zvub9N48M5xgS/ECVl1SmyHuRWvvdOMVPkTltBeLi4mjPnj20Z88eunHjxoeeptgpcQ2ZMORmY6PqiTQzI1qwgNXquT2SgoP3Ew6ZL8h5a2SkKo0wbx4boaxYkX1u2aI+4AogMjAgmjmT6P/vZhzOR5FbDsEv3I/gBzp65yj5BPrkk0zw2upF8AMFXAigmOQY+mLzFyqNv8kvJjTz2Ex6ma3biIGiosTV2cVAw4YNqVGjRoqlYcOGZG9vT2KxmP78809dm5cPvX2Gu3YpJYgePVLtOUVFsd6RgwPr5OXtFXE4HE4pRJv1tVwup0OHDtGiRYto5cqVdOLECS1YWDAbNmwga2trlbKcnBwSi8UUGhqq9phjx44RgHyO5GrVqtHSpUuJiOjnn3+mBg0aqGy/e/cuAVBEwHp4eNC4ceNU9gkICCBLS0siIsrOziaxWEz//POPyj6DBw+mbt26KdY7duxIXbt2pWfPnpFMJqPt27eTmZkZxcfHF3jfb968ofT0dMWSnJysn21uceDvr37mjL8/kzdatUpZ3qaNrq1VUJBcQkiIahBxVJTqpJ+CxpRfvyb6/HO2b8OGbP1T533RuB8rqWC/2J58An0oLStNxYlbFBIKZ+6foRq/1yD4gQznGNLvp37ncgklFE3bXMMPFc397LPP8Nlnn33o4RxN8fAAJBLAxgZ48QKoUIGlk4yIYNvbtAFsbVlWru7dWdnBg0BIyCeTlCwvYjHLEzNjBvtZVq1imTdzctj269fZp4mJMsfM5MksMTjAPjdvVj2nXA7MnQvs28cyf9apUyy3wiml+Nb2RXDfYEw6PAl7b+0FAHT4uwOcrJ0Q3DcY3Wt1h8tKF3jX9MaUVlNw4PYBONk4oUWVFjjy1RH02NED51POo0a5Gjhx7wTmRs5FwKUALOywEANdB0IkEun4DjlFSffu3VWesYGBAWxtbdG2bVveLmsKEcsmDQBffQXY2bHMHsHBLAGZu7ty3/PnC878weFwOBy1iEQieHp6wtPTs1iul5qaCjs7O5UyQ0NDlCtXDqmpqQUeY2xsDGtra5XyihUrKo5JTU1FxYoV820Xtr1rn4yMDGRlZeHFixeQyWRq97lx44ZifdeuXejXrx/Kly8PQ0NDmJmZ4Z9//oGLi0uB971gwQL4+/sXuP2TokYN9ikkJIuPBzZuZJmr83LiBHD7NvCO37a48PVl3fiICKB/f3YbEREsb1pEBOuvbtsG/Por4OTE3AMAe5Xp1Ikl2l65EvjhB9YPNjVlroAmTYBLl4BRo9jP8CkjNhCrJHSe4TEjX8Kz6cenY/rx6Yp9mlZqii8kXyDAKgCTD0+GdRlrXEi9AACIexqHZpWbYUHUAtiXtUfqy1RM95iO60+Yo2HHtR3wrumN3V/uxun7p+EW4IYceQ4GNxiMo3ePYnaE8m+ysAnMPq/8OS58dwHf7v0WO2J3YOzBsTjz8Az+9P4TZkZmWvi1OPqGwYccdP/+ffzxxx+YOnUqJk6cqLJwtIxYDCxZAly8yNafPgXmzGFpJa2sgJgY5pEUHLYCY8cC6enFb68eIThvU1KA8HD2k4jFwMuXbPuPP7LPLVuAoCDWZ7e1ZesA+9kB5iM3/P/wxsWLQOPGwB9/sIaSw/lQfGv74vYPt3H0q6OwNbOFWxU33BxzE761fRGZFInEtERMdZ+KhScXwsnaCR7V2BsaEaFT9U54mPkQ3Wt1x67eu+Bk7YSHmQ/x1T9fofWm1ricelnHd8cpSvz8/DB79mzF8vPPP2PkyJHcYVsYDh0C7txh3/38lOW+vqwTFx4ObNjAyn77jTtsORwOR0dMnToVIpHonUtux2dJ5ueff0ZaWhqOHj2Kc+fOYeLEiejbty+uXr1a4DHTpk1Denq6YklOTi5Gi/UMBwf2aWPDonP8/ABXV9ZfzswE1q1T3T9vlI4OEYuB9u2BtWuZub16sc+7d9n2uXOBsDDmAhCLgdBQ5m8eM4ZtnzCBrYeGsvWqVYGdOwEDA2DTJmX/lsMQnLj9XftjVptZuP3DbYQPCUegbyDCh4QjqHcQnr5+Co9NHriXfg9RyVE4nnAcc9vNhaOVIyYfnozu27sj7FYYvnT9EgBQx7ZOPieugcgA9ezqAQD23NiDvkF90cShCQBg/4D9iBkeg3q29dBrVy9MODQBEYkRkMll77Xf3Ngcgb6BWOq5FGKRGFuvbIXHRg+kZKYU3Y/G0RmFjrQ9duwYunXrBmdnZ9y4cQP16tVDYmIiiAiNGzcuChs5vr7Arl3Al1+ykM8OHVi5mRmwYAHw3XdA69bAzZssnNTKinkqp04F1qzRre16gFgMtG3LFg8PoE8fwNgYqFSJjVxevqyMuLW1Zc5bU1OgWTNW9uwZIJUqz5edDYweDRw5AgQEsPcCDudDEBuI0d65PdZ6r0XvXb3Ra1cvTHOfhrsv2Bva3P/m4uDtgwjuGwyxgRihcaGYdHgSEtMSAQATD0+ExFqCBe0XIOFFAuZFzkNUUhQar2uMUU1HYe4Xc2FVxkqHd8gpCsRiMVJSUvJFFD179gx2dnaQyd7/svfJs3ix8vu+fawid3BgjYTQaJiYsO2VK+vERA6Hw+EAkyZNwtChQ9+5j7OzM+zt7fH48WOVcqlUiufPn8Pe3l7tcfb29nj79i3S0tJUom0fPXqkOMbe3h5nzpxROe7Ro0eKbcKnUJZ7H0tLS5iamkIsFkMsFqvdRzjHnTt3sGrVKsTGxqJu3boAgAYNGiAyMhKrV6/G2rVr1d6DiYkJTIT26lNHmKE6bx4QGwt4ewO7dzPPpVzOQlIlEqBcOeDCBRZ+6ufH2n09IfekHzc3ZfmlS8pJP6GhbEKttzcwfTowYgSwejWbbNu7t3K/du3Y7c2aBXz/Pevb8vF99eSNxAWAnrV75ovGnRk+EwBwL/0ezAzNMO+LeWhQsQGWn1qOrtu64mTySYxrMQ7LTy1XOGtjH8cCUEbf/t3zb1gvtEbamzRkSbMQ+4RtX35qOZafWq5x5K1IJMKElhPQyKER+gb1xYWUC2ixoQX2D9iPunZ1tfwLcXRKYXUXPv/8c5o1axYREZmbm9OdO3coMzOTunXrRn/88ceHSDkUK3qrracJgjq5kRHTsH32jGj+/PxJyQwMlN+LWD+qJBISwjJ15pU8GjJE+T0oiKhpU6VEMEA0ZQr7rFBBuZ+jI1GufAcczgcTcj0kn76Sw2IHhVh9bh3cdefWKUTwc+vgJqcnU9+gvorjKy2pRP/e+FfHd/ZxlOg6u4gQiUQq2aYFHjx4QGXKlNGBRe9G757h1ausAheJmJ5tXu07nnyMw+F8ouhdfV0IhERk53JlDz506JBGiciCg4MVZTdu3FCbiCx3u/vnn3+SpaUlvXnzhohYIrJ69eqpnLt///75EpGNGTNGsS6Tyahy5cqKRGRXrlwhAHT9+nWV83h6etK3336r8e9Qkp+hVhASkgFE69YRZWSoJiQLCVHmjAGIDh/WtcVqkUqZmVu3EtnbM3l9mUxV4zYnR/VVRd2ri1RK9MUX7FabNOH5WT4GTXRxg64FqU0ybf+bvaJM0MH1j/BX27fz3ubNdHEPjtdY8/b2s9tUc2VNgh/IaoEVHbt7rBh+Ec7HUmSJyMzNzen27dtERGRtbU2xsbFERHTp0iW9zFqdlxLdkEmlRNbW+b2NZmZEv/zCMmyZm6tuq1GD6NUrXVuudxSUrAwgmj2bNYwAc9wKou8VK6pPUmZsTLRhg67viFMaEF4Gtl7eSvaL7cl7mzfJ5DKSyqQkWS4hn0AfypHlkE+gDzktdyKpTKp4GRDWiYiO3T2mEKiHH+jL4C/p8cvHOr67D6NE19laZsWKFbRixQoyMDCgefPmKdZXrFhBS5cupR49elDDhg11bWY+9O4ZjhyprMCbNlWmZF63TpmaWSjnycf0gr//JhoxgnU4jY3ZI9q48cPPl5PDsm97ebG23ciIvQ80bUo0YwZRYqLWTOdwShR6V18Xks6dO1OjRo3o9OnTFBUVRTVq1KD+/fsrtt+/f59q1apFp0+fVpSNHDmSqlWrRsePH6dz585Ry5YtqWXLlortUqmU6tWrR56ennTp0iU6ePAg2dra0rRp0xT73L17l8zMzGjy5MkUFxdHq1evJrFYTAcPHlTss2PHDjIxMaFNmzbR9evXacSIEWRtbU2pqalERPT27VtycXEhDw8POn36NN2+fZsWL15MIpGI9u3bp/FvUNKfoVYYPz5/h83JiSUgDQ9nHTehfMAAXVv7XnLnSRVyqa1bp+qHFoiOVk1WRkR0/74yp/nMmcVufqklrxNXcMBGJkaS4zJHct/gTt7bvEnkJ6LxB8cT/EDpb9LJJ9CHJMskir6dTC6jjDcZigRmeZ3BkuUSRRDPu3j66im5B7gT/EBGc4wo+Frwe4/h6JYic9pWrFhRMQJYu3Zt2rNnDxExp23ZsmU/wNTipcQ3ZEIj1KwZc+A2a8YctnmjbY2NlRG348fr2mq9RSplTtrcwckAG9EEiE6eVEbctmr1buftDz+wjiCHow1yR9auOr2K4Adad26dSmStgDBiG54Qrih7/fY1/XT4JzLwN1BkNc29vaRQ4utsLSKRSEgikZBIJKKqVasq1iUSCdWsWZM8PT3p1KlTujYzH3r1DDMy2EAnQNSyJQtLUZe22dCQTbng6AWOjsqZLsL3D3XaJiYSNWigbM8HDSKaNo1o7Fgid3cWfG1iQvSOZO0cTqlFW/X1+fPn6cqVK4r13bt3U/fu3WnatGmUXYShfs+ePaP+/fuTubk5WVpa0rBhwygzM1OxPSEhgQBQeC6PVlZWFo0aNYpsbGzIzMyMevbsSSkpKSrnTUxMJC8vLzI1NaUKFSrQpEmTKCfPS394eDg1bNiQjI2NydnZmTaqqaRWrlxJ1apVI2NjY2rWrFm+NvvWrVvk6+tLdnZ2ZGZmRvXr16ctW7YU6jfQqzZXVwiRtKtXEwUGsvWgoPxtPf4/gzUtTdcWvxd1rypOTqoOW6mUaN8+pXM290ShXbuUE3LPnCl++z8F1M2aNJtrRvP/m0/7bu4j+IHcN7iTyE9E/hH+ishbImVfrqBZlUJkbuCVwHdG32blZFGfXX0IfiCxv5i2Xt5anD8Bp5AUmdO2e/futG7dOiIimjRpErm4uNDcuXOpcePG1L59+w+zthgp8Q2Z0AgJw2W5o23nzSNavFg5xVPYJhIRRUTo2nK9JihI6euuV4+NXiKXNEKTJmwBWBTu0aPse82arK0XfuqOHUtEu88pIahr/J2WO6k4bKUyqeJFYOaxmfka8bMPzlKd1XUIfiADfwOa9988ksllxX0rH0yJr7OLgLZt29Lz5891bYbG6NUz/OsvZYUdHa0sF+YhBgYqw1hyh6lwdMqRI8ro1wULPtxpm5FBVKsWO37yZKL/z2xWIT6eRS9dvPgxFnM4JRNt1ddNmzZVSA7cuXOHypQpQ/379ycXFxcaN26cFizlFIRetbm6IreGgDA4K4SqnjzJplk4OChnqH7/va4t1giplGjZMqU/OrdTVp1TV1B8EhgwgJW7unKZhKJCUwmFwCuBBD9QZnYmyeQy8t7mTYZzDBWzLIXI28ArgRR0LYhM55pqHH0rlUlp6O6hCifwunPrivlX4GhKkTlt79y5Q5cvXyYiopcvX9J3331Hrq6u5OvrS4klYD5ZiW/IhEaoUSNl2ImbG9HOnQWPHgq1dkm952JCndat4BsXomybNmVtvzD1ZOxY9lm1qvKYevWIkpN1fTec0oJUJqVlMcsUo625nbLqnLrqGvGX2S9pyD9DFPt4bfWi9Dcloz4o8XU2R7+eoZubsrLOFX2lQkYG2x4YWLy2cTTiY5y2s2axYwcNev++vEPL+RTRVn1taWmpkNP79ddfydPTk4iIoqKiqEqVKh9tJ6dg9KrN1SWCo9bbmzlovbyY5l1uTYH581mjYGJSYvTr8/qjiVRv1d2dzUjJe6tERE+eKPu6/v46u4VPioIkFPLOohT6aHkjb4Vj3Dcw2YP9t/ZTTHKM2pmXuZHJZTQqbJTivH+c0f/cU58imtbXIiKiok52pk9kZGTAysoK6enpsLS01LU5H0ZoKNCrl3J99mxgzhzAzg549AjYsgW4fBlYsoRtt7QEMjKAoUNZlkxOgchkLOHoypXA06fK8ooV2U8bE8MybzZvzrJ4SqWqx5ubAy9fAtWqAUeOADVrFqv5nFKKTC6Dy0oXuNq5YveXu2EgMkBoXCh67+qNrjW7Ii0rDckZydjmuw0LTy5E2K0wBPcNzpd1NOBiAEbvH4030jdo4tAEBwYegG1ZWx3dlWaUijq7CLh//z7+/fdfJCUl4e3btyrbli5dqiOr1KM3z/D2baBGDUAkYm7b1asBGxvAwYFlnBayR8fEsJTN4eFA27a6s5ejll9/BaZNY68z70kon4+qVYH794Fbt9ifAofDUUVb9bWlpSXOnz+PGjVqoGPHjvD29sa4ceOQlJSEWrVqISsrS4tWc3KjN22uPhAaCowaxTpxAk5OwOLFgK8vaxCqVmXlgYFA//66sbOQhIYCvXsD3t7ATz8Bgwax27C2BvbtA4KD2e3J5UCPHkBsLBAfz15zdu4EvvwSMDEBrl7lbWFxExoXikmHJyExLVFRJrGSoEftHlh+ajkyp2XCzMgMPXb0wNVHVwER4Grnir97/g3rhdYI9A1Ef9f+yJHloM2mNrj9/Da299qOtpK2EBuIVa5FRPjpyE9YHLMYALCp+yYMaTikOG+X8x40ra8NCntiZ2dnPHv2LF95WloanJ2dC3s6zofg6wvs2gUY/P/x+fuzDqipKTB5MjBrltJhCzAvIgBs2sRqak6BiMXs50tNZT+rgLc3+3z1ijlsz50DGjYE1q1j5d9/zz5fvgTs7YGkJMDdHbh2rVjN55RSxAZiLPFcgrBbYeixowei7kVh4qGJaFW1FUDAyeSTWNppKVpVa4XdX+6Gd01v/Hj4R8jkMpXzfN3oa0QNi0IFswo4n3Ie7hvdkZSepKO74nwox44dQ61atbBmzRosWbIE4eHh2LhxIwICAnDp0iVdm6e/bNvGPuvXBwwNgdGjgQEDgHbtABcX1guSy4EFC1inzsNDt/ZytMq9e6x/XqUK76RyOEVN06ZNMXfuXPz99984ceIEunbtCgBISEhAxYoVdWwd55PB11fZJw4IYIOx8fGsHGANgjA4GxqqExM/BF9f5pi9epW9qty7B0RFsX6n4LAFmKtg2jQgIQGIjGRlffsCnToB2dnADz8wFwKn+PCt7YvbP9xG+JBwjG8+HiKIUM+uHlxsXAAA269uR48dPRB2KwzDGg1DYloipntMx/Un1wEADhYOCI0LRc1VNRFzPwZPXj9Bh787wGWlC0LjVP+GRSIRFnVchHHNxwEAvv73awRdCyreG+ZohUI7bRMTEyGTyfKVZ2dn48GDB1oxiqMBffow76LApEks2nbxYkAYvR7y/5EUuVy534gRrObmvBPBebtrF+vbb9jAyjt0YBG2TZuyYKx169j2NWuUxz55Akgk7LNdO+645WgH39q+CO4bjKuPr8Jjkwfupd9DVHIUrj25phJVayAywDT3aUhIS0BkUmS+8zSp1ARRw6JQzaoabj27hVYBrXDz6c3ivh3ORzBt2jT8+OOPuHr1KsqUKYOQkBAkJyejTZs26NOnj67N00+IgB072PcrV9iom0gEeHmxirx2bTaDpXlzICyMtaVi8TtPySlZpKayzypVdGsHh/MpsHz5cly4cAFjxozBjBkz4OLCHBLBwcFwc3PTsXWcT4rKldln7drMQSsWs6mVERHA9u3KSNtTp0qUB9PXl00gmjmTre/fr+qPFqhdm32GhLBblsuBVatYpO2hQyXKV11qEBuI0VbSFss6L0Nw32DEPonFmANjAAAjwkbg6qOrCO4bjBrl2AhzHds6WBC1AE7WTnj66il67+oNVztXHP3qKADAv60/XO1c0XtXb7WO22WdluGbRt9ATnIMCB2Afbf2Fe8Ncz4eTfUW9uzZQ3v27CGRSERbtmxRrO/Zs4dCQ0Np9OjRVLNmzY8TdSgGSpXOj1RKZGmZX8fW1ja/OKuxMcuGDRB9/jkXaysEuZOUCbLB69YxfVshSZmgH7R2rTL3m5MT+165MtG9e7q+C05pQSqT0sxjMxW6Ruqyh+YWry+I5PRkqr2qNsEP5LzCmZ68elKUZn8wparO1hLm5uYKrUBra2uKjY0lIqJLly6Ro6OjDi1Tj148wytXlO2hl5cyMUleLXhDQ1bpc/SWD9W0PXWKHdeiRZGYxeGUCoq6vs7KyqK3b98Wybk5DL1oc/UJdUnJ1OWBAYjOnNG1tYVGyFEeE5N/W0gIk/NVl5zs55/ZurOz+qScnOJD0L0df2A8ifxE5L3Nm6KTohWJpt03uJPIT0S7YneRZLmEfAJ9SCaXKXRvwxPC6a30LbX8qyXZLrKlo3eO5usfSmVS6h/cn+AHMp1rSqeST+nobjm50bS+1jjStkePHujRowdEIhGGDBmiWO/Rowe+/PJLHDlyBEtyT8nnFD1iMYuwBdhUToBF1z59qpROEKJtK1ViAqxlygBnzwI//lj89pZQevdmo5NWVkBiIisbMQK4eBH47DMmh3jyJLB0KdCvH9vu6Ai8fs22P3gAdO4MvHihs1vglCLEBmK0d24PALAxtcmnXwQAl1MvAwCuP7mOiMSIfDIJAFDFsgpODD0BJ2sn3H1xF747fZEtzS5a4zlaoWzZsgodWwcHB9y5c0ex7WluMW6OkuBg5fdZs1gbKYSphIczLbtVq1g7WaGC7uzkFBn29uyTTwrjcIqe5ORk3L9/X7F+5swZjB8/Hlu2bIGRkZEOLeN8cojFTCIhLIzNpundm4WfrlvHZtsATN8eYLNWSxgeHmyG5/z5qpNrBd1buZz1VdPS2CxRV1dWLvRh795luVw4ukNd5K1bgBu6bmeyMhdSLmBXn12wLWurkEsAoBJ9+z65BLGBGJt7bEaXGl2QJc2Cz3Yf3Hl+R609HP1DY6etXC6HXC5HtWrV8PjxY8W6XC5HdnY2bt68CW9B+JNTfMyYwRKQCS9GmzezgTS5HLC1ZeuA0tsoOHNXrmSdVI5G+PoCKSmqSU9kMuDGDaV+EADUqcM+ExOZ5v2zZ0C5ckBcHBN9V6MswuEUGo9qHpBYSzA/cj7kJFfZFnw9GJ5bPQEAcyPnot3mdmp1jgDAtqwtwgaEwdLEEpFJkRi5bySoBE0N+1Rp0aIFoqKiAABdunTBpEmTMG/ePHz99ddo0aKFjq3TU/75R/m9Xj3ld7GYTZfs3x8YPJiVpaQUq2mc4sHRkc2STU5mU0g5HE7RMWDAAISHhwMAUlNT0bFjR5w5cwYzZszAnBLoGOOUcHx9WV6XS5dYP/nAARaBc+MGi8wJCGD7HTgA5Enuqu/k9kn36MEcs2lpwJgxyhzlq1ax4KMWLYDdu1mulpkzgblz2Tl++UU1ATdHd+TWvA30DYR/W39kSbOw5fIWnEg8AQB49faVQve2d53e6BvcVyO5BCOxEXb23onGDo3x5PUTeG3zwtPX/MGXBAqtaZuQkIAKPApFfxCLmaBqTo6yLG+07ZYtyojc16+VyVWGDwcuXChee0swYjHw119sNFNIYiLoBwGqo5nJyaysalUWYWtsDBw+zMTgOZyPJW9ispjkGGRmZ2JB5AL0CeqDLGkWFrRfgMxpmYgZHlOgzhHAdJJ29t4JA5EBNl3ahMXRi3VwR5zCsHTpUjRv3hwA4O/vj/bt22Pnzp2QSCTYIAhwc5TcvcuydQhtYmys+v2EcgeH4rGLU+wMH84+hY7quyhh/XYOR6+IjY1Fs2bNAAC7du1CvXr1EB0djW3btmHTpk26NY7zaWJry2bTrF7NApdyJyXz8gLMzVl0zR9/6NrSQpM7MZmbGwscTklhrz0hIao6t7mTk0kkTOI/IwNYuFBX1nPyIkTe9nftj1ltZilymvid8AMAdPi7A2Ifx2Jn750Iuh4E75re2P3lbpgZmQEAWju2LjAxtbmxOfYN2AdHK0fEP49Ht+3d8Eb6Rhe3ySkEItIgrOr333/X+IRjx479KIOKmoyMDFhZWSE9PR2Wlpa6Nkd7BAWxUM7c8yIkEpawLChIGWkLsOQr9esDly8zr+KZM8p5g5z3EhrK8tUAbGZNnz4swlYuZ6OZISFsZNPDgwVCHz/OZuA+ecKO+fdfwMdHd/ZzSg+hcaGYdHgSEtMSFWVmhmbY3HMzetfprSiTkxw9dvRA7ONYxP8Qr1ZSYeXplRh7cCwMDQxx9fur+KzCZ8VxC++l1NbZH4hMJsPJkydRv359WFtb69ocjdD5M1yxAhg/HmjThqVYrlQJGDWKhV16eLARObmchajExrJOHE9Cprf8+ivrcG7cqDr7RRMyM4HPPwdu3mTnmD2bJWPJTUICMGEC4OfHOrMczqeEtuprc3NzxMbGQiKRoFu3bmjVqhWmTJmCpKQk1KpVC1lC0mSO1tF5m6uvbN8ODBjAGgJz8/zbBw8G/v6bZZ0+cqT47dMCMhkQGcn6oqtWsYhbK6v8+2VmApaWzHdtbQ106cIUFG/fVuZt4+gXMrkMEYkR6B/SHzXK1UDE0AicTD6JdpvbIWZ4DJpVbpavrxd1Lwoemzww02Mm2ju3h0c1D0UfMO5JHNwC3JD2Jg1f1f8Km3tshkgk0vFdfnpoWl9r5LR1EvRS34NIJMLdu3c1t1IHlOqGzN+f9TIAFlnboAGLurW0BNLT8+9vbc1q82bNWDpJU9Pis7WEExTE2n2pVFnm4MAaSLmctft530dbtmRTVsqXZ7NzeAZrjjaQyWWITIrEsbvHMDdyLqKGRaFVtVb59otJjoFbgBvCh4SjraRtvu1EhG47uiHsVhg6OHfA4UGH9aLxLtV19gdSpkwZxMXFadw26xqdP0NPT9YBGzKETY8QRtAAoFo1YORIVjmHhbFQlbyplzk656+/gP8rguDqVTZJqFUr4P8J6eHuDnzzjWbnuncP6N6djVvb27M/jypV2ESkixeZRr2hIbuOcH4O51NBW/V18+bN0a5dO3Tt2hWenp44deoUGjRogFOnTqF3794qercc7aLzNldfiYgA2rVj7b0gJSV4OVNSWOW/ejXzcj59yhqCEoq6W81NTAyLyA0PZ+PZrVuzNva774C1a4vdXE4hCI0LRe9dveFd0xtNHJrA74Qfjn51FCtOr0DYrTAE9w2Gb21fhMaFYuKhibiXfk9xrMRagiWeS+Bbm73nHrt7DJ22doKMZFjquRQTWk7Q1W19smhcXxd5SjQ9o1Rn1JRKiSwt1WfDbNKEqFUrIltbtl62rOpnr14soyZHY4KCiEQiZQLS5GSi+fOVP/mCBUSZmURHj7J1kYhl6ASIvviC/9wc7RJ4JZDgB8rMzlS7PeNNBsEPFHglsMBz3H52m0x+MSH4gYKvBReVqYWiVNfZH0iTJk3o6NGjujZDY3T6DF++JDI2VlbCPj6scnZ0VG0j7exYOmWOXjJkiPpXG2EZMqRw53v7ligggKhzZ6KKFYmMjIgsLIgaNyaaPp0oKako7oLD0X+0VV+Hh4eTtbU1GRgY0LBhwxTl06ZNo549e36smZx3wN+bCkAqZZ02Hx/WCQsJUXbi8i7Hj+va2o8i763m5u1bopYtmUvg6FG273//sds2MiK6d083NnM0J+R6CEmWSwh+UCxOy50o5HqIYrvIT0TuG9wJfqD9t/ZTTHIM+QT6kMhPpNiPiGh5zHKCH8jA34AO3z6sq1v6ZNG0vi60pm0ehy9PWqNPiMVK7VohAqtcORaOArARRCHC6NUr9ikSAUZGbB7FTz8Vr70lnN69WVCWEFFbtSowfTpgZsYicadOZbNvzJi8DFq2ZBp5pqZMMmHNGt3Zzil9OFgwHc7Yx+r1OoVyYT91VC9XHT+1YvXAhEMT8DrntZat5GiDuXPn4scff0RYWBhSUlKQkZGhsnByERHBKl5DQ5Z5Y/duVjnfucNCH+7lEwAAv65JREFUTLZuZZWzmRkLv+ToJZs2vctly7YXBiMjYNgwlnMmNZX9iWRkAOfPA/Pmsfacw+F8OG3btsXTp0/x9OlTBAhJngCMGDECa3koH0cX5M7Y1bw568jVrs207ry82D5C3p4SLvCqLjlZZiawYAELJI6JYS6BDh3YjJInT1hO1pwcYNEiXVvPeR9CsrKjXx2FrZkt3Kq44eaYm/Ct7QuZXIZJhyeha82usC5jDSdrJ3hW90SLKi3U6tyObT4WQxsOhZzk6BfcD3ee39Hx3XHU8UFO2y1btsDV1RWmpqYwNTVF/fr18ffff2vbNs6HMGMGE1QVph09f86ctefPA02aMAduxYrKjFgvXwLdurHvS5aw+f0cjfH1BZKS2BRLITnZ4cPsPQBgjd+kSUz7/ssv2WP59lu27aefmN+Aw9EGHtU8ILGWYH7kfMhJrrJNTnIsiFoAJ2sneFTzeOd5prpPhaOVI5IzkjE/cn5Rmsz5QLp06YLLly+jW7duqFKlCmxsbGBjYwNra2vY2Njo2jz94vBh9imVslE1IRmZWMx6KAMHsrYvMZFNkeRwOByOViAinD9/Hn/++ScyMzMBAMbGxjATohk4nOLG1xfYuZPp1BGxkbsRI4AbN1gAkzACePSoapLvEkje5GSWluw1SCRiztvMTOa8dXVl/da2bdlxf/3F1CI4+o3YQIz2zu2x1nstYu7HoNeuXohJjsGh24eQmJaItKw07Ivfh8WeixU6tgYiA0xzn4aEtAREJrF3XpFIhDVd16B55eZ48eYFeu3qhawcrjmubxTaabt06VJ8//336NKlC3bt2oVdu3ahc+fOGDlyJJYtW1YUNnIKg1jMQjhzNzR2dqrRto8esdpa4MgRZSrlsWOBPXuKz95SgLExk0CKj2frb96oH80UcvSJRExn6PVrYPRo9s7A4XwsYgMxlnguQditMPTY0QMxyTHIzM5ETHIMeuzogbBbYSoNd0GYGZlhWSdWl/8W/Rvupd175/6c4ic8PFyxHD9+XLEI65xcCE5bAHjxgiUiiYhgOnYC9eqxT95L4XA4HK1w7949uLq6onv37hg9ejSe/H+m38KFC/Hjjz/q2DrOJ42tLRvIXb2aZeIKD2edOF9foGNHNvNGJgPWr9e1pR+Nry9LLnb0KLttNzeWzkaYDdqiBZuA5O0NbN7MJh5lZwPcpVNy8K3ti+C+wbj6+CrcAtzQdXtXAEByRrJC31ZAJpfhRdYLAEzPVoi2LWNYBiF9Q2BrZovLjy5j3MFxxX8jnHdTWN0FiURCmzdvzle+adMmkkgkhT1dsfPJ6Pzs2kVkYJB/FmHTpkQnTzIht9z6t7NmEY0Ywb6bmRGdP6/rOyhx+Pvn/7nNzJTatuvWKWUVV65Uyizu2qVryzmlCXU6R5JlEvKP8KfAK4EUnhBOUpn0neeQy+XUdlNbgh/o5+M/F5Pl6vlk6uxSjM6e4YMHBc+ol0iUGrbR0awsPLx47eNwOBw9Q1v1dffu3WnQoEGUnZ1N5ubmdOfOHSJiWrcuLi7aMJVTAPy96T0EBrI2P1N9Dgjq1Ytt9/EpXruKkPBwdksxMeq3C69BQm4WS0si/udTspDKpBSeEE4zj80k+IGi7kWpbFfbP1wuUdG3PXz7MIn8RAQ/0JZLW4r7Fj5JikzTNiUlBW5ubvnK3dzckMKjVPSHPn3Y6KGAjQ3QuTOwfDkwbhxw8CATcBOYOxf44gugUycWAtqtG486KiQzZgASCRulzDuaaWYG7N3LtnftCixdqpQQHj9eKTHM4Xwsgs5R+JBwBPoGwr+tPyACZkfMxoDQAWi3uR1cVrogNC60wHOIRCJ81+Q7AMDmy5vzyS1wdE9kZCQGDRoENzc3PHjwAADw999/IyoqSseW6REREcrvpqaAuzuQnq46HzA4mE2LcHICPN4tHcLhcDgczYiMjMTMmTNhbGysUi6RSBRtFoejExz+n9shVn0OCDRowD4vXSoWc4oDoUsvTCzKi1BerRqT+c3IKBWBxp8UYgMx2krawq+tHyTWEiw8uVDRfwuNC0XvXb1Rz64e3Ku6w9HKEVHDouBq54reu3or+oQdq3fErDazAAAj943E9SfXdXY/HFUK7bR1cXHBrl278pXv3LkTNQRRT45+0K8fWwA2LfTgQdZpPXeOzdsHgCFD2KdczkRXBw5ktfWDB2xORXa2bmwvgQii74IcwtChTCohJoblt9m7l4nBd+4MJCQw/4CTE/DwIZ+GwtEuQsNtYmgCvwg/uNq5ImZ4DDKnZSJmeEy+RlodPT7rASsTKySlJyE8IbwYree8j5CQEHTq1Ammpqa4cOECsv9fT6enp2P+fK5DrODYMfbp7Axs2cLkgQYNYrG2f//NRtaGDGGZOhYvZpU4h8PhcD4auVwOWW4Zmv9z//59WFhY6MAiDuf/eHiwKJr581n/F2ByCBERwLZtrMMGAMnJTFugFPA+P7VQXrmyMqf58uUlXtb3kySvXF7UvShMPDQRraq2Agg4mXwSSzstRatqrdQmJvu59c/o4NwBr3Neo09QH56UWl8obAhvcHAwicVi6tSpE82ZM4fmzJlDnTp1IkNDQwoNDf3g0ODi4pObMiKVEllY5J8aamvLltxlxsZEdnZEcXFE1tasbNQoXd9BiWP8+Pw/t6Fh/rLx44l27GDfzc2JUlN1bTmnNCGVSUmyXEI+gT4kk8tUtsnkMvIJ9CGn5U7vlEoYuXckwQ80KHRQUZtbIJ9cna0BDRs2VMgU5Z52euHCBapYsaIuTVOLzp5h5cqsgv3tN7YeEsJkEfJWxv7+xWsXh8Ph6Cnaqq/79u1L3377LRGxduru3buUmZlJX3zxBQ0dOlQbpnIKQG/em+7fJ1q2jKhjR6KqVYmMjIgqViTy9SU6derDzpmTQxQQQOTlxc5lZMTm8jdtSjRjBlFiombnCQlhenU+PkwTwNFR9b3AyIh9Ll36YXbqGVIpe/3x8SGSqXYJSCYj8vYmsrcn2rqV6NAh5g4AiLZv/7Dr/f03U11s0kQpB7hx44fbr63H/imhTg7BabmTihwCEVF0UjTBDxSeEK4oe/TyEdkvtif4gUaFFa8v6Mz9M+S11YusFliR2Twzar6+Oe2M3Vmoc2y8uFHlvvMuue9VU+6n36epR6ZSo7WNyGqBFRnNMSL7xfbUZVsX2nhxI2VLswt9TqIikEeI/f8QTK9evXD69GlUqFABu3fvxu7du1GhQgWcOXMGPXv2LAK3MuejEIsBQfDfyYl9DhkCPH2qzKItRNtWqgQ8fgzs2sVGGgHgjz+AoKDitbmE0707+1y9mkkfiEQsujYmhiUoW7eObV+xAjA0BD7/HHj5EvjlF52ZzCmFRCZFIjEtEdM9psNApFrVq8seqo5hjYYBAEKuhyD9TXqR2svRnJs3b6J169b5yq2srJCWllak1/71118hEokwfvz4Ir3OR5OaymaMAGwWCaDMyBEezuSD9u1j5XyWEIfD4WiVJUuW4OTJk6hTpw7evHmDAQMGKKQRFi5cqGvzOMXBypXAhAnA3buApycL4XR3Zwmv3dyAnTsLd75794CmTYGvvwYuXGBJw378kU1tLFMG+PVXoFYtzaJjfX2ZPNKpU8D06ezcAODoyCSTatZk65s2Fc5GPUWYDRoWxmZ9Cn3SmBigeXNWnprKJiN16gS8fcuOW7nyw643cybr7967p4zy/VC0+dg/JQS5vJkeMwEA+wfsR/wP8SqJyQCgdoXaAFhfLyIxAjK5DHZl7bC5x2YAwB/n/sDem3uLxebwhHC0CmiFqKQo9K3bFyObjETqy1T0C+6HJdFLCn2+7rW6Y3ab2fkWibWkUOfZfnU7aqysgV9P/goDkQEG1R+EyW6T0cWlC64/uY5he4bBa5tXoe0rFJp6gUUiETVr1ozWrVtHGRkZH+RJ1gf0ZvSxOJFK2ZCZMGr4rmhbIXtWdjbRtGls3cqK6N49Xd9FiUEYzfT2zj+qKZOxdWG7kxPR0aPsZzYxYXlzOBxtEHglkOAHysxWn2gh400GwQ8UeCWwwHPI5XKqs7oOwQ+07ty6ojL1nXySdfZ7cHJyoiNHjhCRaqTt5s2bqXbt2kV23TNnzpBEIqH69evTuHHjND5OJ88wKEjZpr0v8wZPQMbhcDhEpN36Oicnh/7++2+aPHkyff/997R+/Xp6/fq1FqzkvAu9eW8KCSGKiMhf/t9/rE9qY0P05o1m58rIIKpVi7XZkyerPy4+nnWyLl7U7JxCh83NjYWYhoezMiKia9eU7xC6/h21SEETjpo2Za9KmZnss2NH5bZz5wp/nSNHlNGvCxZ8eKRtUTz2T43whHCCHygmOf+7cMj1EHJY7FBgcrIJBycQ/EAVFlWghxkPi9TOHFkOVV9RnUx+MaGLKRcV5WlZaVRzZU0y/sWYEl9oFlItRNpuvLjxo+06EH+ADPwNqNzCcnT49uF82+VyOYVeD6Uu27p80Pm1Hml74sQJ1K1bF5MmTYKDgwOGDh2KyMiCI7Q4eoRYDKxZoypMkzfa1tZWue31a6BiRaBhQzb8lp7OhrfkPBmRJuQezUxMBHx8WKKxmBg2uhkWxrZPn860bcViJq+UnQ389puureeUFhws2NB27GP1AlZCubCfOkQiEYY2GAoA2HR5k1bt43w43377LcaNG4fTp09DJBLh4cOH2LZtG3788Ud8//33RXLNly9fYuDAgVi/fj1sbGyK5BpaRUjIZmGhqlsnIJfzBGQcDodThBgaGmLQoEFYtGgR/vjjD3zzzTcwNTXVtVmc4sLXF2jTJn+5hwfQrh3Lt3L1qmbnWrwYuHmThYIuWgSYmOTfx8UF+PdfoE4dzc4ZGck6akuWsJwubdsqte1r1wbs7dn31as1O18JIPeEo61b2S16ewOnTwMtWgDm5uzz4EGmbwuwmaGFpUMHFrT8sRTFY//U8KjmAYm1BPMj56sklhaSk8lJDgdzB6RNScuX92RB+wVoULEBnr5+iqF7hhZpYurjCcdx58UdDHAdgIb2DRXlVmWsMN19Ot7K3mLz5c1Fdn11yOQyjN4/GnKSY1fvXehYvWO+fUQiEXrW7onQvgXnidEGGjttPTw8EBAQgJSUFKxcuRIJCQlo06YNatasiYULFyI1NbUo7eR8LL6+TPZAcNJu3swG0ORyNn+/eXOgVSugQgW2/eVLlsRs0CDAzIwldOFpJDXG15dJIwDAiBGApSWbCXT1KuDnxxy0L16w7SkpbBoJAPz5J1Oo4HA+loIaaQCQkxwLohbAydoJHtXe7bD6qsFXEIvEiE6Oxs2nN4vSZI6GTJ06FQMGDED79u3x8uVLtG7dGt988w2+++47/PDDD0VyzdGjR6Nr167o0KHDe/fNzs5GRkaGylLsxMSwT09PNlLm4cEcucJ8QGEEjScg43A4nCIhPj4e69atw9y5czFnzhyVhfOJY2TEPg0NNds/IIB9zpr1/n2NjTU7Z0oK+6xXL/82kQjo0oV9j4jQ7HwlBLGY+acrV2aSCDNmKN0DAgYGwOzZ7PuOHco+a3FTFI/9UyNvYrKY5BikZaVhzP4xsCtrh0evHmFVl1WwKmOFFlVaqCQnMzQwRGCvQJQxLIPDdw5jzdk1RWZnRGIEAMCzume+bZ1cOgEATtw7UahzXky5iCXRS7AwaiF2xu7Es9fPCnV8eGI47r64C7eqbmjv3P6d+5oYqhlR0CIaO20FypYti2HDhuHEiRO4desW+vTpg9WrV6NatWro1q1bUdjI0RZ9+qjWehMmAKamzFmbksIyaz99yrZJpWw465dfgLlzWdnkycDDh8Vvdwklt7ZtYCDg78/WZ88GBgwAunZl6/HxTJ/n88+BrCzg9991Yy+ndKGukc7MzkRMcgx67OiBsFthWOy5GGKDdzus7M3t4VWD6fRsubylOEznvAeRSIQZM2bg+fPniI2NxalTp/DkyRP8UkTC2Dt27MCFCxewYMECjfZfsGABrKysFEvVqlWLxK4CefMGOH+efQ8JYQOU0dHMcSuMoMXGMj07X993n4vDyUNqKpND/v134M4dXVvD4egn69evR+3atTFr1iwEBwfjn3/+USy7d+/WtXkcXZKUBBw9ysROXV3fv/+9e8D9+0CVKtrVoBfEVmPVz0iDiwv7jIvT3jX1iHf5rAEWuwWwibpbdPD6X1SP/VPEt7YvgvsG4+rjq3ALcIPNIhukvEyBgcgAIX1DVHRu8+Y9qWNbB4s7LgYA/HT0J8Q/iy8SG+Ofs/PWKJf/Ydub28Pc2LzQ1/79zO/48ciPmHpsKr4M+RJVl1XFwijNNdVPJp0EAHwh+aJQ1y0KCu20zY2LiwumT5+OmTNnwsLCAvuEpB4c/WXmTNZpBYBly1jDGRXFOrhNmjCBeKGD7ejIwj7T04FmzViE0sSJurO9hOHhAUgkbIqJkRGLsHV1ZUFe6enspzYzY+X//ANMncqO++MPFujM4XwseRtpy18t4RbghtjHsQjuG5xPjL4g+tVlb24H7xwsSnM5hcTY2BgWFhZwcHCAubl5kVwjOTkZ48aNw7Zt21CmTBmNjpk2bRrS09MVS3JycpHYViBLlwIyGQu7iI5mbVdUFNCyJdvu789Gy7jDlgP2pxIRAWzfzj5lMvX7vXnD/nQkEjaddNw4NtbN4XDyM3fuXMybNw+pqam4dOkSLl68qFguXLiga/M4uiInB/jqKzblcOFCzWa6CLN5q1TRri1CRy23hJLQIGzbxjpnAJCczJZSxvt81teuKb//+Scb/y5Oiuqxf6oIicnCh4RjzOdjAABxo+Py9QVlchleZLHQ6mN3j0Eml+H7z7/HF05f4HXOawzdMxQyeQEvSh+BkPDaqoyV2u2WJpZIz9YsKbaTtRNWeq3ErTG38Hr6a9yfcB9bemxBOdNymHpsKlae1izDXupL9kdYxVL3f4Qf7LT977//MHToUNjb22Py5Mnw9fXFSf72qv+IxSx7J6B0ztrZsWhbBwfWse3EQtAxdCj7/OMPthgYsEyfJwoXmv6pImjb7t0LDB7MfuK//2aN3qBBrLO3eTPr/P34I/t0cWFTUITpIBzOx5K7kQ70DUT4kHC12UPfRXsnNiXkYsrFQk8t4WgfqVSKn3/+GVZWVpBIJJBIJLCyssLMmTORk1u7XAucP38ejx8/RuPGjWFoaAhDQ0OcOHECv//+OwwNDSFT4+EyMTGBpaWlylJsyGRM8gAAOndmjlpzc1YBR0UxkfFSkg2a8/GEhrJ2t107NgOmXTu2HppHmkwuZ4oagrxRnTpAr15KyUMOh6PKixcv0KdPH12bwdEn5HLWt/zvP+Dbb5nzVpfkTkLSowfTua9enTUEgwYBZ88q5RuOH9epqUWBOp+1gCD77+jIJuXGxSlTBXBKLmIDMdpK2qJXnV4AgLinqlHkoXGhcFnpgq7b2XTguZFz4bLSBbtv7EZAtwBYGFsgOjkaS2OWFrvthaGNpA3GNBuDGuVrwNTIFJUtK+OrBl/h0KBDKGNYBn4n/CCVS3VtZqEolNP24cOHmD9/PmrWrIm2bdvi9u3b+P333/Hw4UOsX78eLVq0KCo7OdpkxgzmqH30iK0/fsw8iGfOsIRkf/3FyqdPZ59PnrBIpe++Y+sTJvCkZBri68sic7KyWGNnba06M7d3b2DaNJaQLDpaGci8bBlTqOBwtIHQSPd37Y+2krbvlUTIi4OFA+ra1gWBEJ4YXkRWcjTlhx9+wLp167Bo0SJF5NKiRYuwYcMGjB07VqvXat++Pa5evYpLly4plqZNm2LgwIG4dOkSxPqmBxsZqRRfa95cdZuBgbLC5YlUP3lCQ1kbLMyAEeSOXV1ZeW7H7eLFwKFDbHbMzp3KNtwzv/Qah8MB0KdPHxw+fFjXZnD0BbmcJbUODGQO0bVrNT9WGB178ED7dvn6ssr81CnW7713j5U7OjKvpUTC1jdu1P61dUxen3XudlCQ/V+6lA1oAkr3QHFRlI/9U0dd3hMhMVk9u3pwr+oORytHRA2LUiQmO59yHss6LQMAzAyfiWuPr73rEoVGiLAVIm7zkpGdASsT9VG4mlLXri7cq7njedZzxD15v+yJvTn7I3yQqfs/Qo2dtl5eXnB0dMTKlSvRs2dPxMXFISoqCsOGDUPZsmWL0kaOthGLgTVrgLdvlSOIkyYx56ygRL5pExO5EYTi//mHeR8tLYGLF9k8Qo5GCDo8+/ezd5XwcNWZuYKWUEoKG4AuX54lM92/XxfWcjjqEaJtj909pmNLOIGBgdi0aRO+++471K9fH/Xr18d3332HDRs2IDAwUKvXsrCwQL169VSWsmXLonz58qhXkBCaLhFE2gAmFJ6X3BUu55NEJmO5VUeOZIHYISGqWbN371bOgJHJ2CvPjBns2N9/B/r2ZTlqOBxOwbi4uODnn3/G0KFDsWTJEvz+++8qC+cTQi4Hhg1j0wv792d9zLyZr96FoyPLmpWczDpQ2qZ7d6BsWRZVs3Ur66jducN061atYvtERZXKaBrBZ331Krt9dbL/X3/N9g0OBoozr2xRP/ZPmbx5T6LuRWHioYloVbUVQMDJ5JNY2mkpWlVrpZKYbEiDIehSowveyt7i63+/1qpMgqBlK2jb5ib1ZSpevn2JGuU/Xty4glkFAMCrnFfv3bdVtVYAgGMJuu/7alxjGhkZITg4GPfv38fChQtRq1atorSLU9T4+rKeiuBwX7KEzds3MWFDat9/z2psYart6tVMFmHKFLY+e7ZyG+edCJpBNjbsXaVtW+Y3F2STlv5/hoGdHZuCMnw4W//jD11Yy+Gop4NzBwD60XB96piYmEAiRH/kwsnJCcafevrc3IPITZrk3y6ItwkVM+eTQpBD6NCBjVNHRwM1a6pG1eYNyJ4yhfXVe/VSdl45HM67WbduHczNzXHixAmsWrUKy5YtUyzLly8vsus+f/4cAwcOhKWlJaytrTF8+HC8fE+iiDdv3mD06NEoX748zM3N0atXLzwSZiP+n6SkJHTt2hVmZmaws7PD5MmTIc3jxIuIiEDjxo1hYmICFxcXbMojxfPff//Bx8cHlSpVgkgkUpuQjYgwa9YsODg4wNTUFB06dEB8SfZYCQ7bLVtYZqu//9ZMxzYvQudISI79Lt6+Ldy5IyNZtMySJcDAgcqOGsA0BIRO265dhTtvCcHXF7h9m/mqheCiGzeAcuVYjFZ2NmsnX78u/p+gKB/7p07uvCcemzxwL/0eopKjcO3JNZW8J7kTk0UlR2Gd9zpYmVjhzIMzWHF6hdbsaePYBgBw+E7+GRqHbh9S2edDkcllOPfwHADA0crxvfu3k7SDs40zopOjEZ7w7pmm2dLsj7LtvdAnRnp6OgGg9PR0XZuiH2RnE5UtS8RctsrFzIzol1+ImjUjMjdXlm/dSmRnx75v2KBr60sEUimRRELk40Mkk7GykBBWlvsnl0hY+Z07RCIRK4uP163tHI5A+pt0EvuLCX6ge2n3iu+6vM7Oh7+/P/Xv35/evHmjKHvz5g0NHDiQ/Pz8dGiZeor1GR44oGzDhApXQCZjFbGTE6uYOZ8EUilReDjR+PGsbfX2JvL3Z38mx46xPwmRiLW/AhkZbPvPP7NPQ0Oiu3d1dgscTrFR0tvczp07U4MGDejUqVMUGRlJLi4u1L9//3ceM3LkSKpatSodO3aMzp07Ry1atCA3NzfFdqlUSvXq1aMOHTrQxYsXaf/+/VShQgWaNm2aYp+7d++SmZkZTZw4ka5fv04rV64ksVhMBw8eVOyzf/9+mjFjBoWGhhIA+ueff/LZ8uuvv5KVlRXt3r2bLl++TN26dSMnJyfKysrS+DfQm2cokxENGcIq0T59iHJyPvxcGRlEtWqxc02bRpTr/UfB3btE3bsTXbxYuHMHBrLzZmaq3968Ods+cmRhrS6RqOuj2tiwz1z/FhqzYAE7duPGwh9blI+dw5DKpDTz2EyCH2j/rf0kleV/P854k0HwAwVeCSQiovXn1xP8QKZzTSn+mXacFTmyHHJe4Uwmv5jQxZSLivK0rDSqubImGf9iTAkvElSOeZjxkOKexFFaVppK+bkH5/KdXyqT0o+HfiT4gdptaqexXQfiD5CBvwFVWFSBjt09pnaff2/8S96B3hqfMzea1tfcacthPRmAOWitrdnnL78QmZqq1tjGxsxhu3AhW3dx4R1fDQkJYZ1CHx+i+fPZd3d3tgCsQcvdcezShZVPmqRryzmlEalMSuEJ4RR4JZDCE8LVNtDqaPFXC4IfKOBCQBFbqITX2fnp0aMHWVhYUIUKFah9+/bUvn17qlChAllaWlLPnj1VFn2gWJ+h0D4BRI0bE61aRfTiBVF0tHrvHKdUo67z6eiodNrGxKj35UdHs+31639SfXUOR2v19dWrVwvcps5ZqQ2uX79OAOjs2bOKsgMHDpBIJKIHDx6oPSYtLY2MjIwoKChIURYXF0cAKCYmhoiYs9XAwIBSU1MV+6xZs4YsLS0pOzubiIh++uknqlu3rsq5+/XrR506dVJ7XXVOW7lcTvb29vTbb7+p2GdiYkLbt2/X4Bdg6M170+zZrAI1NyeaMYOt510K42lLTCRq0ICd096eaPBgounTWT+2TRs2ulamTOEjXsLDlQ2COgYNYtu9vAp33hJI7v5qTAzzY8fEEHXsqGxDb9x4/3nWr2f++iFD2KsYQNSqlbJs/XrNbSqqx85REp4QTvADxSSr/x+IToom+IHCE8KJiNVV7Ta1I/iB2m5qS3K5XCt2HL97nIzmGJHFfAv69t9vaeLBieS4zJHgB1p8cnG+/Yf8M4TgB9p4caNKOfxA9dfUp0Ghg2jKkSn07b/fUs2VNQl+oCpLq9Cd53cKZde2K9vIdK4pwQ/UdF1T+mH/DzT96HQavmc4VV9RneAH6rClwwfdc4lw2v7xxx/k6upKFhYWZGFhQS1atKD9+/drdOz27dsJAHXv3r1Q19SbhkyfEBorYRgtd7TtvHlEixcrQ0EB1vCWL8++79qla+tLDCEhrLOY+yd2clL6D3J3HP/5h22vWJH7xTnaJeR6CEmWSwh+UCyS5RIKuf5+R5YwEjswZGAxWMrgdXZ+hg4dqvGiDxTrM2zVKv/MEXUVLqdUIkTVBgYyx6zQ+Vy9mv0JrF/P1gE2Bi3MgBGctOHhyrbY3p6VmZgQJSfr+s44nOJBW/V1pUqV6K6a8PTg4GAyMzP7qHMXxIYNG8ja2lqlLCcnh8RiMYWGhqo95tixYwSAXrx4oVJerVo1Wrp0KRER/fzzz9SgQQOV7Xfv3iUAdOHCBSIi8vDwoHHjxqnsExAQQJaWlmqvq85pe+fOHQJAF/M4Mlu3bk1jx45Vex4iNtMmPT1dsSQnJ+vHe5MQZfuupbDhl2/fEgUEEHXuzDpJRkZEFhbMMzh9OlFSUuHtVDclUkAmY8FMQrBSKeZ9P4Mw0Xb69Pef632PfsiQwtlWFI+do0Qqk5JkuYR8An1IJld9+G+lb6nlXy3JdpEtHb1zVBHoc/vZbYUjc925dVqz5fT909R5a2eyXGBJpnNNqdn6ZrTj6g61+xbktJ10aBK12tCKKv5WkYzmGFHZeWWpwZoGNPPYTHr++vkH2XU//T5NOTKFGq1tRJYLLMlwjiFV/K0idd7amTZe3EhvpW8/6Lwlwmn777//0r59++jWrVt08+ZNmj59OhkZGVFsbOw7j0tISKDKlSuTh4cHd9pqA6GWbtSI1aQVKrD5Dzt35g9RAYgsLYlmzmTfP/+cSEujK58CR4+yn83fn3UO8zpkhY7jkSNKv/iRIzoxlVMKCbkeQiI/EfkE+lDM/9g78/iYrvePfyaTRSKyIJslEmJPLLUlqSgVYokmjVSpokr7pbRVsUX7ragfUV+KorRadAslgoraJQRjp4SUIMSSCCKZSCSSmfP74/TOkswkk21mkjzv1+u+bu6559557p3JPfc85zmf556E5RTkMMk9CRsWNYyJIkRlOm6FkVjnpc5VNqpaFvTMrvno7Tvcvl3ZTi1fzlhWFvfWCWEeNMhYq9EUVWtpydi2beqzXwWnrKOj0ql78CDfHxGhDMhu1YqXTZtm6CsjCP1RVc/rL7/8krVs2ZKlpaUpyrZs2cKsrKzY1mp6Fi9cuJC1adOmRLmDgwP77rvvNB7z+++/M3Nz8xLlPXr0YLNmzWKMMfbBBx+wgQMHqu3Pzc1lABTBRq1bt2aLFi1Sq7Nnzx4GgOXl5ZU4vyan7YkTJxgA9vDhQ7Xyt956i40YMUKj/YwxNm/ePAagxELvTeVANcT05Ek+Q2f1auX7g7A8emRoS6uNsgKOhRkqTk4lnbpEzUe1j3gy9SST5kvZomOLFI5ZTYE+35z8hiECzCbShj2UPizjEwhN6NrmliN1Y9UzbNgwDBkyBK1bt0abNm2wcOFCWFtb49SpU1qPkclkGD16NObPn4+WLVvq0dpajFjMxdcvXuTbT54AAwYAI0cCL17wst9+A374gf8tlXK1bwsL4OxZ4PRpw9hdA8nI4Ovp09V17gWExOaPHwOhofzvzZv1Zh5Ri5HJZQg7EIbANoHYOXInvJt5w9rcGt7NvNUyg5aWCdSnmQ8sTS2R/jwd1x5f06P1BFEGMhl/sAq88w5gawt89BFvp4YN4xmlZFWX6ZYwPEJCz88+422mpycgkQB//cX3d+sGjBihzDydmKhMNJaRAURE8KzZAwbw/RERvM7MmTx5eP36vC5BEOVj/vz5GDJkCPz9/ZGZmYmoqCiMHz8ev/zyC956661ynWvOnDkQiUSlLv/88081XYnxEx4ejuzsbMVy7949Q5tU8wgJAaKjeYPg68uzR0+dCly4wPebmfG1RGI4G6uZtDS+FvqixZk0ia8fPQJOnNCPTYT+UE1M5rvBFzaLbTD3yFyIIEJk/0jkhOdAMkECL0cvhG4NRUxSDD7p9Ql6NOkBaYEU0/ZPM/Ql1GoM6rRVRSaTYcuWLcjNzYWPj4/Wel999RUcHR0xQUgnSFQNISE8JaTJvz+J+fP5mKKlJbB9O+/1bNwINGrE92/YwLOAAsCqVYaxuQYiJCwXEpgXRzWx+ahR/O/t23nmToKoDAmpCbiTdQdz/ebCRKT+6FfNDJqQmqD1HBamFvBr4QcAOHT7ULXaS2jn6dOnmDJlCjp06IDGjRujYcOGakudJCEBuHuX/+3sDDg6KvcJXrqUFF6PqBXExAAeHkC/fsCKFfyV5coV4OFDICuL19mzBwgM5K8vbm7AokU8mbnQKW3dGrhxA/DxARwcgEOH+DmEpO7Tpqn/lAiC0J1Vq1ahc+fO8Pb2xgcffIDNmzdj+PDh5T5PWFgYkpKSSl1atmwJZ2dnZAjREf9SVFSEzMxMODs7azy3s7MzXr58iSzhofEvjx49Uhzj7OyMR48eldgv7Cutjo2NDSwtLXW6TuFcms6jzX4AsLCwgI2NjdpCVICQEOB//wNEIuCVV4A1a3hjIpEoO3CbNhnSwmqlrD7qrVvKv3/9tfrtIfRPSPsQ3Pz4Jg6NOQQHKwf4NvNF1pwszOk9R2OgDwD8MOwHiEVibL26FX8l/2XgK6i9GNxpe+XKFVhbW8PCwgKTJk3Cjh070KFDB411jx8/jp9++gnr16/X+fwFBQWQSqVqC6GFt94CvvxSuR0WxqNvr1/nEUsSCfD0Kd/35Al36AJ8ZPLJE/3bWwPx81PvOALKSKHff+e33M2N1/PzA5o2BbKzgX37DGg0UStIy+FD6J6OmofQhXKhnjb6u/cHABxOOVyF1hHlYcyYMTh48CDGjRuHpUuXYvny5WpLnSRN5XfbqVPJ/YKXLq303zdRM4iJ4ZG1Xl68Xw0A69fzrz40VBlZe+0a99ffuQOMHw/ExgLBwcoZLJmZwPDhwKlTwLp1QP/+wNdfc0euiwuPuCUIQjf+/PPPEktISAjy8/MxatQoiEQiRXl5cHBwQLt27UpdzM3N4ePjg6ysLJw/f15x7JEjRyCXy9GrVy+N5+7WrRvMzMxw+LDyneb69etITU1VBBH5+PjgypUrag7hgwcPwsbGRtFn9fHxUTuHUKe0QKTiuLu7w9nZWe08UqkUp0+fLtd5iAoik/GHfmAgn6Hz0Ue8/+vtDcybx+vs3VtrZ+xo6qMKyOVAZKTSsbttGwUU1VbEJmKITcR4nPcYywKWwUxspra/eKBPF+cumOY9DQDw0Z6PkPsy1wBW1wH0JNeglYKCApacnMzOnTvH5syZwxo3bsyuXr1aop5UKmVubm5qicrGjRtXpqYt6fyUk6IirllbXMdWSEo2eLAyO4dIxFjLlvzvZcsMbXmNQVU2adGiksnJHB2VuXKmTuVlkycb1GSiFlDezKDaOPfgnEK/qLhYfXVAmrYlsba2ZpcuXTK0GTqjl+9QEGMDGAsLK7lfNdMUUaMpnixFk16tm5uyTlYW3x8VpVn3VjU/3ZUrPMEJwFh0tGGvkyAMQWWe1yKRSKfFxMSkGiznDBo0iHXt2pWdPn2aHT9+nLVu3ZqNGjVKsf/+/fusbdu27PTp04qySZMmMVdXV3bkyBF27tw55uPjw3x8fBT7i4qKmKenJxs4cCC7dOkS27dvH3NwcGDh4eGKOrdv32ZWVlZs5syZLCkpia1Zs4aJxWK2b98+RZ2cnBx28eJFdvHiRQaAffPNN+zixYvs7t27ijqLFy9mdnZ2bNeuXezy5cssKCiIubu7sxcvXuh8D+i9qYKUJuqalKRsNGpxspHi0r5SKV8LWu/btjHWpAm/Dbt2GdpaorqIuhzFEAGWU5Cjcb80X8oQARZ1OYoxxlhOQQ5zXe7KEAE288BMfZpa46kRicg00b9/f/bhhx+WKBcaOLFYrFiExl8sFrObN29qPJ/RZtQ0ZgSlcU9PxuzseNbMo0cZCwzk5W+/zdc+PjxpmVCXEpLpzPbtjDk4qHccW7RgLDJS2TBu384bxDqQsJTQA6VlBpXJZWxY1DDmvsJdkRVUG4WyQma10IohAuxaxrXqNJkxRp0PTXTv3p1JtGWKMEL08h0WFTFWr57mbNSCJ8/dvWT2R6LGUbxfXXxb8M/Pn8/b0t69+faePXyf8CozbZp6QtCcHMbat+f7hg2jVxqiblLT29ynT5+yUaNGMWtra2ZjY8PGjx/PcnKUjoeUlBQGgMWpDOC9ePGCffTRR8ze3p5ZWVmxN998Uy2BGmOM3blzhw0ePJhZWlqyxo0bs7CwMFZYWKhWJy4ujnXp0oWZm5uzli1bso3F2qK4uDiNgUTjxo1T1JHL5ey///0vc3JyYhYWFqx///7s+vXr5boHNf07NBiqI4DFkcmUQU3FEs7VNsoa3Jw+XekOIGonFQn02X19N0MEmHi+mF1KqzmBJYZG1+e1iDHG9BfXWzavv/46XF1dsamYZkx+fj5u3rypVvbFF18gJycHK1euRJs2bWBubl7m+aVSKWxtbZGdnU2aP9qQyYAmTbiOz8uXynJTU6CoSLnt4sKnmpqZAYWFwLlzPOsHUSYyGdfia9KEz75p2pRPSxGL+RSU4GCuKXT+PNfZk8m4HKObm6EtJ2oyMUkxCN0aisA2gQjvHQ5PR08kZiQi8ngkYm/EInpENELah5R5Hr+Nfjieehw/B/+MsZ3HVqvN9MwuydmzZzFnzhx8+eWX8PT0hJmZ+tQlY7tPevkOGQNsbIDnz4E+fYDFi7kkQmIin9MXG8ulfELK/n0TxolMxiWJt28HVq/mryi2tsr21MuLa9Hm5vKfQlQUfz0ZO1aZUxUA3N2BpUvVfwqMAaNHc9mEJk24MhRp2RJ1EWpzaz70HVaQ+Hguki6RcEmE4vTowfu6n30GfPON3s3TJ0J7m5bGu/tCHxXgt6BHD66SmJEBWFsb1lai6pHJZfBY5QEvRy/sHLlTLReKnMkRtDkI5x6ew9KBS9HUpin8XP0gNhEjdGsotidth29zXySMTyiRQ4Uoia7Pa4PeyfDwcBw7dgx37tzBlStXEB4ejvj4eIwePRoAMHbsWIT/m7a3Xr168PT0VFvs7OzQoEEDeHp66uSwJXRELAbWrlU6bENCuCh7r17Aq6/ysrffBpo353+7u/P1b7/p39YaSkIC19hbtox3FPv2VTaGqjlz/v6b33YAOEwSokQl0ZQZ1HeDLxIzEnV22AJAzyY9AQBnHpypTnMJLdjZ2UEqleL111+Ho6Mj7O3tYW9vDzs7O9jb2xvaPMOQkcEdtgBPSObryz13vr7ccUsO2xqNatKx1at5Wbt2vFws5m2pJr3aX37hDtv587kTNy6O690W/yl8+SU/ztSU52Qlhy1BVI5PPvkE3377bYny1atXY9q0afo3iCDKoixR1+xs/ndmpt5N0zdiMe+bjhql3kcFeHxW69a8bd21y1AWEtWJ2ESMZQOXIfZGLIK3BENyT4KcghxI7knQa30vxCbHIj03He/ueBf9fu4Hj1UeiEmKwYpBK1DfrD5O3juJTZc2GfoyahWmhvzwjIwMjB07FmlpabC1tUWnTp2wf/9+DBgwAACQmpoKExPy0BuEkBDey5k3j/eKAODECd6jAYA//lDWffBAWbZ0qfqTndCIkAvHU3NOKLWcOf7+wMmTPKv1hAn6sY+ovYS0D0FQ2yAkpCYgLScNLg1cFCOkutKjaQ8AwNmHZ6vLTKIURo8eDTMzM0RFRcHJyQkikcjQJhmea9f4ukkTYOFC4PFjPk1BdRoDUSMRko4FBnLHavv2fJHLeRKx7dv5K0t0NE/muXs3P27qVD6mLOzXxrffAv/3f/zv775Tjk0TBFFxtm/frjHhmK+vLxYvXowVK1bo3yiCKA1hBDA0lI8Ahoerz9gRZvxevGhQMw1B8cjbt9/m7eYff/DgI6L2IQT6hB0Ig+8GX7V93Zt0x6rBqxQzNhclLELo1lBEj4jG/L7zMePgDMw6OAtBbYPQyKqRga6gdmF08gjVDU0ZKQcyGe/wPnoEjBjBU0UOHQp8/jnQoQP/++xZnj6yXj0gP5+HsfTta2jLjZ6yZuBIJDxALC6Ov0P06QM0bsy/ChrHIAzN7We30erbVjAXmyMnPAfm4uqb6UDP7JJYWVnh4sWLaNu2raFN0Qm9fIcffAD8+KN6mZsb74BRhG2NRCbjbeWoUTyqJz6eyx0ASkeuoyNvE69dA5KSeIBUbCwwbRoQFFS2v/6bb7ijF+Dj1F9+Wc0XRRBGTlU9r+vVq4fExER4eHiold+8eROenp7Iz8+vrKmEFui9qZLExPCG4c4dZZmzMx85/PFH3ujk5ABWVgYzUZ9ouh1Nm/KYLTMzPtHJzs5Q1hHVjUwuQ0JqAh5IH2DGwRno7tIdu0btKiGZELwlGIkZibj20TX0+LEHEjMS8eErH+L7Yd8b0Hrjp0bIIxBGjljMe0sAD1159VUugcAY8O67PPJ2/Xr1Y7Zv17+dNZCyZuBERvIIIT8/Lo9gYQE8eQLcvm0QcwlCDXc7dzSybISXspe4/Oiyoc2pc3Tv3h337t0ztBnGQ0yM0mE7ZgzvTEkkXOQ0NFQ5W4SoMQhyCP7+PGj65EmgTRvlVylE1pqY8Mgfe3s+0Hn1Kn8NWb685JROVRgDvvhC6bANDwf++1+9XBpB1Ak8PDywb9++EuV79+5Fy5YtDWARQehISAiPqo2L4yOAzs5AerryPUMuB1auNKiJ+kIYIPXy4q9VwuvVK6/w/YWFXEueqL2ITcTo69YXTW2aIv15Oj7v83kJrVoTkQnCe4cjJSsFpx6cwtqhawEAP1z4AafunzKE2bUOctoSpRMUxNcvXgDHj/OhNF9f4MoVICJC6UUURsxjYkp6IYkSFNfgU20Ig4N5uaA0YW7Op4ICvENKEIZGJBKhe5PuAEjX1hB8/PHH+PTTT7Fp0yacP38ely9fVlvqFDIZ97w1+nf6lb8/z4rh7c17EoGBwIwZvB5RI1DtJM6fz8sOHy7pgw8JUapiTJ2qXa+2OC9e8OmcCxfy7a++4n+TyghBVB3Tp0/HrFmzMG/ePBw9ehRHjx7Fl19+iTlz5uCzzz4ztHkEUTpiMdeuXbmSZ90SOmqCfs7nn9f6AWHh9SowkL9OeXurv14Jk71UFROJ2ktaDtd29HTUrO0olKflpKG3a2+M6zwOADDlrymQyekdvLIYVNOWqAH4+fF5+U+ecE+iVMp7RRs3cr1bVczMgIcPeVrJnj0NY28NQlWDz1dFKsbdvWTOHE9P4NIlLqsk+NEJwpD0bNoT+2/tJ11bA/D2228DAN5//31FmUgkAmMMIpEIsrrkoBSyOjZsyLfbtVPuE7I6+vryeiTdY/QU7yQeO8bLraz4dnAw98EHBfE+dVIS3z98uG5f7927vG29cIFL9P/wAzB+fPVcC0HUZd5//30UFBRg4cKFWLBgAQDAzc0Na9euxdixYw1sHUGUQfHGSNCm69+fzzRt1ky9MaqFCK9XmzeXlOYzMeGxW6NGAQcOAE+fKsfOidqJSwMXAEBiRiK8m5XUdkzMSFSrt2TAEuz8ZycupF3A+gvrMan7JP0ZWwuhSFuidMRi4OOP+d+LF/Ond0QEb6x69+blk/79Jyws5GshCwhRJqozcErLbC0kJktM1L+NBKGJHk14MjKKtNU/KSkpJZbbt28r1nUKIaujkM25uM6valZHwugROolz5/JOoaqUEMB98CkpvF5xKaGy2L0b6NqVO2wbNwb27yeHLUFUJ5MnT8b9+/fx6NEjSKVS3L59mxy2RM2geGMkIOgCWFgoG6NaSllJs4cO5Wu5HNi1Sz82EYbDz9UPbnZuWJSwCHKmPqtazuSIPB4Jdzt3+LnyFzLH+o5Y0I8P2M09PBdP8p7o3ebaBDltibL5/HOe7ePcOd54McalEu7f5w7cM2cAJyegc2deX0O2WEI7YjGPEBoxgm9v3coTrqgGy5HTljA2ejTlTtukx0nIKcgxsDV1ixYtWpS61ClcXJR/OzkBtrbq+4WHpmo9wiiRybgMAgA8e8a3i0sJ5eby/UePlpQS0kZ+PpclfOMNft7u3fnrzOuvV/MFEQQBAHBwcIC1tbWhzSAI3dHmsezala9TUtTr1UKE1yZtfU/V8ujo6reHMCxiEzGWDVyG2BuxCN4SDMk9CXIKciC5J0HQ5iDsvrEbQe2CkJCaoJBDmNxjMjo5dcKz/GeYe3iuga+gZiNijDFDG6FPKKNmBYmJ4fMPAWDCBN453rhRvbFycgIePeJ/P3gANGmifztrKJoyc6omPr97l2+bmvJOq7m5gQwlCBVcl7vinvQe4sbFoa9b32r5DHpma+bXX3/FunXrkJKSAolEghYtWmDFihVwd3dHkJFpqFTrdyiT8Z7F48d89odq1Itczj17iYl8CkMtncJYGyirDdS0392dO2xL07C9dInnphM6l59+CixZQm0oQWijMs/rV155BYcPH4a9vT26du0KUSlC0RcuXKisqYQW6L2pCoiPB/r141q23ipTwRkDHBy4HgDAp0jWUuklmYwnBPXyUleIAJSvVxcv8hguMzPuArC3N5S1hL6ISYpB2IEw3Mm6oygzNTFFkbxIse1m54ZlA5chpH0IEu4moM+mPhBBhNMTTyuCfgiOrs9rirQldCMkhIeqAMBPP/G5imlpvLP822/qqSQBLnBD6IS2zJyqSVdcXbn4e1ER9z0QhDHQsynXrj77gHRt9cnatWsxffp0DBkyBFlZWQoNWzs7O6xYscKwxukbYaoCAKSmlp7VkTBKVNvA48eBFi24/93TU9kGhoQAN24APj68v3zoUOlJxwoLeXKxnj25w9bREdizB1ixghy2BFFdBAUFwcLCAgAQHByMoKAgrQtBGDWq2jyqCbZFIuXM0saNddPmqaHokjR75UreVhcW0kTbukJI+xDc/Pgm4sbFYVqvaRBBhEGtBkEyQYKc8BxIJkjg5eiF0K2hiEmKgV8LP7zb6V0wMEz5a0oJaQVCR1gdIzs7mwFg2dnZhjal5hEXxxjAWKNGjL3yCmOHDzNWVKTcf/w43w8wNmKEwcysSRQVMebmxtiwYYzJZOr7ZDJe7u7O63l781u7ZYthbCWI4ixOWMwQARa6NbTaPoOe2SVp374927FjB2OMMWtra3br1i3GGGNXrlxhjRo1MqBlmqn273DUKP5wtLdXtkEAf3hu3149n0lUCZrawO3bGROJGAsMZOzVVxlr0YKxhAReRyQq+yu9cIGxrl2VP4PgYMYePar2SyGIWgG1uTUf+g6rCKExGjaMsZMnGZNK+bplS964DB5saAv1wvbtvJ3W9noVEcHLhg41rJ2EfimSFTG3FW5sWNQwJpOrOzFkchkbFjWMua9wZ0WyIvZQ+pA1WNSAIQLsx/M/Gshi40TX5zVF2hK64+fHJRCePgVWrVIKwsXHA7//DsycqczgfeAAf64TpaJN5x5QJj4XdO5J15YwNoQpLhRpq19SUlLQVdBVU8HCwgK5guhnXeLmTb7+4YeyszoSRoWmNjAkhOvjJSbyJN137/LXj8REXq7tK5VKuYRC9+58yqa9PfDLLzxS19FRb5dEEIQKL1++xP3795Gamqq2EITRIzRGV64Avr6AjQ1f5/ybx+H5c8PapyfKSpodGsrXBw/ydpioGySkJuBO1h3M9ZsLE5G6E8NEZILw3uFIyUpBQmoCXBq4YN5r8wAA4YfDkZWfZQCLazamhjaAqEGIxcCoUXx+4f/9H+9Fff8971EJNG7M11lZwLVrQMeOhrC0xlBWZk7VxOdeXvxvctoS1YFMLkNCagLSctLg0sAFfq5+EJuUPqW8m0s3iCDC3ey7yMjNgGN98ozoA3d3d1y6dKlE0rF9+/ahffv2BrLKgAhO27ZtlQ9KwqiRybjDdvt2vl38ZxsSAgQFAfv38wzVX3zB855qUrmQy4FffwXmzAHS03nZ22/zVxVn5+q8CoIgtHHjxg1MmDABJ0+eVCtnjEEkEilkfQjCqBEao4QEpSygrS2XBPz7bx6gVIp2c21BVYmqOB068Nev69e5DNGoUXo1jTAQaTncieHpqNmJIZQL9T7p9Ql+uvgTkp4kYV7cPKwcvFI/htYSKNKWKB+CDpVEwkNjBIdtixZAZCTQrp2yblyc/u2rYeiamdPFBWjdmv8tJCwliKoiJikGHqs80O/nfngn5h30+7kfPFZ5ICYpptTjbOvZom3jtgCAcw/P6cPUOs1XX32FvLw8TJ8+HVOmTMEff/wBxhjOnDmDhQsXIjw8HLNmzTK0mfolMxN49oz/3bKlYW0hdCImhic36dcPWL2al7Vrx8tVEYuVSU369y/psGUM2LcP6NYNeO897rD18AD++gvYsoUctgRhSMaPHw8TExPExsbi/PnzuHDhAi5cuICLFy9SEjKiZiF4LEeN4uuOHXnmLalUPTtmHUUkUuYqFwZiidqPSwPuxEjM0OzEEMqFemZiM3w7+FsAwJqza3Dl0RU9WFl7IKctUT78/LiDtqCAZwT57TfunL11C5g1C7Cz4w0ZwGUTiFLRpnMP8O3ISJ4h288PaN6cl9OsMqIqiUmKQejWUHg5emkVkS+NTk6dAADXHl/Th7l1mvnz5+P58+eYOHEivv76a3zxxRfIy8vDO++8g7Vr12LlypUYOXKkoc3UL7du8bWLC1C/vmFtIcqkeOLNrCz+1THGO32qjtvibaAAY/y147XXgMGDgUuXeODT11/zgc7Bg/V9VQRBFOfSpUv4/vvvMXjwYHTp0gWdO3dWWwiixiGT8b7t9u08QzTApRMIhVTC3r1AXp5hbSH0g5+rH9zs3LAoYVGJ5GJyJseihEVwru+MB9IHiL8TD5lcBv+W/ghpHwIZk+HjvR+DkZSmzpDTligfYjHw/vvAixf875YteZjLmTM8leSePTzkBeANG/0zlooumTmFxOfC+8GzZ3VGRomoZmRyGcIOhCGwTSB2jtwJ72besDa3hnczb+wcuROBbQIx48AMyOTapzF2aNwBADlt9YHqy83o0aORnJyM58+fIz09Hffv38eECRMMaJ2BEKQRGjcGNm/m7Q5NuzVKZDKuORsYCOzcCXh7c2fr6tVARgaXzJ86lTtyNbWBjAGHDnFn7euv89mqFhbA9On8ZzBrFt8mCMLwdOjQAU+ePDG0GQRRNahOEXnnHeWA8ebNhrXLgAg+7M2bedCxqyt32O7fb2jLCH0gNhFj2cBliL0Ri+AtwZDckyCnIAeSexL0Wt8LscmxSM9Nx7s73lWbwfnNwG9Qz7Qejt49iq1Xtxr6MmoM5LQlyo8wTz81VV2Y/dQpLjo3fz7f//QphYXqgDad++JJV2xs+AIA9+4Zzl6i9lAeEXltdHAgp60+ERXTTrOysoJjXc6ytHs3X1+5wjtS/frxjlXxufaEwdGWeFNoA01MuGSgvb16G/jmm7wT+OqrwIAB/Dzm5tzBe+sWH/gU5PQJgjAcUqlUsXz99deYNWsW4uPj8fTpU7V9UspWRNQkik8RyckBpkzh+7ZsqZPvG8V92K+/zgdchX1E3SCkfQiiR0TjSsYV+G7whc1iG/hu8MW5tHPo3qS7xhmc59POI7x3OABgxsEZeP6SItF0gZy2RPkRhFijoriD1sGBbz9+DMybxyNvBU6c0L99NZCyMnMKCNG25LQlqoLyishrQtVpS9Ncqp82bdqgYcOGpS51hpgYZZTLhAnKaQpeXryDRT0Ho0EmAw4f5n8/e1YyGDokhOcuBbgzNi4OuHEDsLbmDtxBg/hXa2EBfPIJcPs2sGoV0LSpfq+DIAjt2NnZwd7eHvb29hgwYABOnTqF/v37w9HRUVEu1CGIGoGmKSLW1sCwYXy/tTUwY0admuGjyYctvHoJ+1++NKyNhP4IaR+Cmx/fRNy4OPz25m9wtnZGYOtAnJ54WusMzune0+Fu54770vtYlLDI0JdQIzA1tAFEDUQQYp02DTh/njdkc+cCnp7A5cvqnsbTp/kQHFEmpWXmFGjenEcfUQAzURWoish7N/Musb+4iLwmWjdqDbFIjJyXOXiQ8wDNbJpVj7EEAK5ra2tra2gzDI/QkWrcGHjyBOjTh3eevL15xyo4mHekgoJKZrAi9EpMDP+qhHwtQ4bwV4hly9RfF5KS+Hr4cB5J6+8PHD3KyywtgUmTgJkzlePGBEEYF3GUgJiobQhTRDZvVp8iIngoc3O5Zl1CQtmduFpAcR+2cEu8vblUQv36XCLh0CHe1hN1A7GJGH3d+iL+TjzSn6fj8z6fa53BKUTiLg9YjuA/grH05FKM7zIerRu1NpD1NQNy2hLlRywGliwBRozgInSzZ/NMmleuAIsXA48e8cwhKSlc65aoMijSlqhKVEXkd47cqdbAypkckccj4W7nDj9XP63nMBebo3Wj1vjnyT+49vgaOW2rmZEjR9ZtOQQBoSPVpAnfdndX7jMxAcLDeYhmHelIGStCRE5gIM9bOno0H3y0s+PlggSQkHSsWTPgm2+UqhcWFsDkyfw1w9nZoJdCEEQZvPbaa/jqq68wY8YMWFlZGdocgqg8af/ONPMsNiPNxQVo2BDIzFSvV8vR5sMGAFNTngh01y5g3Tpy2tZFyjODc6TnSAzyGIR9N/fh032fYs87e0pIwBFKSB6BqBiCJIJIBPTurS7Eun07sOjfUPfz54HCQsPZWcto3pyvKdKWqApKE5EP3hKM2BuxWDpwKcQmpUcqkq6tfqCXGRWEDtKjR3yt6rQFlB2sOtKRMjYEOYRJkwAfH/5a8Oqr3CErqCb5+vIkYseP887d7t3Aw4d8LRYDEydy2aDly8lhSxA1hfnz5+M5ZcslagvC1I7ERPVykUjdkVtHpoBo82ELCLnIjx6tU4oRxL+ozuDUhOoMTpFIhJWDVsLMxAx7b+5F7I1YvdlZEyGnLVExhKf2P/8ohVgPHQLWrwcKCvg0VYA7bK9eNZydtQyKtCWqGm0i8okZiYgeEY2Q9iFlnqNDY3La6gPSDFZB6CDJZICZWckOk9DBqiMdKWNCSFDi78+l7k+eBNq04eVC0rHERO68vXuXKy4dOMCPlcu5okViIn+daEaB+wRRo6B2iqhVCJKAixbxBkqVjh352taW16sDaPNhCwhpFaRSrnNL1C1UZ3DKmfr/i6YZnG0atcF0n+kAgE/3fYoXhS/0bnNNgZy2RMUQntpJSXzqqYUFD4vx9+catoJAOwBcvGgQE2sjFGlLVAeqIvJRIVGIGxeH5I+TdXLYAhRpqy/kcjlJIwj4+SnDL11d1XVrhbn27u51piNlLKgmKJk/n5cdPqyeGy4khCfa/PJL5XGMAa+8wjXxdu4E2rUzhPUEQVQFNCuEqDWIxVyAPTaWa+WrZt46fpzXcXevM9r5pfmw5XJg6VJl3NaOHXo3jzAwpc3gDNochN03diOoXRASUhMgk/NQ7C/6fIGmDZoiJSsFS04sMfAVGC/ktCUqhupTOzpaPY1kdjaXTDD9VzI5OtqgptY0ZDLecd28ma9Vp5eoRtpSMANRlQgi8qO8RqGvW98yJRFUUXXaUpQNoRfEYuCtt/jf2dnqHangYN7BWrq0znSkjIHiCUr69OHlVlZ8OzCQ54a7dg144w3gq6/4fnt7YMMG4OxZ4LXXDGU9QRBVRZs2bdCwYcNSF4KoMQhTRK5c4bo+giTgkyd8/9OnhrVPj5TmwxZevT76iNeNiaG+al1E2wzOfbf2AQBWnFqBfj/3g8cqD8QkxcDa3BrfBHwDAFh8YjFSnqUY0nyjRcTqWA9bKpXC1tYW2dnZsLGxMbQ5NZuYGJ7m2dIS6NaNP6mvXeMRTsJTe80aoF49nlmTOs9lUjzLNqCeZbuggN9OAMjIUEoLE4QheVH4AtaR1pAzOdLD0uFk7VRl56Znds2n2r7DhQuBL77gYR2qGoru7txhG6JbpDhRNcTHA/368c6btzd34np48PHcnTu5glJAAE9eIpdzSUAbG56z1N7e0NYTBAFU/nltYmKCFStWwNbWttR648aNq6iJRBnQe1M1IZPxTFxpaXzGqaensiOWnc0btDqCpv6q8Oo1aBDQuDHw4gWfbNuli6GsJAyJTC5DQmoCdv2zCytPr8TQ1kPxeZ/P4enoicSMRCxKWITYG7GIHhGNN9u9Cf9f/XEk5QjeaPsGdo3cZWjz9Yauz2tTPdpE1DZCQvj8x3nz+BQROzte7u7ORyRbteJO2/x84Ngx3psjtKKaZXvzZv4ukJjIg5lVs2w7OfHcO/fukdOWMA4szSzR0r4lbmbexLXH16rUaUsQWhF0Yj77DHj9dWVHys+PBgn1iNCP3b6db7dvz9dCRE5oKO+03b3Ly+Vy3o5lZPAIW3LYEkTtYuTIkSTlQ9Q+xGIuCahKkyY8g+bVqzzrZh0hJITrz6v6sFVfvQYN4vIIMTHktK2riE3E8HP1w/hd4xHYJhA7R+6EiYhP8vdu5o2dI3cieEswZhyYgaC2QVg9eDU6reuEP6//iT039mBom6EGvgLjguQRiMrRujVf//UXT0YWF8fF6kJCeM9NkEi4csVwNtYAik8r9fbmwWPe3urTSmUypUQC6doSxgTp2hJ6R3gIurnxjtSoUXxNDlu9ISQd69cPWL2al7Vrx8sBLo3QrBl/BZBKlcdZWSkHIgmCqD2Qni1Rp/D05Os6mHRb8GFrevV6802+Ft4FiLpJQmoC7mTdwVy/uQqHrYCJyAThvcORkpWChNQEtHdoj8+8PwMAfLLvE0pKVgxy2hKVQ0hIZm/Pn9p+fnzYbfNmnjJaSP2cl2c4G2sACQl8isncuXzqqComJkB4OJ9CmpCgTEZ2/77ezSQIrXRoTE5bQs8ITlthJIvQK6pJxyQSICuLvxIwxpWTOncGBg/ms0Jsbblv3cGByyQIY7tE3eDsg7MY8vsQ2C22Q/1F9eH9oze2Xt1arnNsurQJovkirUv8nfhy2/VA+gDhh8LxyvevwG6xHcwXmMNlmQuGRg3Fpkub8FL2stznrOsYWnUvMzMTo0ePho2NDezs7DBhwgQ8V5XP0UB+fj6mTJmCRo0awdraGsOHD8ejR4/U6qSmpmLo0KGwsrKCo6MjZs6ciaKiIrU68fHxeOWVV2BhYQEPDw9s2rRJbf+xY8cwbNgwNGnSBCKRCDt37lTbX1hYiNmzZ8PLywv169dHkyZNMHbsWDx8+LDC94OoZjp25OvERMPaYWQEBvK4ratXgRs3DG0NYSjSctIAAJ6Onhr3C+VCvf/2+S+aNmiK289uY/HxxfoxsoZA8ghE5VBNSDZ2LDBzprrAjTDsVtwTSaiRxp9VigHb4gjlaWnKhOnp6dVvF0HoiiLS9gk5bQk9wBg5bQ1I8dkhQhP/xRfAlCn878uXlfnisrKA/ft5dG3//oaymjAEcSlxCPgtAPVM62Gk50g0MG+A7Unb8Xb027iXfQ9hvmHlOl9Q2yB0ce5SotzNzq1c59l8ZTMm/DkBL4peoJtLN7zb6V3YWtgi/Xk6jtw5gvG7xuPXy7/i8NjD5TpvXUdePKW8nhk9ejTS0tJw8OBBFBYWYvz48fjwww8RFRWl9ZjPPvsMe/bswbZt22Bra4upU6ciJCQEJ06cAADIZDIMHToUzs7OOHnyJNLS0jB27FiYmZlh0aJFAICUlBQMHToUkyZNwu+//47Dhw9j4sSJcHFxQUBAAAAgNzcXnTt3xvvvv48QDaNWeXl5uHDhAv773/+ic+fOePbsGT799FO88cYbOHfuXDXcLaJCqGrbCjNK62CkbWnY23PVqgMHuEzC7NmGtogwBC4NeHBfYkYivJt5l9ifmJGoVq+BRQOsGLQCb217C4tPLMboTqPRplEb/RlszLA6RnZ2NgPAsrOzDW1K7WH7dsZ4F5qx3r0ZO3SIL717K8v79TO0lUZNXBy/TRKJ5v0nT/L9cXGMzZ/P/544UZ8WEkTpnHtwjiECzPF/jlV6Xnpm13yq5TvMzFS2L3l5VXdeokyKihhbvpzf+jVr+HZqKmMffsiYqanya1Fd3N35qwJRtyiUFbJWK1sxiwUW7GLaRUV51oss1mZVG2a+wJzdeXZHp3NtvLiRIQJs48WNlbZrb/JeZjLfhDX8uiE7cPNAif1yuZzFXIthQ34fUunPqmnU5Db32rVrDAA7e/asomzv3r1MJBKxBw8eaDwmKyuLmZmZsW3btinKkpKSGAAm+fel/K+//mImJiYsPT1dUWft2rXMxsaGFRQUMMYYmzVrFuvYsaPaud9++20WEBCg8XMBsB07dpR5TWfOnGEA2N27d8usK1CTv0OjZ/t2xtzcSjZydnaGtszoWLeO35qePQ1tCWEoimRFzG2FGxsWNYzJ5DK1fS+LXjKfH32YwxIHdujWIVYkK2KM8fZ30G+DGCLA/H/xZ3K53BCm6w1dn9cU/khUnqAgwNERsLTkCcn8/fny4AEwfTqvc/IkH5kkNKIasFw8SEEuByIjeX43Pz+ewAXgycgIwlho17gdACAjNwNP8p4Y2Bqi1iPowzRqxNseQi8IGrafcdkxTJnCI2patgR++AEoKuIJSOLj+f6pU9Wl7om6xZGUI7j17Bbe8XpHLTrWtp4t5vaei5eyl/j575/1apNMLsOUv6ZAzuTYGroVA1oNKFFHJBLhzfZvImYECTLWJCQSCezs7NC9e3dFmb+/P0xMTHD69GmNx5w/fx6FhYXw9/dXlLVr1w6urq6QSCSK83p5ecHJSZlkNSAgAFKpFFf/jbCUSCRq5xDqCOeoKNnZ2RCJRLATkj0ThqO4JlBODtf7Afh0kp/1+ywzNmQy3vZv3szXgYGASAScOUOSfnUVsYkYywYuQ+yNWARvCYbkngQ5BTmITIiE7WJbSO5L8DjvMfx/9YfHKg/EJMVAJBJh1eBVsBBb4NDtQ/jj6h+GvgyjgJy2ROVJSOBpoA8e5L0z1YRkEybwOgUFwLFjhrXTiBGybMfGAsHByncBiYRvx8YCS5fyeoI8AjltCWOivnl9xfTUpMdJhjWGqP0IPQBBN52oFlQ7YV99peyvRkTw/WIxb6uKioAOHfjrwN69gLk53z98OOWGq8sIOrMDWw0ssS/Ag08ZP3r3aLnOeTHtIpadXIavj3+NPxL/wNO8p+U6Pu5OHG4/uw3f5r7o37J0rQ4LU4tynZswLOnp6XB0dFQrMzU1RcOGDZGuRVMsPT0d5ubmJZyiTk5OimPS09PVHLbCfmFfaXWkUilevKhYQp38/HzMnj0bo0aNgo2NjdZ6BQUFkEqlagtRxWjLGN2/vzLZSHh4nQ1QUk1K+s47fN27N9C2Ld+/Y4dh7SMMR0j7EESPiMaVjCvw3eALm8U2mHtkLkQQIbJ/JHLCcyCZIIGXoxdCt4YiJikGHg09MNdvLgDgs/2fITs/28BXYXjIaUtUHkGQtXPnkmkkW7Xiw2wAkESOnNIICeF6f1euAL6+gI0NXycmqmfZpkhbwlhR6NpSMjKiunnwgK/r1VOGddTRzlJ1UbwTNm8ed8ZmZnIHLsBveZ8+gI8P8OIFXxefHULUXZIzkwEArRu2LrHP2doZ1ubWSH6aXK5zfnvmW8w4OANzDs/ByO0j0Xx5c3x9/Gudjz+RynVKX3d7vVyfSxiOOXPmQCQSlbr8888/hjazSiksLMSIESPAGMPatWtLrRsZGQlbW1vF0lxwIhJVR2kZo728+DotjderY2gKQJZI+Pb168o6RN0lpH0Ibn58E4fGHIKDlQN8m/kia04W5vSeA2tza3g388bOkTsR2CYQMw7MgEwuw+xXZ6N1w9ZIf56Oz498buhLMDjktDUkDx4AK1YAAwfyRCrm5jyMcvhwQMs0njIpKgI2bgSGDOHnMjfnaZt79OAZQu7erdJLAMDTRQOaM2daWCi9jMWyrBIlCQkBbt4sGbCsOq1U1WlbkSS9v/0G/Oc/QPfu/OsRiYBiCW7LhSF+coaEsmBrp33j9gCAq48pIQNRzRw8yNenTyvDOjw8qGdQRah2wo4fB/77X15eUACcOMEds1268PbD1hZ47z0gJQVYt67k7BCi7iJEx9jWs9W438bCBtkFukXQuNu5Y9XgVbgx9Qby5ubh/mf38UvwL2ho2RBzDs/BqtOrdDpP+nMeGdnMhqL0awphYWFISkoqdWnZsiWcnZ2RkZGhdmxRUREyMzPhLExTK4azszNevnyJrKwstfJHjx4pjnF2dsajYpESwnZZdWxsbGBZTgkfwWF79+5dHDx4sNQoWwAIDw9Hdna2Yrl37165Po/QgdIyRnfsWLJeHUFbALK3N99+/d+xsaNHgcePDWkpYWjEJmKITcR4nPcYywKWwUxsprbfRGSC8N7hSMlKQUJqAixMLbAucB0A4Luz30Fyr3JSMzUdU0MbUKdZtQr4+msejTpwIODgwD10O3fyJSoKePtt3c939y7Xl/37b+7ZGzCAT9nIzQUuXAAWL+a9qMRE3rmtKlQFWVXTSAO8Zyc4a62squ4zazFiMQ9U1obgtM3PB6RS3mEuD4IjtXFj7m+vjFPVUD85Q0FZsEunbSM+D+rWs1vV+jlEHScmBtj670DJxInA8uX8IbNoEfc0qk5NIMqN0Anz9+eDb6NHK9sJkYi3GyIRcO4csGsXr7t7N98/dSqPsKWvgKhqXnN7Da+5vabYbmrWFGM6j8ErLq+g+/ruiDgagck9JsPUhLo2tQ0HBwc4ODiUWc/HxwdZWVk4f/48unXrBgA4cuQI5HI5evXqpfGYbt26wczMDIcPH8bw4cMBANevX0dqaip8fHwU5124cCEyMjIU8guCM7VDhw6KOn/99ZfauQ8ePKg4h64IDtvk5GTExcWhUaNGZR5jYWEBCwuS8qhWVAOUvL3V96k6coV6dQQhAHnz5pIByCYmwIIFwOHDPMho507ggw8MYSVhLKTl8EENT0cNgx8q5UK9191fx7jO4/Dz3z/jw9gPcf7D8zAXm+vHWGNDP3nRjAejyqi5fTtj8fEly48dY8zMjDF7e8by83U7l1TKWNu2PE3jzJmaj0tOZmzYMMYuXqyU2RrZvp0xkYifPyGBsT17GIuIYMzHR5lZc86cqv/cOkqDBvyWXr9e/mMPHmTszr/JmiMj+Xk2biz/eQz9k9M3lAW7bPbf3M8QAdZxTceyK+uIUT2ziQpRpd9hURHP3OzgwB8+P/2k3CeT8QeOuzuvR5QbqZSxMWNKJsYW2pwdOxg7eZL/HRfHjykqYmz1al62fDndekJJ6NZQhgiwcw/OadxvvciaNf+meaU/x/8Xf4YIsMvpl8usGxEXwRAB9t8j/63059ZGanqbO2jQINa1a1d2+vRpdvz4cda6dWs2atQoxf779++ztm3bstOnTyvKJk2axFxdXdmRI0fYuXPnmI+PD/Px8VHsLyoqYp6enmzgwIHs0qVLbN++fczBwYGFh4cr6ty+fZtZWVmxmTNnsqSkJLZmzRomFovZvn37FHVycnLYxYsX2cWLFxkA9s0337CLFy+yu3fvMsYYe/nyJXvjjTdYs2bN2KVLl1haWppiKSgo0Pke1PTv0CgR3j2GDePvGqqcOcMbQBMTxgoLDWOfgYiK4peek6N5v1SqfI8ICNCvbYTxEZcSxxABJrkn0bj/ZOpJhgiwuJQ4Rdnj3Mes8ZLGDBFgC48t1JOl+kPX5zXJIxiSkBDgtddKlvv58amez55xgVNdWLqUC8e8+y6wZAmf914cDw/gzz95tpCqRhBkPXWK2z90KM9UIpHweRIAjyImqoTK6Nr6+wMtWlTeBkP/5PQNZcEumxa2/Id1N/suWEW0OwijIzIyEj169ECDBg3g6OiI4OBgXBdEygyBENbRoAHfbtpUuc/EhCcCSUmpk7pyFUEm44nDPvuMSyHY2gK//qrcb2HBo2cfPuQTajZsUD7PhVmgIhGwfz+PsP34Y5JEIJQIWraCtq0q6c/T8fzlc7RuVFLvtrw0tmoMAMgtzC2z7quurwIADqdU78wTwjD8/vvvaNeuHfr3748hQ4agd+/e+OGHHxT7CwsLcf36deTl5SnKli9fjsDAQAwfPhx9+vSBs7MzYlSkdsRiMWJjYyEWi+Hj44N3330XY8eOxVeCuDcAd3d37NmzBwcPHkTnzp2xbNky/PjjjwgICFDUOXfuHLp27YquXbsCAKZPn46uXbviyy+/BAA8ePAAf/75J+7fv48uXbrAxcVFsZw8ebLa7hmhA6VljBYyc8rlwNPyJUas6ZSmkFi8/PBh7tog6i5+rn5ws3PDooRFkDO52j45kyPyeCTc7dzh56pMiNDYqjGWBywHAHx19Kty6+DXFmgOkbFi9q/Oh6mOX9GGDXz9b8NfKubVGFb+5AnPnhUQwOdV2tgA06cDZ87wuZREleDkxLVvDZmMzFh+cvqiurJgP817iiJ5Edzs3ODf0h+NrMqeCidgbFmwXW1dAQDPXz7Hs/xnaGjZsNo/k6hejh49iilTpqBHjx4oKirC3LlzMXDgQFy7dg3169fXv0GCpzAzk69VnbaAcppiHdOV0wWZjPuy09J4//PwYe6gLZ7Y3NoaeP6cT2s8cwZYs4aPIy9bxtUnAgN5PVtb3l+NjOT92OhoctgS6rzW4jVEHo/EgVsHMNJzpNq+/Tf3K+pUBplchnMP+fulMHBYGv3c+qGlfUucvHcScSlx6OfeT2vdgqICvbSdRNXRsGFDREVFad3v5uZWYlC5Xr16WLNmDdasWaP1uBYtWpSQPyhO3759cfHixVL3lzagrck2wogQApTCwnhfV8DdnSf1SE/nXkohsqYOUJZCopCU1MoKuHqVB/KMG2coawlDIzYRY9nAZQjdGorgLcEI7x0OT0dPJGYkIvJ4JHbf2I35fedj69WtcGngAj9XP4hNxBjtNRq//P0LDt4+iA9jP8ThsYdhIqpbsafktDVGUlOBQ4f48JWQkbI07t4F7t8HmjUDWlc+YqFCFFciV31qb9jAO9KpqVzfVldHNKGVykTaVgXG8JPTN9WVBVsVS1NLzHttHmb3nq3T8caWBdvSzBKO9R2RkZuBu1l3yWlbC9i3b5/a9qZNm+Do6Ijz58+jT58++jdICOsQksYUd9oKYR11TFeuLH75BZg5EyiWo0eBWMz7nG5uPNGYoyN32MbE8H7qjBl8sszWrcDYsfyYoUP5mjRsCW30b9kfLe1bIupKFD7p9Ylilkp2fjYWHV8Ec7E5xnYeq3ZMWk4asguy4WLtopbA7PzD8+jWpJtaXZlchjmH5uBm5k30c+sHlwZl/9+LTcRYM2QNhkYNxYjoEfgj9A+87l6yDd19fTd+uPADdo/aXYErJwiiVhISwpN5CCOgLi7ccxkayvu/iYlA/9KDKGoTQgByaCgPQA4P513+xET1Ad3ERGDePGDbNnLa1nVC2ocgekQ0wg6EwXeDcvDD0coRjvUdMS9+nqLMzc4NywYuQ0j7EKwLXAevtV6IvxOP9efX4z/d/2MI8w0Gec+MjcJCYMwYnqL56691C1tJ55lw0cyAmXBLUyJv2ZKvGQP27OGNHVEphCS4wlevb4zhJ6dvqiMLdkCrADSzaYbMF5k4knIE4YfDMefwHFiZWeHjXh+XeR5jzILdwrYFd9pm30VXl66GNoeoYrKz+W+8YUMDOeT9/PiD5/59HsJvZ6fcpxrW4een9RS1naIiICkJOH2aJwqTSDTP2DQz468ckycDa9fyjlXPnrzjdfo072yFhPCx2N27gXXruAzCixfA/Pl8wE7or1KELaEJUxNT/DjsRwT8FoA+G/uoJfC8m30XSwcsLZF4M/xwOH7++2dsDNqI97q8pyjvvr47Ojl1QienTmjaoCkyX2Ti6N2juPH0BprZNMOPb/yos12DPAbh1zd/xcQ/J6L/L/3RvUl3+DTzQQPzBniU+wjxd+Jx69kt+Lf0r6I7QRBErUFTxmhPT+601VXWsBZRWgCyMKDbrh132h44wMfcVV/diLpHSPsQBLUNQkJqAtJy0pCcmYyI+AgEtgnEXL+5iujbRQmLELo1FNEjohHSPgSLXl+EafunYebBmRjcerBihmddgJy2xoRcDrz3HnDsGE+vOGaMoS3SHWEqqmoGTQFLSx4a+ugRH2ojp22lMXSkLVE5akIWbJlcpmhMVaeolEULuxY4+/As7mbd1YOVhD6Ry+WYNm0aXn31VXhqetb/S0FBAQoKChTbUqm06owQi7mX8fPP+d+nTmkO66gDXsTCQj5Wev06d9ImJvLbkZLC9xXH1JTLHPj6cqdrfDyweDG/XQC/jYIssFBn40busAW4tq27O7B9O0XVErrTz70fjr9/HPPi5+GPq3+gUFYILycvfO3/Nd72fFvn84T5hOHU/VM4eOsgMl9kwlxsDo+GHvjC7wtM95kOe0v7ctn1jtc7eK3Fa1h1ZhUO3DqAn//+GXmFeWhk2QhdXbriiz5fYLTX6PJeLkEQdRFhZqw2cddajrYAZOFVrEMHvly7xiUSxo4t/XxE7UdsIkZft76QyWXwWOWBwDaB2Dlyp0L2wLuZN3aO3IngLcGYcWAGgtoGYWrPqdh6bStO3juJ/8T+B3+98xdEIpGBr0Q/kNPWWJDLgfffB6KieGandet0P1YIu3zwoHps0wVVJXJv75L7HR25h1FTT5IoN4Z22hrDT07fCBG2QsRtcaQFUtjXK1+nsTgdHTuit2tvHLp9CEmPk+DlVLo8irM1/yIe5FTtFxGTFIOwA2G4k3VHUaY6RaU0VJOREbWLKVOmIDExEcePHy+1XmRkJObPn199hrRty9dyufawjloAY8Djx9wJe+cOXwvL7dtcpqaoSPvxJiZAx4488GfZMu6kjY3lZQDQqZPSQQsom2/BH9+6NddOX7eOO2yXL6dEY0TF6Nm0J/aO3qtT3U3Bm7ApeFOJ8qUDl1axVUBTm6ZY7L8Yi/0XV/m5CYKoQ6g6beXykrNO6wCaApBVeestPhi8bRs5bQklCakJuJN1B5uHby6hU2siMkF473D4bvBFQmoC+rr1xU9v/IQu67pg3819+OXvXzCuS93Q2zDoE2Xt2rXo1KkTbGxsYGNjAx8fH+zdq/2lbv369fDz84O9vT3s7e3h7++PM2fO6NHiakIuB8aPB37+GRg1Cti0qXwP+xYtuK7fvXtcdM4QqCqRy9WzAUIuVyaNsaCEDlWBoZ22xvCT0zd1JQt2TFIMQreGwsvRC5IJEuSE50AyQQIvRy+Ebg1FTFJMqceT07Z2MnXqVMTGxiIuLg7NytBFCQ8PR3Z2tmK5d+9e1Rrz8CFfDx0KxMXxwc64OP4wqoEOW6mUSxhs2ADMmsUvoVMnoEED/qz39gZGjuQO1h9+AA4eBG7d4g5bMzNle9CmDbBwIdCkCfDqq8CQIcqZmh9+yGduBgYCW7bwssREpYPWwUHZfKvKAotEXBLB3Z0ctgRBEAShQCbjo6GbN/P3EjMzIDeXj7ISJXjrLb7ev1+ZloAg0nL4bG1PR80z+IRyoV67xu0wvy8PDJm2fxoeSOtGBJlBnbbNmjXD4sWLcf78eZw7dw6vv/46goKCcPXqVY314+PjMWrUKMTFxUEikaB58+YYOHAgHtTkcD/BYfvLL8Dbb/NUzhXpFU2YwNf/939l1335svznLwtBiTw2lgviSSRATg5fBwcrQzLvkiOnKjC00xYw/E9O3wgZrg/cOlBin7FkwS6NgqKCUvcLnx92IEwxRcW7mTesza0VU1QC2wRixoEZkMllWs/Rwu5fpy3JI9QKGGOYOnUqduzYgSNHjsDd3b3MYywsLBSDscJSpQhyPE2a8LCOUaP4ugZ4FKVSnmd00SLunG3ZErC15dGuEyYA//sfsGMHd7bm5nKnadOmQO/efBLOf/8LTJminO1QWMjbAUtL7rD19eV9x6VLuZ6tjw+v9/ffSumD9HR+/KJFwOXLfP/Uqbz5DgriCctatOByCsHBvHzp0hpxewmCIAii+omJATw8uObQO+8A/v68wQaUDSuhRseOfKC4sJAPIhMEAEUC0cQMzdIiQrlqotEw3zD0aNIDWflZmLh7Ihhj1W+ooWFGhr29Pfvxxx91qltUVMQaNGjAfv75Z53Pn52dzQCw7OzsippYdchkjI0bxxjA2FtvMVZYWPFzSaWMtW3LzxUezlh+fsk6t28zFhTE2MWLFf+csti+nTE3N26HsLi7MzZ5Mv97yJDq++w6xO3b/HZaWjIml1f8PJGR/DwbN5b/WGP5yemLQlkha7myJbNYYMEupl1UlGe9yGJtVrVh5gvMWcqzFLVjHkofsqTHSSzrRZZa+bkH50qcv0hWxGbsn8EQAdZvUz+d7dqbvJeZzDdhjZc0ZodvH9ZY589//mSBUYFlnisuJY4hAkxyT6Jx/8nUkwwRYHEpcVrPcSntEkMEWOMljXWyvyyM6pldB5k8eTKztbVl8fHxLC0tTbHk5eXpfI4q/w7fe48/eBYurJrzVSNZWYzt3MnYp58y1qULYyYm6s2jsDRtypi/P2NTpzK2YgVjixcztmwZY/v3M1ZUxJe4OMamTWNMJGIsMJAxiYSxv/7ix/fuzcunTePbOTn88xMS+LavL3/lkEr5tnAeJyfGXFwYe/aMsUWLeJtSvPnevt2AN5AgCL1CbW7Nh77Damb7dt6ADhvGG+KcHL5u1ow3nG+/bWgLjZYFC/gtCggwtCWEsVAkK2JuK9zYsKhhTCaXqe2TyWVsWNQw5r7CnRXJitT2Xcu4xiwWWDBEgH1/7nt9mlyl6Pq8NhpNW5lMhm3btiE3Nxc+QmhIGeTl5aGwsNBwWawry1dfcUkEa2s+r1FTyGJwMNClS9nnatCAzzcICuLJWDZuBAYO5Fm28/KAixeBEyd46MzSqtcFU6BNifzQIZ6e+v796vvsOoQQafviBQ9oLk8g248/AoIkpTB19scf+QwfgEd0TZxY9nmM5SenL+pCFuzyTlHRhBBp+yTvCXJf5qK+eX2dr4UwPtauXQsA6FtMqGzjxo1477339G8QoB5pa2QwxuUFdu8G9u7lk01kxQLT3dy45EGPHsArr/Dok6tX+WUlJwMrVqjPrnRw4AE8GRnKsitXeEStkO9tzx4eiasqfeDtDXTuzLeFSS+BgXzbwwPo1g04xwP7YW+vtG38eK5nWzyRCEEQBEHUaWQyICyMN6Y7dyrlDL29gU8/5VNVdu/m9ajxLMHbb/MZQ4cOcc1+BwdDW0QYGrGJGMsGLkPo1lAEbwlGeO9weDp6IjEjEYsSFiE2ORbTvKchITVBLSl2e4f2iOwfiekHpmP6/unwb+mPlvYtDXw11YfBnbZXrlyBj48P8vPzYW1tjR07dqBDhw46HTt79mw0adIE/v7anRHVmsW6sgi9sufP+bxGTbi56ea0Bfh8xrNngd9+A7Zu5R61zEygXj3eA5s1C5g0CWjevAqMLwVNSuSCBmJVaxvWYmQy7Vk4raz4kpcHPH1aPqft8eN8rECVEyf4IqCL0xYwnp+cvqjtWbBVp6h4NyuZUFDTFJXi2NWzg42FDaQFUqRmp6K9Q/tyXQthXDBjnHIkOG0FjQADI5cDp07xHGg7d/JEYaq0bg30789nUfr5KfN2AnyGZc+e6k5aS0s+EDZ1KrB6NZc0AIAPPgDWr+fLn38CoaFARATfd+2aMqmYIH2wc6dSnzYigg+s7d7Nt6dO5Tq1W7fyTpOmdoYgCIIgCBUSEniDvXlzyfwzQn89L4/XKy0rVx1AUz+2dWs+WH3hArB9O+8jEkRI+xBEj4hG2IEw+G5QJhg2NeGuyhWnVmDFqRUlkmJ/6v0pdl3fhaN3j+K9ne8hblycwqlb69BP4K92CgoKWHJyMjt37hybM2cOa9y4Mbt69WqZx0VGRjJ7e3v2999/l1pv3rx5DECJhaaM6AFhPmdUFGO7dyvnWz5/bmjLjB5NKhNuburTVJs25eXnSs60J4gKUdEpKsXx+s6LIQJsb/LeSttE0/xqPlX+HTo58YefAXVX5HLGTp9m7LPPlDMihaVePS5fsHYtYykpJY/VJHVw/DhjLVpwmYPAQF6+dSt/7gcG8sXBQSl9IJPxmZlubnwZNoxLMahKHwQGMvbqq/y8CQl8W9gfF8ftIAiCUIXa3JoPfYfVSFSUugaRKhkZyheBn37Sv21GRGn92CVL+Lafn6GtJIyNIlkRi0uJY9P2TmOiCBEL/D2QSe5JWE5BDpPck7BhUcOYKELEtl9TOkRuZ95m1ousGSLAvj7+tQGtrxi6Pq8N7rQtTv/+/dmHH35Yap3//e9/zNbWlp09e7bM8+Xn57Ps7GzFcu/ePWrI9IGmp7VIxNfXrxvaOqNGm1TSsGG8XHDcennx23nggGHtJWoX269tZ6IIERsWNYydTD3JpPlSdjL1pMaGUhuBUYEMEWDrzq6rtD3U+aj5VOl3WFSkFIZ9+LDy5ysn168z9t//MtaqlXrz1qABY6NH8+dzaeOSmprGFi0Ymz+f/y2RKB2yzs7KspMnlfUl/0pOC2Xz5/O2oXdvvr1nD+nTEgRRMajNrfnQd1iNxMWpN8TFadyY7//2W72aZUyU1Y/9/nulS+DuXUNbSxgbFQkgWn9+PUMEmNlXZuz8w/P6NrlS6Pq8NikZe2tY5HK5mpxBcZYsWYIFCxZg37596N69e5nnq/Ys1kRJYmL4vE0vLy6kl5PD1/X/1bbcvNmw9hkxxaWSvL255LG3N98ODARmzOD1BCnnZ88MaTFR2xCmqFzJuALfDb6wWWwD3w2+SMxIRPSIaMWUlNJoYct1be9m361uc4m6xpMnXI9AJNKbGFpWFpdk9/YG2rYFFiwAbt3iEjWjRgG7dnG92d9+47Lu9VVknGUyrhe+eTOXsReaxjVr+P7164FOnYB58/i2pyefcRkeDqSnK8s8/5WZdnDg0gdyubKsdWsuc3D+PN8eOhSYO5drn8+fD0RFAXFxXC83pOx/X4IgCIIgNOHnx6ULhYZYFblcqS9UfF8dQZd+7OLF/DYC5BIgSpKQmoA7WXcw128uTETqrkoTkQnCe4cjJSsFCakJivIJXSfgzXZvolBeiHe2v4O8wjx9m13tGFTTNjw8HIMHD4arqytycnIQFRWF+Ph47N+/HwAwduxYNG3aFJGRkQCAr7/+Gl9++SWioqLg5uaG9H97NNbW1rC2tjbYdRAqlCbQ3qsXcPgw761+8QUJ52mgNKkkoSPv68vrCU7bzEy9m0nUckLahyCobRASUhOQlpMGlwYuauLvZUFOW6LaePiQr62tuUB3NYmwMsafs+vXc63a/HxeLhbzhItjxgBvvMH1uxMSgB07uGabry9w8qQyqdjGjSX1aseOBQoL+fbIkcD77/MEkBIJ8PffwKuvKh2yANelFaSFp07l+rSqScUyM7me+IsX3ElLScQIouYhk8sq3OYSBKEnxGJg2TI+AhsczDtmnp68oY6MBB494vUuXTKklQZD135sSAhw7Bjw++/A7NkGMZUwUiqSFFskEmH9sPU4/eA0rj+9jrD9YVgbuLb6jdUjBnXaZmRkYOzYsUhLS4OtrS06deqE/fv3Y8CAAQCA1NRUmKj8x69duxYvX75EaGio2nnmzZuHCCEbB2FYSntaC9moHj8mgXYtCPl1PDU/pxTlaWnktCWqF7GJGH3d+lbo2BZ2/zpts8hpS1QhMTHARx/xv3NyeGYvNzfegaqiENK8PODXX4FVq4CrV5XlXl7Ae+8Bo0fzCFbBnLAwdaesqSlQVKTcFpKKde4MDBkCdOsGjBihTCCWmMjHNJcs4U7WWbN48ygkEHN2VuYpdXcHPv+ctwNhYSWTim3fTpG0BFETiUmKQdiBMNzJuqMoK55wRSA7PxuS+xLkFebhpewlXspewtnaGZ2cOsGpvhNEIpGerSeIOkZICB/NDQvjHkgBd3fugfz6a55pqw6iaz+2bVvAzAy4cgW4fJnPOCIIoOJJsRtZNcLPwT9jwK8DsO78OgzyGISgdkHVb7CeMKjT9qeffip1f3x8vNr2HdWeEWGclPa0bty4ZD1CDSGruNCRL47QkXdxIactYbxQpC1R5QiyO1268EiW117jc+wWLeLl0dGV8lg+fQqsXs2dtU+f8jJB/uDDD4EePfhsx4QE4MgRHkUbEcGjXTdv5nIJY8YAjRpx8xwceKfEzo5LFXz6KT/nnj3Au+/yCFxhhuXOndypC/Bo26AgLnvTogXwwQd8YgrA6+bl8ee/pyd3Fk+bxutTVC1B1ExikmIQujUUgW0CsXn4Zng6eiIxIxGLEhYhdGsookdEo69bX8QkxSAmKQaHbh9CobxQ47kcrBzQ2bkzFvRboLGzSxBEFRESwhvfhATepxWmt6Slcaft1au8wbayMrSlekXXfmzr1vz9accO4JdfgKVL9WcjYdz4ufrBzc4NixIWYefInWoSCXImR+TxSLjbucPP1a/Esf4t/RHmE4ZlkmV4/8/3ccnlEprbNten+dWGiDFh0l3dQCqVwtbWFtnZ2aRvWx3Ex/PoJ4mk5NN68WI+LwLgAnsUaVsCmQzw8OBRXarqEgB3GAQH8wYvOZlHZs2dC4wfD2zYYCiLCaIk6c/T4bLMBSYiE+R/ng8zsVmFz0XP7JpPpb9D1Qdj7948kmX0aC4iW/zBWE7PpVTKA3WXL+fBuwAPlvnkEy5jcPly6VIHv/wCvPmm0ryYGKBPH94EHj8O+Phw886e5Rq1EgmXOvD15VIGguM3MBD4z3+AiRP5dMEXL5Sf4+jI1xkZyjJ3d97Jochagqi5yOQyeKzygJejl8bO6bDNwyC5J0GRvAg5L3MU+1rZt4KztTPMxeYwNTFFanYqbjy9AQbepTs98TR6Nu1ZIZuoza350HdoQBgDmjThDf6JE+qRuHWA8vRjY2P5tpMTcP8+n6lEEID6YGZ473C1wczY5FhM856GoLZBGmWECooK8OqGV3E+7Txebf4q4t+Lh6mJ8f64dH1eG+8VEDUTVYH24k9re3u+trJSKpATapQllRQbywPKxGKKtCWMF8f6jrAQW6BAVoD70vtwt3c3tElETUZVdmf7dl4maBQUF/vWcTBQLgc2beIDX4IEXadOfDs0lCcX69atpJNWm9SBYJ6pKRAQwJ2zUqm6ec7OvGn89Vd+vtatlTMsBamDH3/kTej48eratMJtUA3oochagqg5aNKsFRKubB6+Wc1hWyQvwsaLG3H6/mk8y+fZZjs6dMQoz1EIaR+C9g7tS5w/rzAPVzOu4vKjy1q1AAmCqAZkMvUGuls3Pq3m3Lk657QtTz928GA+CffRI+DAAf5eRRCAMil22IEw+G5Q/g8JztcVp1ZgxakVGmWELEwt8EfoH+j6fVecuHcC8+LmYWH/hXq/hqqGnLZE1VLa0/rHH3mdZs2ot1kKpUklqc4AJqctYayYiEzgauuK5Mxk3M2+S05bonKoyu589x3/W3DaCuWq9crg+nUe0Xr8ON9u2hT43//4KR894hqyQgTsb7/xoN7mzbVLHaxerW5Gjx58ffYs75QI5SNHAitXKhOI2drysUxdpQ5ocgpB6B9dEoSVVUebZm1wu2AA6glXDt46iOkHpit0+wDgo+4fYdWQVSUyaatiZWaFHk17oEfTHpW8YoIgdEaTuL2dHV+fPWsIiwyOrv1Yc3PgnXeAb78Ffv6ZnLaEOqpJsXf9swsrT6/EoFaD8HmfzzXKCKk6bls1bIUf3/gRb0e/jcjjkXjN7TUMbDXQgFdTechpS1Q92p7WLi7ajyHU0CaVpNqRJ6ctYcy0sGvBnbaUjIyoLKoiaY8f878FzQChXLWeFhgDfvoJmDIFePlSWf7gAZdCKJ5AbOxYoLAQuHsX2LIF6NmTj0Vu2cLrXLumjKIVzPD2BoTZTfv3cz1awbygIODVV/l5AWDoUL6mJGIEUX50caZW9hy6JAgrq05pmrUrTq0AwBOrWIgt8N+4/2JP8h4AgH09e4zpNAbfnvkWb3V8q1SHLUEQBkDQ2hfE7YUgpWnTgNOnuQB+HUWXfizAE7x++y2fnPv0Kc8LQBACYhMx/Fz9MH7XeAS2CVSTEfJu5o2dI3cieEswZhyYgaC2QWrt94iOI3Ak5Qi+P/893o15Fxf/cxFNbZoa6lIqDTltiepB09O6YUM+r1TI8kKUilhcemSV4LR99kwv5hBEuaBkZESVoSq7I2gZCE5buZzPuXN3L1V2p6AAmDSJSyIAfEreDz/wPCGqCcTmzuUfI0gfCFG1np6lSx04OPDtmBiuN+7oWDKpmKkp18B98YLr2arKH9DkE4LQHV2cqUDpTtnKOFuFyB4Apdb5I/QPzDo0S2tnM2hzEPbe3IthUcPw5MUTAHz655QeU/BFny/w/q73tSZcIQjCgMhkPDgpMFBdDtDbm+srOTsDDx/yyBqhw1bHKKsfCwBdu/Ll4kU+s0l45yIIAW0yQgCf2RneOxy+G3yRkJqAvm591fYvD1gOyX0JLj+6jBHRIxA3Lg7mYnM9Wl91kNOWqD6KP63T0/n62TPe0TahqIHKQJG2hDEjOG1VO8QEUSFUZXcsLHhZ/frcK1pcJE0DWVnAsGFKOQRXV+DGDe5E9fDgfa6YGD7WuH49ryNIHwhRtUIUrTapgw8+4KY0a8adv7/9BqSmAgsWKJOK+flRVC1BVBZdnKmC01WbUxaonLM1eEswwvaHASKUWueTvZ8gPTe9RGfzxtMb2HxlM/5+9DdkTKZw2A5oOQCR/SPxUvYS7+96H7E3YhE9IrrcEcQEQVQzqlr7xfuzTk7caZuezkeKp083hIU1hokT+Qyo9et5EliRyNAWEcZEWg6XPtOm1S6UC/VUsTSzxPYR29H9h+44ee8kZhyYgW8Hf1t9xlYj5LQl9IfgZZTLeS+6jo48VhVCXrf8fO4UsLQ0rD0EoUoLO4q0JaqQkBBg2zbuuAWUA4LFRdKK8eQJlzVISVGWpaYC7drxZF+qCcRUpQ5UpQ+EqNqdO7VLHSxaxNfCRJJ33+VrTUnFKKqWIEpHW4SsTC5D2IGwMqdJyuVyjIgeodEpO3zrcDjWd6yQsxVQj+wBoFOdNo3a4Oidozhw6wD23tyLi+kX1ep3ceqCJy+e4ODtgzh4+yAAwN3OvYROH0EQRoKq1r4mPv4Y+Pxz/nJBlMro0cCMGcDVq8CpU4CPj6EtIowJlwZc+iwxIxHezbxL7Bf034V6xfFo6IFf3/wVb2x5A6vOrEKvpr0wutPo6jO4mqAnCaE/zM2BBg2AnBzesyWnbaVo0IB3/mUyHm3btObKtBC1EIU8AmnaElXFgAHKvzdu5B7RUrygUinQvTvXpDU359Ec330HHD4MrFgBzJvH6wl9LmEtSB0I0gdCVK0uUge+vsDJk6VruBEEoZ3SImQbWjbUaZrkx/s+1uqU7b2hNyT3JZj96uwyna1lRfZoq5P+PB33pPcU202XNUW+LF+xLRaJMaDVAPRo0gMLji3A8kHL4efqV2mNXoIg9ISq1r53SUcS+vXj606d9GdTDcXWlstR/fwz8P335LQl1PFz9YObnRsWJSxSa9MBQM7kiDweWaaM0LC2w/C53+dYmLAQH8Z+CC8nL3Ryqln/m+S0JfRLo0bcafvkCe/lEhVGJOJ+78ePyWlLGB9CpO096T3ImZySqBCVJyODr+vX59krSqGwEBg+XOmwvXCBPyu/+w6wsuJRs717c4WFv//mUbNCFO3UqUBEhFL6YMAArlGrq9RBWRpuBFGXKUtntjTZgk+9ueBhWc7U9OfpmOs3V6NTNqBVACT3JZAWSEs9B1B2ZA8AbLq0CaYmprj+5DoSHyfi8qPLyMjNUKufL8uHg5UDBrYaiIGtBmKwx2A0smqE4C3Bis6m2ERcQo+PIAgjRVVrX1XTFtBZa78uIpNpTk42aRJ32m7ZAixdyvMOEATAk5EtG7gMoVtDEbwlGOG9wxXvBpHHI7H7xm7M7zsfW69uLXXAc37f+Tjz4AwO3j6IoC1BODPxDBzqOxjgiioGOW0J/dKoEZ+PSsnIqgRVpy1BGBNNGzRFf/f+cLV1xYvCF6hvXt/QJhE1nceP+VpIQlYK06YBhw7xv7//HujYkXcWVPtYS5bwDsOsWcDRo8o+1uefAx06KKUPhg7la5I6IIjKUVoUbVDboDKlD7Zc4SLTujhTtTl2ezTpAQA4+/AsBrcerPUcjvUdEX4oHLNenYWHOQ9xT3oP96X3cTfrLiT3JYr6H+/9uMQ5RBAp2rx3PN/B+gvr0atZL0zpMUXR2Zzw5wTSrCWImoqq1n5wMNdT8vTko786aO3XRWJieO62O3eUZW5u/Da++Sbwyit8gH3DBv5eRhACIe1DED0iGmEHwhQzYQDA0coRjvUdMS9+nqJMU1JSgDt/t4RuQc/1PXHr2S2EbgvFwTEHa0xiMnLaEvrFxoavnz83rB21BEpGRhgrZmIzHBp7yNBmELUJIdK2DKft1q08olbgzTf5ungf65NPePnJk+oJxM6c0Sx9QE5agqg4ZUXRRvSN0En6wNnaudRpks71nZGem67VsWtjwd9D/7z+J15r8RpSs1NxJ+sOUrNTcTebO2RFECEjNwMZuRmIvxuv9ZpMYAI55HCs74h+bv3g5+rHE58kbcfe5L0KTdoAj4ASnU3SrCWIGk5ICHfMhoUpBfGBMrX26yIxMfzdKzCQ5xEQ/NuLFvHy6Gg+y+n994G1a/ktpfctQpWQ9iEIahukmKmTnJmMiPgIBLYJxFy/uVqTkqrS0LIh/hz1J7x/9Maxu8fw6d5PsTZwrYGuqHyIGGPM0EboE6lUCltbW2RnZ8NGcCAS1UfxeRDLlvHRx/XrucAgUSmGDgX++gv48UdgwgRDW0MQVQ89s2s+VfYdrl8PfPghf+vfvVtjlR9+4NPsVN9snJ2BNWuU/SdN0R6mpkBRkXLb3Z1P0aM+F0GUD03yBwDgscoDXo5eGp2twVuCcer+KTzOe4yc8BxYm1uXOG9OQQ5sFttgWq9pWHl6JQLbBJaYJhl7IxZ/hP6BWYdmwdPRE98O+ha3nt3Cjac3kPw0GTee3sCx1GN4/lK3wIH6ZvXxUvYShfJCRZmDlQOm9pyKsZ3HommDpth9Y3eJ6GF3O3csHbhUrcNYmiyEMUFtbs2HvkM9o23OPwGA3x4PD8DLS7OSRHAwd+BevsxzBmRmArt2AW+8YSiLCWNHJpeV+U6RmJGI5I+TNbaze27swbDNw8DAsHrwakzpOUWf5quh6/OaIm2J6kNTz7j+v1OkKdK2ShAibZ89M6wdBEEQ1Y4gj+CgWYMqJgb4z3/43x06cMmDTp14p2D4cKX+bEgIMGwY8NprwM2bPOrDz48SiBFEZdEmfzC+y3idomiBsqUPgtoFwa+FX4nI1cZWjRHcLhh/XP0DcrkcsTdiEXsjVqutJiITmIhMUCQvUjvHRz0+wjue76CZTTPUN69fprO1ePSPNocsadYSRC1FLCYx+1JISOCugM2b1R22AN8OD+eByufO8XiuJUt48ldy2hLaSEhN0OmdIiE1QWO7O7TNUCz2X4zZh2bjk32foKV9S41yScYEZYYhqgdhHoSXF8/0kpPD140a8f2nTxvWvloCySMQBFFnePKErzU4bWUyYPJk/rdYzCUSGjcGVq/mqgpOTnzqXVYWb4qGDwdOnQLWrQP69+fJyvr2BUaN4mty2BJESWRyGeLvxGPzlc2IvxMPmVym2CfIH3g5ekEyQYKc8BxIJkjg5eil0JsrK4GYg5UDFiUsgpzJAQCMMTzNe4rzD8/j032fopFlI8QkxeCXv39BfbP6sBBbKM7xJO8JdvyzA9uTtiNVmqrxcxpZNsKXr32J25/cRsEXBcj/PB9x4+IQFRKFuHFxSA9Lx/y+89G2cVuFJq3gbB3lNQp93fpqjNrRpQ6hHzIzMzF69GjY2NjAzs4OEyZMwPMyAkXy8/MxZcoUNGrUCNbW1hg+fDgePXqkVic1NRVDhw6FlZUVHB0dMXPmTBSpTs8AEB8fj1deeQUWFhbw8PDApk2b1PYfO3YMw4YNQ5MmTSASibBz585S7Zo0aRJEIhFWrFih6+UThNGRlsbXnpof/4rytDRgyhT+/nXkCHDlin7sI2oeaTn8R1XWO4VQTxMzfWfivS7vQc7kGBE9An+n/131hlYhFGlLVD0yGY+wDQxUnwfh7c1DnFasAPbu5fWoZ1wpyGlLEESdoRSn7eHDSsnbWbN44jFAKTk3dSrvENjb83KSnCMIdcqKKK1MErHeG3pDcl+Cv9P/xquurwLgDllpgRT3pPdw8NZBAEBHh47YfWM3nJY6ob5ZfTzOfYy8ojw1O1edWaW2bSIygautK1o3bM2XRq3RplEbtGnUBs1tmkNyX1JqBCxFv9YuRo8ejbS0NBw8eBCFhYUYP348PvzwQ0RFRWk95rPPPsOePXuwbds22NraYurUqQgJCcGJEycAADKZDEOHDoWzszNOnjyJtLQ0jB07FmZmZli0aBEAICUlBUOHDsWkSZPw+++/4/Dhw5g4cSJcXFwQEBAAAMjNzUXnzp3x/vvvI6SMxmfHjh04deoUmjRpUkV3hiAMg4sLXycmcldAcRITlfVcXXkeguhoYNUqLnlFEMVxacB/VGXNzBHqaUIkEuH7wO9xN+su4u7EIXBzIE5PPI0mDYzzmUuatkTVEx8P9OvHw5mKP52//BJYsID/HRdH00kqyapVPJnOW2/xyDKCqG3QM7vmU2Xf4eDBwL59wMaNwHvvqe0aOxb49Vf+0n/zJmBlpX5oVhZ32E6dyqNsSf6AIJSU5pANaR+ilkSseMKP2BuxiOgbgXnx8yCZIFF0oPIK85CanYrU7FQcuX0EX5/8Go5Wjujk1AkPch7gnvSeztqyJiITuNm64ZUmr8DN1g3u9u5wt3OHR0MPtLBrUWOyP9cEanKbm5SUhA4dOuDs2bPo3r07AGDfvn0YMmQI7t+/r9EBmp2dDQcHB0RFRSE0NBQA8M8//6B9+/aQSCTw9vbG3r17ERgYiIcPH8LJyQkAsG7dOsyePRuPHz+Gubk5Zs+ejT179iBR8EABGDlyJLKysrBv374SnysSibBjxw4EBweX2PfgwQP06tUL+/fvx9ChQzFt2jRMmzZN5/tQk79Dovahq6ZtcjJ/L0tIAPr0AerVAx4+VA62E4RAZTVtVXn24hl8N/jinyf/oKtzVxwbf0yjrn51QZq2hOEobR6EoGmrWo+oMEKk7dOnhrWDIAii2hEibRs3Vit+/lyZl2zs2JIOWwBISuLr4cNprJAgVFF1yG4evrlEBmYhsVfxKFpPR0/83+v/h0fPH+F/J/4HAIg8HokH0ge4m30XT/KelPisjLwMHEo5pFZmZmKGQnkhXnF5Bd1duqOZTTM0tWmKZy+eQSQSoaNDR/i39CfJAaJMJBIJ7OzsFA5bAPD394eJiQlOnz6NN998s8Qx58+fR2FhIfz9/RVl7dq1g6urq8JpK5FI4OXlpXDYAkBAQAAmT56Mq1evomvXrpBIJGrnEOqUx9kKAHK5HGPGjMHMmTPRUZgyUgYFBQUoKChQbEul0nJ9JkFUJ2Ixz0MeGsodtOHh3EWQmAhERvL85NHRyoH03r15PoLLl4GffwbK+S9E1AHEJmIsG7gMoVtDEbwlWC0p6aKERYhNjsU072lISE0oM+mnvaU99ryzB94/euNi+kW8s/0d7Hh7h9G9c5DTlqh6SpsHYW1dsh5RYYQBGXo/Iwii1qPFafvttzyS1sSEJx8rLATMzJT75XLeMXB35xG2BFHX0CZ9IJPLtMoaRI+IxpDfh+A/sf/Bs/xn6OPaB+9sfwe3n91GSlaKRqfsn9f/VNu2NrdGC9sWaGDeAKcenMLrbq/jcsZltWOb2TTD0oFLEdKetEqIypGeng5HR0e1MlNTUzRs2BDp6elajzE3N4ednZ1auZOTk+KY9PR0NYetsF/YV1odqVSKFy9ewNLSUqdr+Prrr2FqaopPPvlEp/oAEBkZifnz5+tcnyD0jSBVFRbGk44JaJKqEol4joLJk4G1a4FPP+VlBKFKSPsQRI+ILpGU1NSEuzdXnFqBFadWqM0a0kZL+5b4c9Sf6PdzP+y+sRthB8KwYtCK6r6EckFOW6Lq8fMD3NyARYtKzoMQQqAsLan3XAU0aMDXOTmGtYMgCKLa0aBpu3UrV90BuHP21CnA1hb473+5FIK2SA6CqCtokz5YOmApRCIR7mTdwftd3sfXx79GSlaKwil7N+suZEyZaOyXy7+UOLd9PXu42rri70d/w9LUEq3sW2HB6wvgbucOV1tX2NWzAwND8JZguNu548CYAwBQqnYuQRRnzpw5+Prrr0utkyRMp6jBnD9/HitXrsSFCxcgKoeXKjw8HNOnT1dsS6VSNG/evDpMJIgKExICBAVx+YO0NB67pU2qavRonp/gxg2elKx/f/3bSxg/Ie1DENQ2CAmpCdj1zy6sPL0Sg1oNwud9Pi8xayh6RHSpjlvvZt74JfgXjIgegZWnV6KVfSt83OtjPV5N6ZDTlqh6SpsH8d13vE6LFtR7rgKESFty2hIEUavJzeU6CADXOnBzQ8wuMUaOBBjjUjHJyTxpxVdfAXPn8gWgpGNE3aSgqAAbLm7AlL+moItzFwS8EoCXspe49uQarj6+itBtoYq6X8Z/qfEcgnQBAAxvPxy+zX3hbueu0JS1rWcLyT0JfDf4Yk7vOYiIj8CGixsQ3jscpiamOHX/FCKPRyL2RiyiR0QrnLOU/IsoD2FhYXivmI55cVq2bAlnZ2dkCBkp/6WoqAiZmZlwdnbWeJyzszNevnyJrKwstWjbR48eKY5xdnbGmTNn1I579OiRYp+wFspU69jY2OgcZZuQkICMjAy4uroqymQyGcLCwrBixQrcuXNH43EWFhawsLDQ6TMIwpCIxbpJVDVoAIwZw90G69eT05bQjthEDD9XP4zfNV5rMtTgLcGYcWAGgtoGlTpI/FbHt7D42WLMOTwH0/ZPQ6uGrTCk9RB9XUqpkNOWqB60zYMQXprq1TOMXbUMirQlCKLWExOjLmo2bBhkLVoi7MVl2NjUR3Y28Nln3HE7Zw5vdl57jSck27yZdxBojJCoLciZHE/yniAtJw1pz9PwMOchHuY8xAPpAzzI4ct96X1k5CqdVxfTL+Ji+kWt5+zs1BmdnDqhpX1LhVO2lX0r3Mm6g94be8PZ2hkvZS8xzXtaiYQfkccj4W7njs/9eGRL8amK7nbuZUa4EERpODg4wEFlhoU2fHx8kJWVhfPnz6Nbt24AgCNHjkAul6NXr14aj+nWrRvMzMxw+PBhDB8+HABw/fp1pKamwsfHR3HehQsXIiMjQyG/cPDgQdjY2KBDhw6KOn/99ZfauQ8ePKg4hy6MGTNGoy7umDFjMH78eJ3PQxC1gQkTuNN2504ugVVMwYQgFCSkJuBO1h1sHr5Z7R0F4IlMw3uHw3eDLxJSE8ocNJ716iwkZybjp4s/YdT2UTgz8QzaNm5bjdbrBjltiepD0zwIExPem36uW8ZgonQEp+3z5zzajDR/CIKoVcTE8Fkbfn7AvXuAkxOwcycSpv+JOxKe2NLCAvjPf5SHmJnxyR6+vtxZSw5bwlgRtGbvZd+DpZklWtq1RGZ+JjJyM/Do+SO+zn2E9OfpyvXzR2qyBWXRzKYZ2jZqC1dbVzS3aQ43Oze0sGuBp3lPMSJ6BJytneFq64pNwZtKOGQn75kMdzt3LPFfghHRI0ok/CgeRas6VZHkDwh90759ewwaNAgffPAB1q1bh8LCQkydOhUjR45EkyZNAAAPHjxA//798csvv6Bnz56wtbXFhAkTMH36dDRs2BA2Njb4+OOP4ePjA+9/83IMHDgQHTp0wJgxY7BkyRKkp6fjiy++wJQpUxQRrpMmTcLq1asxa9YsvP/++zhy5Ai2bt2KPXv2KOx7/vw5bt68qdhOSUnBpUuX0LBhQ7i6uqJRo0Zo1KiR2jWZmZnB2dkZbdsa3mlAEPqka1egQwfg2jUeBzZxoqEtIoyVtBye3N7T0VPjfqFcqFcaIpEI3w39Dv88+Qcn7p1A8B/BOD3xNGwsbKrO4ApATluieik+D+LSJb7OzTWENbUOwWnLGL+lqnneCIIgajQyGQ+bDQwEpkwBjh3jerbe3kib0hOQ8Goj3pLDwUF9ZN3z3/e2tLLfzwiiSngpe4mcghxIC6TILsiGtECKrPwsxfLsxTNkvshEZn4mnuY9RXJmMu5k3UGRvKhCn+dg5YAmDZpAJBLhZuZNPH+pHAxv0qAJhrUZhu/Pf4+kKUmwNi/5cpBTwKfojOw4EitPryzVIRvSPgTRJiUTfmiKohWbiEn+gDAYv//+O6ZOnYr+/fvDxMQEw4cPx7fffqvYX1hYiOvXryMvL09Rtnz5ckXdgoICBAQE4DtBzg2AWCxGbGwsJk+eDB8fH9SvXx/jxo3DV199pajj7u6OPXv24LPPPsPKlSvRrFkz/PjjjwgICFDUOXfuHPr166fYFnRox40bh02bNlXH7SCIGotIBIwdy2dQ/fILOW0J7bg04MntEzMS4d3Mu8T+xIxEtXplYS42R/SIaHT/oTv+efIP3o15V012wRCIGGPMYJ9uAKRSKWxtbZGdnQ0bG8N6zOskN28CrVtzb6NUamhrajyMAaamPAHPw4c8mJkgahP0zK75VPg7jI8H+vUDJBLgzh1g1Ci+feQIDhwAhL7woaWX0D+si9qhEgmPtI2L000/jag9yOQyFMoLUSgrRKG8EC9lLxVLQVEBCmQFauv8onzF8qLoBV4UvlCs8wrz+FKUp/y7MA+5L3Px/OVz5Bby9fOXz/FS9rJSdosggpnYDC9lL+Hp4AlPJ084WjnCob4DsvOzIYIIrRq2wmCPwXBp4AIzsRlikmIQujUUgW0CMddvrlrijd03dgMAJBMkGjsxghZt3Lg4ZL7ILJGszN3OHUsHLlVzyAqRwRRFW3uhNrfmQ98hUVu4fx9wdeX93du3eY4CgiiOTC6DxyoPeDl6lXCuypkcwVuCkZiRiOSPk8v1znLu4Tn03tAbBbICfNnnS8zvN7/Kbdf1eU2RtoR+EUJBaT5/lSAS8VsqlXJdW3LaEgRRaxDCZD09gXPn+N//Th3Nzuab9fACfRz/AdBFcZhcDkRG8pd7Pz/9mVuXYYwhrzAP0gIpcl7mKByZuS9zkVuYq3B2qjpE84vy1RyoCsfqv3+rOl2FvwtlhSiSF6FQ/u9apVxYMxg2FsHKzAo2FjawsbCBrYUt7C3tYVfPDnYWdmhk1Qh29ezwv5P/g7udO5YNXAbH+o5obNUY9pb2AKDoXPz25m/YdX1XCWeqm50blg1chqC2QQg7EKY18UbQ5iDsu7UPC48txK5Ru7Rq0QqOV11kDSiKliAIgqgqZDJ1FUU/P3VJq2bNFGP12L4dmDHDcLYSxovYRIxlA5chdGuoxllDu2/sxvy+87H16tZyDTh3b9Id64etx9idY7Hg2AK87v46XnN7TQ9XVBJy2hL6RXDaMga8eAFYWRnWnlqAELRMycgIgqhVCKNQiYnA06f873+dtr//zjfzUQ/D1/kj3IP7dhMTucM2NpZroJGere7ImRzZ+dl4+uIpMl/wKfyZLzLxLJ9P63/24hme5fMlKz8L2fnZiqn/OS9zIGdyQ1+CVizEFrAwtYC52Fzxt7C2NLVEPdN6ir8tzSxhIbbAsxfPUCQvQiOrRmjXuB0amDeAlZmVYrE0tURyZjKeFz5HC5sW6N+yP2zr2cLUhL9aa4tKjb8Tjyd5T7B71G6NEbBCwoyFCQsRER+BwDaB2Dx8s1oUbejWUET0jSg18cZcv7mITY7FnuQ9ZWrRAuSQJQiCIPRHTAxXwLpzR1nm5sZzEoSo5K0MCeFO25gYctoS2glpH4LoESVlnBytHOFY3xHz4ucpyoTBb10SpI7pPAbxd+Kx4dIGvLvjXfw96W80tGxYLddQGuS0JfSLqpP2+XNy2lYBgq4tqU0QBFGr8PPjb/CLFvH5cQBkDR2wbw8QG8sAiLDM/v+w6uEX8FW+n8HdnTtsQwyQrN5Ypo8XyYsUjtcneU/w9MW/a5Xtpy+e4nHuY9yX3kdWfhaev3xe6ShVEURoYN4ADSwawNrcGvXN66O+WX1YmloitzAXDAx2FnZoYdcCVmZW3Fn6r/PUzMQMqdmpeF74HA6WDvB08oSF2AJmYjOYi81hAhMkPUlCVn4WnOo7oXuT7qhnVg9mJmYwE5vh4O2DWHhsIe5J7ynscbNzw9IBSzG8w3BFWVnfUUxSjNboVuEFPyYpBp/EfqK1TmnnKCgqAFB2wozVZ1ZrjaIN3hKM1WdW63SeT3t9ip3Xd5apRUsQBEEQ+kDIMRsYCGzerBx0X7SIl6u+wwUHA1OnctkrISKXIDRRPBlqcmayYvC7uIRU6NZQnd+DVg5eiYTUBCRnJuOD3R8g+q1oiPQ8W5w0bQn9Y23Ns2bdvAm0amVoa2o8PXsCZ88Cu3YBb7xhaGsIomqhZ3bNp1LfofBm36QJYh70RJj9Btx5ZqfY7eaYiyWr68PBQfv0uqqiKpx95f0cJ2sndHbqDGmBlCexepGpcMBeeHgBac/TUMSKYGZihswXmQqHbFZ+VoWvs55pPTjWd4SJyATpz9ORX5Sv2Gdfzx4jOo7A6+6vw66eHS6mXcSqM6vwIOeB1mvW1QlaWh1d9mvTdlVNplUV5wFQap0ZvjOw9ORSrfsj+kZgXvy8MrVmgbL1aHWpEzcuDn6ufkYxmEAYP9Tm1nzoOySMGZkM8PAAvLyAnTsBE5WJInI5d9ImJgLJycp3OW9v4PRp4LvvgMmTDWE1UdOoap3b8w/Pw+cnHxTKC7F+2HpMfKVqMuPp+rwmpy2hfxwdgcePgStXlCm+iVIpTfOnf38+beS334DRow1rJ0FUNfTMrvlU+juMiUHMyK0ILYxCIGKRDwscRADe7XMX2bYtFFII1RlZW1ln3y9v/gI/Vz88y3+GJ7lPcPL+SdyT3oMYYthZ2iE7PxvP8p/hnyf/IOlJUqWTWmnC0tQSvZr2wisuryD9eTo2J25GN5duGN91PLo36Y6M5xlYd34d/kr+q0zHoy7Oy6qqU5Ytf4T+gVmHZpX5Yr7EfwlGRI+o1HmuPLoCiKC1jqAjO6jVII06srqcI3hLME7dP4XHeY+RE54Da3PrEt9lTkEObBbbwMHKQRF9W1WJN4i6DbW5NR/6DgljRjXHrHfJ8UaNiWSXLAFmzwYGDAAOHNCntURNJf5OPPr93E+ngW1dpaGWnFiC2Ydmw8rMClcmX0FL+5aVtpOctlqghswIaNECSE0FzpwBevQwtDVGT1maP8HBPMp27Vpg0iQDGUkQ1QQ9s2s+lf0OZTLAw+oBvF6ex7bPJHD6YQGyc01x4gR/4dcUlVGVCA7ZoW2G4uOeH8PRyhFnHp7BTxd+wtmHZ/Fup3fx5/U/YVfPDt7NvLnu67/6r5l5mcjMz6wSOxpZNoK1uTXuZt+Fs7UzejbtiVb2rfBS9hLHU4/j8qPLWNBvARqYN8C0/dMwtPVQfN7n83I7J6vK8VgVdXSx5eyDs0jPTS/zxdzZ2hk9mvTQaouu5wG0R7euObMGU/dOxZoha/BRj4+0nmN+3/mKKXuatGZ1jcYt6zwkgUCUF2pzaz70HRLGzObNwDvv8Fws1iXHJJGTA9jYAFFRwKhRvCwlBfjjD97vbdNGv/YSNZPNVzbjnZh3yhz8jgqJwiivUTqdU87keP3n13H07lEMaT0EsaNiKy2ToOvzmjRtCf1jacnXeXmGtaMGoIvmj6BpS4nICIKojSQkAHdeNsVmDEeC+0Zk55rC0RHo1YtPqwsP51EZCQnKqIzykp2fje3XtuPak2sokhfB0tQS6bnpeCB9gKN3j8JcbI69yXsReyO2xLG/Xv6Vn6MgG3ez72r9DLFIDBmTwdrcGu527mjSoAkA4MbTG0jJSoG1uTU8Gnpg3mvz0MiyERpaNkRDy4awrWeLkdEjFQ7OYW2GaXU8/njhR0CEUrVQP9n7CdJz07UmsBrkMQixybEY3Hqwxv1CoiwAWs9RVXXKY0tZ2q7pz9Mx129upc9TWh1LM/5+Y2lqWeo5WjdsrTFhhqA1G9Q2CBsvbcSihEUav+vI45Fwt3PH537cKa/tPOSwJQiCIIwJ1RyzmiJtExPV6wE8V8GcOdVvG1F7cGnAf0CJGYkaB78TMxLV6umCicgE6wLXodPaTvgr+S/s+GeH3t6zyGlL6B8h+diLF4a1w8iRyXiEbWCguuaPtzffDg7mWTQHDuTl5LQlCKI2kpbG155IxNxz/OVq2DBlVK2gsiPUK438onycf3geF9Mv4mrGVVx9fBV/p/8N6UvdMzk2MG+ARlaN0NiqMcQiMU4/OA0AmPPqHDhZO8G+nj0aWjaEvaU97OvZw0xshrar26KhZUOtU9l7b+gNyX0JVg9ejVddXy3xmdXhKK2s47G0c1RVnfLYUtaLua626HIebXVeFPL3mhdFmt9vVDsJfd36qiXMKK41u2zgMoRuDUXwlmCtUbRiE3GJxBukWUsQBEEYK6o5ZjVp2kZGcietn5+hLCRqA36ufnCzcytz8NvPtXw/tHaN22H2q7Pxfwn/h0/2foIBLQeggUWDqja/BOS0JfQPRdrqREICl0TYvFm9QQPUo8uys3kZOW0JgqiNuDR6CcAcifDEwVP8xWjIEOV+TVEZAjK5DCfvncSu67vwV/JfuPH0BmRMpvFzGpg3QEv7lrCtZ4vU7FTczbqLAS0H4MDtAzg+/jjc7NzQ2KoxLEwtFMcI06sAIKhdkNap7ADwOO+x1kjPgFYBkNyXQFqg2XlcHY7SyjoeSztHVdXR1RZna+dSX8yd6zsjPTe9TFvKOo+brRsggtY6+27ug6mJKfYm78Wk7pPK7CSITcRatdRC2oeUGo2rGt1R2nkIgiAIwlgQi7nEX2goD0AKD1fOJI2MhCJPQXXIXRF1B7GJWOvg96KERYhNjsU072lISE0o90D3XL+5iEqMwu1ntzEvfh6+CfimGq+EY1J2FYKoYijSVicU0WVacrUJ5bJ//Q/ktCUIojbi1+4x3JCC/2IB/rkhhokJT2IBaI7KYIzheOpxvL/rfTgvc0afTX2wTLIMSU+SFA5bS1NLBLcNRmOrxujj2geZszIhDZfi0qRLOPreUdz65BYC2wTi8qPLAPjLX1ObpmoOW6Cks0/O5Gr7BUedg5UDAO3O1B5NuL772YdnNe4v7uCsbJ3S7FV1PGq7HjdbN0UEQ3XW0cUWdzt3rBq0CrE3YhG8JRiSexLkFORAck+C4C3BiL0Ri28Hf1umLbqcZ1nAMiwbuExrnT3Je/CZ92fYk7xH6zmWDlyqc+cgpH0Ibn58E3Hj4hAVEoW4cXFI/jiZZA8IgiCIGktICHfMXrnCA5BsbPg6MbH6E8sSdQdh8PtKxhX4bvCFzWIb+G7wxb5b+wAAK06tQL+f+8FjlQdikmJ0Pq+lmSXWDFkDAFh5eiUupV+qDvPVIKctoX+ESFty2paKquaPJoRyJye+JqctQRC1EXHWUyxDGA6jPwCgbVvA1JRnGA4OBmJjGd6blYioxF/x+eHP0WN9D/ht9MPGSxvxJO8JAKCZTTPMe20eLk+6jJPvn4R/S3/svL4TT/Ke4OsBX8Pe0l7tMwUpgfTc9DIdsro4+6b2nApAuzPVxoJH6+6/ub/aHaVl2auL47Es52VV1dHVCRraMVTji3liRiKiR0TjrY5vlWmLLucJaR+itRMg1FkyYEmZ5ygPQhTtKK9R6OvWl2QPCIIgiBpPSAhw8yYQF8eTjsXF8YSy5LAlqhLVwe9pvaZBBBEGtRoEyQQJcsJzIJkggZejF0K3hpbLcTvIYxDe6vAW5EyOJSeWVOMVcESMMVbtn2JEUEZNI+Dtt4GtW4GVK4FPPjG0NUaLTAZ4eABeXpo1f4SM6Z9/DkycCAweDPz1l6GsJeoCMrlM77qJ9Myu+VT6OzxyBOjfH30szyDhRQ+1XY7NcsEGhOFxi+/Vyk1NTDGm0xjsvbkX3V26Y9eoXSWmqvf4oQcupF9A1uws2NazLfGxgvTBtF7TsPL0SgS2CdSqLRrSPgQxSTEIOxCGO1l3FOdwt3PH0oFLEdQ2CB6rPODl6KU1idjp+6fxOO9xqZ8DAKFbQytdpyx7ddkPQG91dDkHUPYzqqrOo0sdQzwvCaKyUJtb86HvkCAIQh2ZXFbme3hiRiKSP07W+V3tYc5D/HThJ8x8dSbqmdarkF26Pq/JaUvon/HjgU2bgMWLgdmzDW2NURMTwzV/AgO1a/7I5cBbbwG9e3MdXIKoDjQ5O9zs3LBs4LJqnapLz+yaT6W/w23bwEaMgIvZEzwqbITly/kMg+SXxzDv9utoauuMBzkPAAD29ezhVN8J159eR0TfCMyLnwfJBIlGHdM1Z9Zg6t6pWDNkDT7q8VGJ/ZJ7Evhu8EXcuDhkvsistLMvJilGJ4ervhylZdmry3591qkqJyg5UwlCO9Tm1nzoOyQIglAn/k48+v3cT2ufQPWdX585AshpqwVqyIyAjz4C1q4F5s0DIiIMbY3RExMDhIXxpGQC7u7A0qV8Csn+/cCgQUDnzsClS4aykqjNqDqb5vrNVRdyV4ncqw7omV3zqfR3uG4dEievhhcSYWkJPHsGmJrJ0Gx5MzzNe4pCeSFMRCb4uOfHWNBvAeqb10fwlmCcun8Kj/MeIyc8B9bm1iVOm/UiC/ZL7PGK8ys4++HZMkfdq8LZp4szVZ+OUoIgCFWoza350HdIEAShzuYrm/FOzDta+wTC7LqokCiM8hqlN7t0fV6b6s0ighAQNG3z8gxrRw0hJAQICuJRtGlpXOvWz0+ZVbMBT6ZOmrZEtSCTyxB2IAyBbQLVppN4N/PGzpE7EbwlGDMOzEBQ2yByCBHVw9OniEdfAHxGgYUF8GXcfKQ/TwcAdHHugvXD1qN7k+6KQ8J7h8N3gy8AriOraVQ96UkSAOBi+sUSmWVVo1+F37WgLVoZQtqHIKhtUKnOVF0+p6rqEARBEARBEERtxqUBTxakrU8g5JwQ6hkbBk1EtnbtWnTq1Ak2NjawsbGBj48P9u7dW+ox27ZtQ7t27VCvXj14eXnhLxLxrHlYWfE1JSLTGbEY6NsXGDWKr8UqvjFy2hLVSUJqAu5k3cFcv7lqkYiAMllTSlYKElJJm4OoBmQy4NIlHEMfAMBrfnJ8I/kGC44tAAAEtQ2CZIJEzWELAJ6OngAAByuHMpNybQ3dWmVJo3SBEksRBEEQBEEQhH7wc/XTKVGvn6ufgSwsHYM6bZs1a4bFixfj/PnzOHfuHF5//XUEBQXh6tWrGuufPHkSo0aNwoQJE3Dx4kUEBwcjODgYiYmaszETRgpF2lYp5LQlqpO0nDQASidYcYRyoR5RO1mzZg3c3NxQr1499OrVC2fOnKn+D42JgaxVG8RFP8Z+DAQA3Nz3BsIOhCmqzPTVLP4vjJhP7TkVsTdiEbwlGJJ7EuQU5EByT4LgLcGIvRGLpQOXIrRjqCKzbFRIFOLGxSH54+Rq1WomCIIgCIIgCKL6EZuIsWzgsjL7BMYaSGFQeYRhw4apbS9cuBBr167FqVOn0LFjxxL1V65ciUGDBmHmzJkAgAULFuDgwYNYvXo11q1bpxebiQogk6nP7a/3bwebIm2rBMFpm58PFBYCZmaGtYeoXdT06SRE5fnjjz8wffp0rFu3Dr169cKKFSsQEBCA69evw9HRsXo+NCYGMcN/R5jlSdyBk6J409XVQDNzLOpniR8KTuLrE19jZ/OSWWCFEfPP/T6Hp6Mnwg6EKeQSAK4jqxpJS1ICBEEQBEEQBFE7CWkfgugR0SX6BG62bojoG4GCogLE34k3yhwQRqNpK5PJsG3bNuTm5sLHx0djHYlEgunTp6uVBQQEYOfOnXqwkKgQmrJoNWrE1+S0rRIEpy3Ao20bNjScLUTtQ3U6iaqmLVAzppMQleebb77BBx98gPHjxwMA1q1bhz179mDDhg2YM2dO1X+gTIaYSQcQim2o1/IIYP8TcHwu4HwBsLkPbI1G2/gpWHbkfwiNHlGmHq0uOrIEQRAEQRAEQdReivcJkjOTsfHSRsyLn6eo42bnhmUDlxnVjDuDyiMAwJUrV2BtbQ0LCwtMmjQJO3bsQIcOHTTWTU9Ph5OTk1qZk5MT0tPTtZ6/oKAAUqlUbSH0REwMEBoKeHkBEgn3KEokQPPmfH9KimHtqyWYm/MFIIkEouqp6dNJiMrx8uVLnD9/Hv7+/ooyExMT+Pv7QyKRVMtnyuITMDlrNlibWPQPX4XXklvzHW32wGnCJKBNLCY/m4mg9MaIHhGtkx4t6cgSBEEQBEEQRN1G6BNYmFogIj4CXo5ekEyQICc8B5IJEng5eiF0ayhikmIMbaoCg0fatm3bFpcuXUJ2djaio6Mxbtw4HD16VKvjtrxERkZi/vz5VXIuohzIZDzCNjAQ2LkTMPl3fMDbG1i6FBg5kkffymTqWbWICjFgACCXK28zQVQl2qaTFJ9iTtQ+njx5AplMpnHA9J9//tF4TEFBAQoKChTb5R0sjY8rREahO3qP/Ba/Df8FDpOfAQBadriDi5/+gyHPI3BiwRuIj7uFkP+jKFqCIAiCIAiCIHRDJpch7EAYAtsEqs0k9W7mjZ0jdyJ4SzBmHJiBoLZBRtGnMLjT1tzcHB4eHgCAbt264ezZs1i5ciW+//77EnWdnZ3x6NEjtbJHjx7B2dlZ6/nDw8PVJBWkUimaC5GeRPWRkMCdsps3l/Qk9u8P/Pkn4OvL6/XtawgLaxWxsYa2gKjt0BRzQlcqO1ga/6IIADC/S2fcT3sOVmgFiIqwZ/KnsLGwQURnLwz4t15/kB4tQRAEQRAEQRC6kZCagDtZd7B5+GY16T8AMBGZILx3OHw3+CIhNcEo+hhGF5cnl8vVInRU8fHxweHDh9XKDh48qFUDFwAsLCxgY2OjthB6IO3fTPKemjPOK8rTKOM8QdQUaIp53aNx48YQi8XlGjANDw9Hdna2Yrl37175PrT9v+PJy/9GxxYuyMysh/1HrqCdpyefUrDyino9giAIgiAIgiAIHUjL4T4oT0fNviqhXKhnaAzqtA0PD8exY8dw584dXLlyBeHh4YiPj8fo0aMBAGPHjkV4eLii/qeffop9+/Zh2bJl+OeffxAREYFz585h6tSphroEQhsu/2aST0zUvF8od6GM8wRBEMaKubk5unXrpjZgKpfLcfjwYa0DppUdLO3bzwywS8G8s/0gD3oTDS5dxcBuHsDp05AHvYn5Z/sCdrd5PYIgCIIgCIIgCB1xacB9UIkZmn1VQrlQz9AY1GmbkZGBsWPHom3btujfvz/Onj2L/fv3Y8CAAQCA1NRUpKlEYvr6+iIqKgo//PADOnfujOjoaOzcuROe2qI5CcPh5we4uQGLFvHIKFXkciAyEnB35/UIgiAIo2X69OlYv349fv75ZyQlJWHy5MnIzc3F+PHjq+Xz+rr7weHNr3G8IBBBh6dC4jsdOTZNIPGdjqAjH+N4QSAc3/wf+rpT+0EQBEEQBEEQhO74ufrBzc4NixIWQc7UfVVyJkfk8Ui427nDz9U4+hoGnVv4008/lbo/Pj6+RNlbb72Ft956q5osIqoMsRhYtgwIDQWCg4HwcC6JkJjIHbaxsUB0NCUhIwiCMHLefvttPH78GF9++SXS09PRpUsX7Nu3r0RysqpCbCLGupkDMTz3LRyO+w6xLySKfVb104HAt7B25miS5yAIgiAIgiAIolyITcRYNnAZQreGInhLMMJ7h8PT0ROJGYmIPB6J2BuxiB4RbTR9DRFjjBnaCH0ilUpha2uL7Oxs0rfVBzExQFgYT0om4O4OLF0KhFDGeYIgSoee2TWfin6HMUkxmL5vJu7+3Rx47gJYp8Gt830sG7QEIe2p/SAIgqhqqM2t+dB3SBAEoRsxSTEIOxCGO1l3FGXudu5YOnCpXvoauj6vjS4RGVHLCAkBbt4E4uKAqCi+Tk4mhy1BEARRKiHtQ3Dr0xuIi4hA1Lw3EBcRgZufXieHLUEQBKGRzMxMjB49GjY2NrCzs8OECRPw/PnzUo/Jz8/HlClT0KhRI1hbW2P48OElkm+mpqZi6NChsLKygqOjI2bOnImioiK1OvHx8XjllVdgYWEBDw8PbNq0SW3/sWPHMGzYMDRp0gQikQg7d+7UaE9SUhLeeOMN2Nraon79+ujRowdSU1PLfS8IgiCI0glpH4KbH99E3Lg4RIVEIW5cHJI/Tja6vgalXiaqH7EY6NvX0FYQBEEQNQyxiRh93foa2gyCIAiiBjB69GikpaXh4MGDKCwsxPjx4/Hhhx8iKipK6zGfffYZ9uzZg23btsHW1hZTp05FSEgITpw4AQCQyWQYOnQonJ2dcfLkSaSlpWHs2LEwMzPDokWLAAApKSkYOnQoJk2ahN9//x2HDx/GxIkT4eLigoCAAABAbm4uOnfujPfffx8hWoJXbt26hd69e2PChAmYP38+bGxscPXqVdSrV6+K7xRBEAQB1Iy+BskjEARBEEYLPbNrPvQdEgRB1Axq8vM6KSkJHTp0wNmzZ9G9e3cAwL59+zBkyBDcv38fTZo0KXFMdnY2HBwcEBUVhdDQUADAP//8g/bt20MikcDb2xt79+5FYGAgHj58qNByX7duHWbPno3Hjx/D3Nwcs2fPxp49e5CYqMxEPnLkSGRlZWHfvn0lPlckEmHHjh0IDg5WKx85ciTMzMzw66+/Vvg+1OTvkCAIoi5B8ggEQRAEQRAEQRBErUcikcDOzk7hsAUAf39/mJiY4PTp0xqPOX/+PAoLC+Hv768oa9euHVxdXSGRSBTn9fLyUku+GRAQAKlUiqtXryrqqJ5DqCOcQxfkcjn27NmDNm3aICAgAI6OjujVq5dWGQWBgoICSKVStYUgCIKoPZDTliAIgiAIgiAIgqixpKenw9HRUa3M1NQUDRs2RHp6utZjzM3NYWdnp1bu5OSkOCY9PV3NYSvsF/aVVkcqleLFixc62Z+RkYHnz59j8eLFGDRoEA4cOIA333wTISEhOHr0qNbjIiMjYWtrq1iaN2+u0+cRBEEQNQNy2hIEQRAEQRAEQRBGx5w5cyASiUpd/vnnH0ObWWnkcjkAICgoCJ999hm6dOmCOXPmIDAwEOvWrdN6XHh4OLKzsxXLvXv39GUyQRAEoQcoERlBEARBEARBEARhdISFheG9994rtU7Lli3h7OyMjIwMtfKioiJkZmbC2dlZ43HOzs54+fIlsrKy1KJtHz16pDjG2dkZZ86cUTvu0aNHin3CWihTrWNjYwNLS8syrxEAGjduDFNTU3To0EGtvH379jh+/LjW4ywsLGBhYaHTZxAEQRA1D3LaEgRBEARBEARBEEaHg4MDHBwcyqzn4+ODrKwsnD9/Ht26dQMAHDlyBHK5HL169dJ4TLdu3WBmZobDhw9j+PDhAIDr168jNTUVPj4+ivMuXLgQGRkZCvmFgwcPwsbGRuFg9fHxwV9//aV27oMHDyrOoQvm5ubo0aMHrl+/rlZ+48YNtGjRQufzEARBELULctoSBEEQBEEQBEEQNZb27dtj0KBB+OCDD7Bu3ToUFhZi6tSpGDlyJJo0aQIAePDgAfr3749ffvkFPXv2hK2tLSZMmIDp06ejYcOGsLGxwccffwwfHx94e3sDAAYOHIgOHTpgzJgxWLJkCdLT0/HFF19gypQpigjXSZMmYfXq1Zg1axbef/99HDlyBFu3bsWePXsU9j1//hw3b95UbKekpODSpUto2LAhXF1dAQAzZ87E22+/jT59+qBfv37Yt28fdu/ejfj4eD3dRYIgCMLYqHNOW8YYAFBmTYIgiBqA8KwWnt1EzYPaXYIgiJpBTW9zf//9d0ydOhX9+/eHiYkJhg8fjm+//Vaxv7CwENevX0deXp6ibPny5Yq6BQUFCAgIwHfffafYLxaLERsbi8mTJ8PHxwf169fHuHHj8NVXXynquLu7Y8+ePfjss8+wcuVKNGvWDD/++CMCAgIUdc6dO4d+/foptqdPnw4AGDduHDZt2gQAePPNN7Fu3TpERkbik08+Qdu2bbF9+3b07t1b53tAbS5BEETNQNc2V8RqaqtcQe7fv09ZNQmCIGoY9+7dQ7NmzQxtBlEBqN0lCIKoWVCbW3OhNpcgCKJmUVabW+ectnK5HA8fPkSDBg0gEokqdA6pVIrmzZvj3r17sLGxqWILazd07yoO3buKQfet4hjDvWOMIScnB02aNIGJiYlBbCAqR2XbXWP4HdZU6N5VHLp3FYfuXcUwhvtGbW7Nh9pcw0H3ruLQvas4dO8qjqHvna5tbp2TRzAxMamykWMbGxv6x6ggdO8qDt27ikH3reIY+t7Z2toa7LOJylNV7a6hf4c1Gbp3FYfuXcWhe1cxDH3fqM2t2VCba3jo3lUcuncVh+5dxTHkvdOlzaUhVIIgCIIgCIIgCIIgCIIgCCOCnLYEQRAEQRAEQRAEQRAEQRBGBDltK4CFhQXmzZsHCwsLQ5tS46B7V3Ho3lUMum8Vh+4dYQzQ77Di0L2rOHTvKg7du4pB940wBuh3WHHo3lUcuncVh+5dxakp967OJSIjCIIgCIIgCIIgCIIgCIIwZijSliAIgiAIgiAIgiAIgiAIwoggpy1BEARBEARBEARBEARBEIQRQU5bgiAIgiAIgiAIgiAIgiAII4KctlpYs2YN3NzcUK9ePfTq1Qtnzpwptf62bdvQrl071KtXD15eXvjrr7/0ZKnxUZ57t2nTJohEIrWlXr16erTWODh27BiGDRuGJk2aQCQSYefOnWUeEx8fj1deeQUWFhbw8PDApk2bqt1OY6S89y4+Pr7Eb04kEiE9PV0/BhsJkZGR6NGjBxo0aABHR0cEBwfj+vXrZR5HzzqiOqA2t+JQm1t+qM2tONTmVgxqcwljgtrcikNtbsWgdrdiUJtbcWpTu0tOWw388ccfmD59OubNm4cLFy6gc+fOCAgIQEZGhsb6J0+exKhRozBhwgRcvHgRwcHBCA4ORmJiop4tNzzlvXcAYGNjg7S0NMVy9+5dPVpsHOTm5qJz585Ys2aNTvVTUlIwdOhQ9OvXD5cuXcK0adMwceJE7N+/v5otNT7Ke+8Erl+/rva7c3R0rCYLjZOjR49iypQpOHXqFA4ePIjCwkIMHDgQubm5Wo+hZx1RHVCbW3Goza0Y1OZWHGpzKwa1uYSxQG1uxaE2t+JQu1sxqM2tOLWq3WVECXr27MmmTJmi2JbJZKxJkyYsMjJSY/0RI0awoUOHqpX16tWL/ec//6lWO42R8t67jRs3MltbWz1ZVzMAwHbs2FFqnVmzZrGOHTuqlb399tssICCgGi0zfnS5d3FxcQwAe/bsmV5sqilkZGQwAOzo0aNa69CzjqgOqM2tONTmVh5qcysOtbkVh9pcwlBQm1txqM2tGqjdrRjU5laOmtzuUqRtMV6+fInz58/D399fUWZiYgJ/f39IJBKNx0gkErX6ABAQEKC1fm2lIvcOAJ4/f44WLVqgefPmCAoKwtWrV/Vhbo2GfnOVp0uXLnBxccGAAQNw4sQJQ5tjcLKzswEADRs21FqHfndEVUNtbsWhNld/0G+u8lCbqw61uYQhoDa34lCbq1/od1c5qM0tSU1ud8lpW4wnT55AJpPByclJrdzJyUmrFkh6enq56tdWKnLv2rZtiw0bNmDXrl347bffIJfL4evri/v37+vD5BqLtt+cVCrFixcvDGRVzcDFxQXr1q3D9u3bsX37djRv3hx9+/bFhQsXDG2awZDL5Zg2bRpeffVVeHp6aq1HzzqiqqE2t+JQm6s/qM2tONTmloTaXMJQUJtbcajN1S/U7lYManM1U9PbXVODfjpR5/Hx8YGPj49i29fXF+3bt8f333+PBQsWGNAyorbStm1btG3bVrHt6+uLW7duYfny5fj1118NaJnhmDJlChITE3H8+HFDm0IQRDVCbS6hb6jNLQm1uQRRN6A2l9A31OZqpqa3uxRpW4zGjRtDLBbj0aNHauWPHj2Cs7OzxmOcnZ3LVb+2UpF7VxwzMzN07doVN2/erA4Taw3afnM2NjawtLQ0kFU1l549e9bZ39zUqVMRGxuLuLg4NGvWrNS69KwjqhpqcysOtbn6g9rcqoXaXGpzCcNAbW7FoTZXv1C7W3XU5TYXqB3tLjlti2Fubo5u3brh8OHDijK5XI7Dhw+rjZSp4uPjo1YfAA4ePKi1fm2lIveuODKZDFeuXIGLi0t1mVkroN9c1XLp0qU695tjjGHq1KnYsWMHjhw5And39zKPod8dUdVQm1txqM3VH/Sbq1qozaU2lzAM1OZWHGpz9Qv97qqOutjmArWs3TVoGjQjZcuWLczCwoJt2rSJXbt2jX344YfMzs6OpaenM8YYGzNmDJszZ46i/okTJ5ipqSlbunQpS0pKYvPmzWNmZmbsypUrhroEg1Heezd//ny2f/9+duvWLXb+/Hk2cuRIVq9ePXb16lVDXYJByMnJYRcvXmQXL15k+P/27h00qm2NA/g3RoPoxEQNJFpIfCKIj4hY2CgWgoJgo2ARYgTBeBCnERUkdoKFL9RGwUepINiICmJsBFFRfIDEF1iFiIqipovrFJcEguce7sxN9mzj7wcbJnv2yvpmkZk/fDOzEpGOHTuWnjx5kt6/f59SSmn//v2pra1t6Pp3796lSZMmpb1796aXL1+mM2fOpJqamnTz5s1qPYSqKXftjh8/nq5du5Zev36dnj9/nvbs2ZPGjRuXbt++Xa2HUBWdnZ2pvr4+3b17N/X29g4d/f39Q9d4rSMLMrdyMrcyMrdyMrcyMpe8kLmVk7mVk7uVkbmVG0u5q2n7X5w6dSrNmjUr1dbWppUrV6b79+8P3bd69erU3t4+7PorV66kBQsWpNra2rRo0aJ0/fr1jCvOj3LWrlQqDV3b1NSUNmzYkB4/flyFqquru7s7RcQvx+Batbe3p9WrV/8yZtmyZam2tjbNmTMnXbhwIfO686DctTty5EiaO3dumjhxYpo2bVpas2ZNunPnTnWKr6J/WrOIGPZ35LWOrMjcysnc8sncysncyshc8kTmVk7mVkbuVkbmVm4s5W4hpZRG8pO7AAAAAABUzp62AAAAAAA5omkLAAAAAJAjmrYAAAAAADmiaQsAAAAAkCOatgAAAAAAOaJpCwAAAACQI5q2AAAAAAA5omkLAAAAAJAjmrZQJdu2bYtNmzZlPu/FixejUChEoVCIUqk0dL6lpSVOnDjxr2MHxzU0NIxqjQAw0uQuAGRD5sLIGF/tAmAsKhQK/3r/oUOH4uTJk5FSyqii4aZMmRI9PT0xefLkssb19vbG5cuX49ChQ6NUGQCUT+4CQDZkLmRH0xZGQW9v79Dty5cvR1dXV/T09AydKxaLUSwWq1FaRPwnaJubm8se19zcHPX19aNQEQBUTu4CQDZkLmTH9ggwCpqbm4eO+vr6oeAYPIrF4i9fGVmzZk3s3r07SqVSTJ06NZqamuLcuXPx48eP6OjoiLq6upg3b17cuHFj2FwvXryI9evXR7FYjKampmhra4uPHz9WVHd/f39s37496urqYtasWXH27Nn/ZxkAIBNyFwCyIXMhO5q2kCOXLl2KxsbGePDgQezevTs6Oztj8+bNsWrVqnj8+HGsW7cu2traor+/PyIivnz5EmvXro3W1tZ49OhR3Lx5M/r6+mLLli0VzX/06NFYsWJFPHnyJHbt2hWdnZ3D3jUFgLFE7gJANmQulE/TFnJk6dKlcfDgwZg/f34cOHAgJk6cGI2NjbFjx46YP39+dHV1xadPn+LZs2cREXH69OlobW2Nw4cPx8KFC6O1tTXOnz8f3d3d8erVq7Ln37BhQ+zatSvmzZsX+/bti8bGxuju7h7phwkAuSB3ASAbMhfKZ09byJElS5YM3a6pqYnp06fH4sWLh841NTVFRMSHDx8iIuLp06fR3d39j3sGvX37NhYsWFDx/INfcxmcCwDGGrkLANmQuVA+TVvIkQkTJgz7uVAoDDs3+J86f/78GRER379/j40bN8aRI0d++V0zZswYkfkH5wKAsUbuAkA2ZC6UT9MWfmPLly+Pq1evRktLS4wf7+kMAKNJ7gJANmQu2NMWfmt//fVXfP78ObZu3RoPHz6Mt2/fxq1bt6KjoyMGBgaqXR4AjClyFwCyIXNB0xZ+azNnzox79+7FwMBArFu3LhYvXhylUikaGhpi3DhPbwAYSXIXALIhcyGikFJK1S4CyM7FixejVCrFly9fqjIeAP4kchcAsiFzGWu8PQF/oK9fv0axWIx9+/aVNa5YLMbOnTtHqSoAGJvkLgBkQ+YylvikLfxhvn37Fn19fRER0dDQEI2Njf/z2Ddv3kRERE1NTcyePXtU6gOAsUTuAkA2ZC5jjaYtAAAAAECO2B4BAAAAACBHNG0BAAAAAHJE0xYAAAAAIEc0bQEAAAAAckTTFgAAAAAgRzRtAQAAAAByRNMWAAAAACBHNG0BAAAAAHJE0xYAAAAAIEf+BuvIGvqA8HekAAAAAElFTkSuQmCC", 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", 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" ] @@ -311,12 +349,12 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -334,8 +372,8 @@ "st_surf_n = solution2C[\"Negative particle surface tangential stress [Pa]\"].entries / E_n\n", "st_surf_p = solution2C[\"Positive particle surface tangential stress [Pa]\"].entries / E_p\n", "\n", - "data_st_n_2C = pd.read_csv(path + \"stn_2C.txt\", delimiter=\",\", header=3)\n", - "data_st_p_2C = pd.read_csv(path + \"stp_2C.txt\", delimiter=\",\", header=3)\n", + "data_st_n_2C = pd.read_csv(data_loader.get_data(\"stn_2C.txt\"), delimiter=\",\", header=3)\n", + "data_st_p_2C = pd.read_csv(data_loader.get_data(\"stp_2C.txt\"), delimiter=\",\", header=3)\n", "\n", "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 3.5))\n", "\n", @@ -402,7 +440,7 @@ ], "metadata": { "kernelspec": { - "display_name": "pybamm", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -416,7 +454,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.12.3" }, "toc": { "base_numbering": 1, diff --git a/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb b/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb index 2faac3bb1d..6909f0fca0 100644 --- a/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb +++ b/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb @@ -30,13 +30,28 @@ "start_time": "2023-09-16T18:29:52.652390Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" + ] + } + ], "source": [ "%pip install \"pybamm[plot,cite]\" -q # install PyBaMM if it is not installed\n", "import pybamm\n", "import numpy as np\n", "import os\n", - "import pickle\n", + "import json\n", "import matplotlib.pyplot as plt\n", "\n", "os.chdir(pybamm.__path__[0] + \"/..\")" @@ -84,6 +99,7 @@ "# load model and geometry\n", "model = pybamm.lithium_ion.DFN()\n", "geometry = model.default_geometry\n", + "data_loader = pybamm.DataLoader()\n", "\n", "# load parameters and process model and geometry\n", "param = model.default_parameter_values\n", @@ -125,19 +141,11 @@ } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/z4/5lmf5d5d23sc2gkhs__zfnfc0000gn/T/ipykernel_2839/4153409347.py:5: MatplotlibDeprecationWarning: Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.\n", - " discharge_curve = plt.subplot(211)\n" - ] - }, { "data": { - "image/png": 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YsRkZGVx00UXMmDGD22+/ndzcXL773e+Snp5OX1/fccNWALfbfdxg9UTrhRBCCCGEEEIIIYQQE8tItg49r5qQJpNJIpHICR9ftGgR69evJy8vj3g8zhNPPIHX6z1h31chhBBCCCGEEEIIIYQ4HRO2wvX222/n0ksvJZlM0t/fD8B7773HV7/6Verr6/n3f/93Dh48SEVFBX/4wx/YvHkzxcXFVFdXc/3111NTU4PP56Onp4d77713jM9GCCGEEEIIIYQQQghxPpiwgWt7ezs333wzbW1tg+s0TePWW2/lpptuor6+no6ODiwWCwDhcJj//u//JpFI8Ne//hVN08jPz+dnP/sZV1555VidhhBCCCGEEEIIIYQQ4jwyoSfNGguNjY0UFBTg9/ulh6sQQgghhBBCCCGEGHGJRIJYLDbWh3FeMBgMGI1GdDrdcR/v7e3F4/HQ0NBAfn7+iDznhK1wFUIIIYQQQgghhBDifNPX10djYyNSIzly7HY7OTk5mM3mc/J8ErgKIYQQQgghhBBCCDEOJBIJGhsbsdvtZGRknLAqUwyPpmlEo1E6OjqoqamhoqICvV4/6s8rgasQQgghhBBCCCGEEONALBZD0zQyMjKw2WxjfTjnBZvNhslkoq6ujmg0itVqHfXnHP1IVwghhBBCCCGEEEIIMWxS2TqyzkVV61HPd06fTQghhBBCCCGEEEIIIc5jErgKIYQQQgghhBBCCCHECJHAVQghhBBCCCGEEEIIIUaIBK5CCCGEEEIIIYQQQoiz8qtf/Yri4mKsVisLFizg7bffPun49957j09+8pMUFxej0+m47777hvU8mqbx61//mgULFuB0OvF6vcydO5f77ruPYDA4Amdy9iRwFUIIIYQQQgghhBBCnLFHH32Ub37zm/zgBz/g3XffZebMmVx55ZW0t7efcJtgMEhpaSk/+clPyM7OHvZzfeELX+Bf/uVfuPbaa3nttdfYvn073//+93n66ad56aWXRuJ0zppxrA9ACCGEEEIIIYQQQghxLE2DsSratNtBpxve2HvvvZdbbrmFL37xiwDcf//9PPfcczz44IN897vfPe428+bNY968eQAnHPNBf/3rX3nkkUd46qmnuPbaawfXFxcX87GPfYze3t7hHfAok8BVCCGEEEIIIYQQQohxKBgEp3NsnruvDxyOU4+LRqNs3bqV22+/fXCdXq9n+fLlvPXWWyN6TI888giVlZVHha0DdDodHo9nRJ/vTElLASGEEEIIIYQQQgghxBnp7OwkkUiQlZV11PqsrCxaW1tH9LkOHjxIZWXliO5zNEiFqxBCCCGEEEIIIYQQ45DdripNx+q5xxtN08b6EIZFAlchhBBCCCGEEEIIIcYhnW54t/WPpfT0dAwGA21tbUetb2trO63JsIZj0qRJ7Nu3b0T3ORqkpYAQQgghhBBCCCGEEOKMmM1m5syZw7p16wbXJZNJ1q1bx6JFi0b0ua6//noOHDjA008/fcxjmqbh9/tH9PnOlASuQgghhBBCCCGEEEKIM/bNb36T3/zmNzz88MPs3buXW2+9lf7+fr74xS8OjrnxxhuPmlgrGo2yfft2tm/fTjQapampie3bt1NdXX3C5/nMZz7DZz/7WT73uc/xn//5n2zZsoW6ujqeffZZli9fzmuvvTaq5zlc0lLgDB08CHPmjPVRCCGEEEIIIYQQQggxtj772c/S0dHBHXfcQWtrK7NmzeJvf/vbURNp1dfXo9cP1X42Nzcze/bswZ9/9rOf8bOf/YxLL72U119//bjPo9PpWLNmDb/+9a958MEHueuuuzAajVRUVHDjjTdy5ZVXjto5ng6dNlG6zY4TjY2NFBQUAH5mzHCzfDksXw4XXwxO51gfnRBCCCGEEEIIIYSYqMLhMDU1NZSUlGC1Wsf6cM4bJ/tz7e3txePx0NDQQH5+/og8n1S4noWdO9Vy772qibHFooLXhQth8mQoKoKKCsjMHOsjFUIIIYQQQgghhBBCnAsSuJ6hPXtU2PrKK/DSS1BfD+EwvPyyWt7PboclS6CyUgWxOh0sWgQzZ4JeuugKIYQQQgghhBBCCHHekMD1DOXlwZQp8NnPQjIJGzfCunXgcsGBA7BvH2zdCsGgWk4UxFZVqRDW6wWbDT70IVi2TFXLCiGEEEIIIYQQQgghJhYJXEeAXg+XXKKW99M0Fb4eOAAdHbB/v6qKXbcOYjEVxG7ZopYBq1ap/ZWUqCC2q0t9/fznVUVsevq5PTchhBBCCCGEEEIIIcTwSeA6inQ61UagsvLYx/r6oKFBVcLu2wdr18LevZBIqNYEhw6pBWDTJnjoIfV9aqpaDAYV8F5zjQpkS0rAKH+bQgghhBBCCCGEEEKMKYnoxojTqVoSTJmifr79dvVV06CtTYWwmzbBn/+sqmMtFqithe5utYCqmP3Nb9T3JpOqjE1LgxtvVPudNEkFsVlZ5/z0hBBCCCGEEEIIIYS4IEngOs7odJCdrZZly+C73x16LBiEgwdVCPvmm6rna3u7Cl5DITWmuRl+8pNj91leDkuXqmrb4mK17bJl4HafoxMTQgghhBBCCCGEEOICIIHrBGK3qz6uM2cevT6ZhD174Omn4fBhVQ27b5/qHdvUpKpmDx5UyweVlqpK2MpKtZ+CAvjIR2DqVFUxK4QQQgghhBBCCCGEGD4JXM8Dej1Mm6aWD2prg9deU9WxjY0qhH377aHw9fBhtfztb0PbfPvbKtytqIDcXAgE4KKL4IYbVDjr9Z6T0xJCCCGEEEIIIYQQYsKRwPU8l5UF11137PpkEmpqVAXs/v0qiP3zn1WLAk1TAe2OHWoB2LgRfvlL9X1mppq0y+2Gq6+Giy9WQWxZGZjN5+7chBBCCCGEEEIIIYQYb+Sm8QuUXq8C0ksugVtugVWrVAVsNKrC1gMH4Jln4J/+CSZPVq0HcnLUtu3t0NKigtr/+i/4+Mehqkr1hTWb1X6/8Q1YvRpefRXee08FvEIIIYQQQgghhBDi/PSrX/2K4uJirFYrCxYs4O233z7p+IceegidTnfUYrVaT/k80WiUn/70p8ycORO73U56ejpLlizh97//PbFYbKRO56xIhas4hsmk2glUVKgK1vcLBFTQ+vvfw+7dqoL20CEV0Pb1qWD18GG4775j9ztpkuoNW1Gh2hLYbCrwnTVL+sUKIYQQQgghhBBCTFSPPvoo3/zmN7n//vtZsGAB9913H1deeSX79+8nMzPzhNu53W72798/+LNOpzvp80SjUa688kp27NjBj3/8Y5YsWYLb7WbTpk387Gc/Y/bs2cyaNWukTuuMSeAqTovLBXPnquX9NE0FsC+9BN3dEA6rYHbfPhXIggplDxw4dp82m6qKraiA1lYoKYHPfU4Fsbm5EsYKIYQQQgghhBDiwqRpGsFYcEye226ynzIAHXDvvfdyyy238MUvfhGA+++/n+eee44HH3yQ7373uyfcTqfTkZ2dPexjuu+++3jjjTfYsmULs2fPHlxfWlrKpz/9aaLR6LD3NZombOC6evVqVq9eTW1tLQBTp07ljjvuYMWKFSfc5r777mP16tXU19eTnp7Opz71Ke6+++5hlSuLk9PpYPp0tXxQXx/8/e+q+vXgQaiuhpdfVkFsIgGhkAprd+9W4996C9asUd/bbJCaCrEYzJmj2heUl6twNiMD5K9OCCGEEEIIIYQQ56tgLIjzbueYPHff7X04zI5TjotGo2zdupXbb799cJ1er2f58uW89dZbJ3+Ovj6KiopIJpNcdNFF/Od//idTp0494fhHHnmE5cuXHxW2DjCZTJhMplMe77kwYQPX/Px8fvKTn1BRUYGmaTz88MNce+21bNu27bh/MWvWrOG73/0uDz74IIsXL+bAgQPcfPPN6HQ67r333tN+/mcPPMv1c68fiVM57zmdcOWV6vsP5uGxGNTVqSB25074y1+grQ0cDjWpVyikJvYCeOEFtbyfyQQf/jBMmaKCWLtdTeq1eLGqxhVCCCGEEEIIIYQQo6ezs5NEIkFWVtZR67Oysti3b98Jt6usrOTBBx9kxowZ+P1+fvazn7F48WLee+898vPzj7vNwYMHWbZs2Uge/qiYsIHrNddcc9TPd911F6tXr2bTpk3HDVzffPNNlixZwvXXq5C0uLiYz33uc2zevPmMnv+GJ27gxYYX+cnyn5DjyjmjfQgVmJaXq2XFCvjOd4YeGwhjX3wRNm5U6/x+VSFbUwPxuBrz3HNqeT+DASor1WRfxcVqfFUV3Hijal9gs52zUxRCCCGEEEIIIYQ4I3aTnb7b+8bsuUfTokWLWLRo0eDPixcvZsqUKTzwwAP8+Mc/Pu42mqaN6jGNlAkbuL5fIpHgscceo7+//6i/qPdbvHgxf/rTn3j77beZP38+hw8f5vnnn+cLX/jCSfcdiUSIRCKDPwcCgcHv/7DzD6zdt5Z/u/jf+MbCb2AxWkbmhARwdBj71a8e/VgsBtu2wZYtqlVBdbWqkn3zTfD5VKuCPXvUMuC552DVKvV9Xh6YzaoVwsUXwxVXqCC2pES1KpC+sUIIIYQQQgghhBhrOp1uWLf1j6X09HQMBgNtbW1HrW9razut/qwmk4nZs2dTXV19wjGTJk06adXseDGhA9ddu3axaNEiwuEwTqeTJ598kqqqquOOvf766+ns7GTp0qVomkY8HucrX/kK3/ve9076HHfffTf/8R//ccz6dTeu43t//x6bmzZz+7rb+dH6H3HLRbfw/2b/P6ZnTUevk8RuNJlMMH++Wj4oHofaWjh8WC1btsArr0Bvrwpq+/qG2hSAGvPww0fvw25XbRBKS9ViMkFRESxaJK0KhBBCCCGEEEIIIQaYzWbmzJnDunXr+PjHPw5AMplk3bp1fO1rXxv2fhKJBLt27eKqq6464Zjrr7+e733ve2zbtu2YPq6xWIxoNIrDMfYBtU6bKLW4xxGNRqmvr8fv9/P444/z29/+lvXr1x83dH399de57rrruPPOO1mwYAHV1dV8/etf55ZbbuH73//+CZ/jgxWuTU1NVFVV4ff7cbqcPLLzEVb+30piydjgmDRbGpcUXUKuK5ebZ93MnJw5w57VTYwuTYOuLjVh19NPw7vvqn6xA+uamtSYE9HpoLBQhbBZWWr8lCmqVUFxMeTkSHWsEEIIIYQQQgghzkw4HKampoaSkpIJNcn7o48+yk033cQDDzzA/Pnzue+++/jrX//Kvn37Bnu73njjjeTl5XH33XcD8KMf/YiFCxdSXl6Oz+dj1apVPPXUU2zduvWEBZWRSIQrrriC3bt38+Mf/5ilS5ficrnYsmUL99xzD7/73e+YNWvWMdud7M+1t7cXj8dDQ0PDCXvHnq4JHbh+0PLlyykrK+OBBx445rGLL76YhQsXsmrgnnLgT3/6E//4j/9IX18f+mGmZI2NjRQUFOD3+3G73QDsatvFl5/9Mnqdnu2t2+mP9R+1jdviZlb2LGZnzybbmc3SgqUsyF+AyTA+Zk4TQ3p7Vb/YgwdVH9hDh1QF7Guvwfu6SZyQ2QwWiwpdly6FJUtUm4LMTFUle9FFKuAVQgghhBBCCCGE+KCJGrgC/M///A+rVq2itbWVWbNm8ctf/pIFCxYMPr5s2TKKi4t56KGHAPjGN77B2rVraW1tJSUlhTlz5nDnnXceU7n6QZFIhJ///OesWbOGgwcPYrfbmTJlCrfccgs33HADRuOxN/RL4HoWLr/8cgoLCwf/4t5vzpw5LF++nHvuuWdw3Z///GdWrlxJIBDAYDAM6zmOF7i+XywR453md1j9zmoe3/s40USUpJY8ZpxRb2RK+hSmZk6l0F1Iqi2V5aXLmZE1Q4LYcSqZhNZWNQHXoUOwaRO8/jqEQurxhgbVO/ZkdDo1mVdJiaqI7egAjweuvVa1K0hLU2OEEEIIIYQQQghx4ZnIget4dq4D1wnbw/X2229nxYoVFBYWEggEWLNmDa+//jovvvgicGyZ8jXXXMO9997L7NmzB1sKfP/73+eaa64Zdtg6HCaDicUFi1lcsJg/8kd8IR/1vfVsa9nGu63v8putvyEUDxFPxtnVvotd7buGNl4HJr2JyvRKqjKq0KFjWuY0PlrxUaZlTpMgdozp9ZCbq5YlS1QbgfeLx6GxUfWL3b5d9YFta1MB7Z49qm2BpsG+fWp5v9/9Tn11ONSkXT09KpC9/nrVwqCwUFXcVlaC13sOTlYIIYQQQgghhBBCnJEJW+G6cuVK1q1bR0tLCx6PhxkzZvCd73yHK664Aji2TDkej3PXXXfxxz/+kaamJjIyMrjmmmu466678J5GgnWqCtfh6A524wv72Nu5l/c63uPJvU+ytWUrGhrxZPy42xj1RspSysh0ZGLUG1lSsIQVFSuYnD6ZVFvqGR2HOLeiUaiuhpYWFcLW1sJTT6mfTSYVzg5HdrYKXgsLVUDb06NaFVxzzdA6IYQQQgghhBBCTDxS4To6pKXAODcSgeuJxBIxmgJN7OnYw9bmrdy/9X66gl0Y9cZj+sK+X7o9HR06cl25fKrqU8zMmsnk9MmUpJRg1E/YIuYLTjgM9fWwdSts2KD6yep0al19vQpohyM1VbU/sFrhiitg5kwVxHq94HbDjBlgs43mmQghhBBCCCGEEOJMSOA6OiRwHedGM3A9kaSWpLG3kf2d+3lk1yNsrN+IzWTDF/bR2Nt4wu0MOgNmg5mqjCquKL2CyvRKJqdPpiyljAxHxjk5djFyEgk1mVdHh2pdUF+vgtmtW9XjoRD4/afej16v2hUUFEB+vqq2TUmBT35ShbH5+aqX7HF6TAshhBBCCCGEEGIUSeA6OiRwHefGInA9mb5oH1uatvDg9gep8dWQ58pjX+c+DnQdIBQPnXA7g87AovxFTMmYQmVaJWn2NKakT+GinIukV+wE5verStgnn1R9YgsKoKlJhbPvvQc+3/D2YzCoKtmsLFi27OhwNj9ftS8oK1PjhBBCCCGEEEIIMTIkcB0dEriOc+MtcD2RpJbk1ZpXeeXwKxj1RnpCPezv2s/Otp10BDtOuJ1ep6c0pZRJaZMo8ZYQT8aZlzuPK8quIN+dj16nP4dnIUZaNKoC2ba2oSrZtWvVzxkZ0NysesoO57eC0agmELNaobsbqqrg4x9XgWxenuotO3myqqaVYFYIIYQQQgghhDg1CVxHhwSu49xECVxPJKkl2de5j3eb30Wv17Ovcx/7u/bzt+q/0RvpPem2VqOV8tRyLAYLXquXD5V+iIsLL2ZS2iQy7BnodLpzdBZiNMVicPgwvPsudHWpkHYgnH31VejrU+0Nksnh7S8lRVXI5uaqILe7G2bNgo9+VK3LzVVj7PZRPS0hhBBCCCGEEGLck8B1dEjgOs5N9MD1ZPoifbT1t9HQ28CBrgO8WvMqLx56kXgyTiQeIZaMnXBbp9mJQWeg0FPIJ6Z8gklpk5iUNonylHK8Nu+5OwlxTsTjQ1Wyb78NW7aoEDYeV+uamoY/ydcAnQ6mTVMVsQPhbCgEc+bApZeqdenpqgetEEIIIYQQQghxPpLAdXRI4DrODQaun/kM7j//+YJJf+LJOHW+Og52H+RXb/+KvZ17yXZm0xRoos5Xh8aJ/xkZdAamZkxlTu4cJqVNosBdQKYjk8UFi3GYHefwLMS5pGnQ2QmtrapVQXMzbNwI27eDyaQeH2hhkEgMb58mkxprNqsQtqgIsrNVFW5//1A4m5UFNtuonp4QQgghhBBCCDHiJHAdHRK4jnODgSvgvvpq+MMf1P3QF7BwPMwbtW/w1P6n8Ef82Iw2DnYf5EDXAVr7Wk+6bb47n/LUcspSyogn45SmlLK8ZDnTsqbhtpxfFcTi+OJxqK6GXbtUSDoQzr7+Ouzdq3rEhsPQ3n56+/V4VAhrtcKHPqQqZwfC2XBYhbNLl6qqWekxK4QQQgghhBBiPJDAdXRI4DrODQauZjPuaBRKSuCJJ2D27LE+tHFpV9suNtRtwGl2Uuuv5WD3QTbUbaDOX3fKbTPsGWQ5s0gmk8zImsHVk66mLLWM8tRy0mxp0jP2AhOLqXYFmzergDYjQ1XPtraqdYcPq4m8+vogEhn+fvV6yMxUk3xZrfCRj6jK2cxMFc5GIqrn7Lx56jnN5lE7RSGEEEIIIYQQF7iJGri+8cYbrFq1iq1bt9LS0sKTTz7Jxz/+8VNuF41Gue+++3jkkUc4ePAgdrudyspKvvSlL/H5z38ek8k0Isd3rgNX44js5UL08stw881QUwPz58N3vgN33jnWRzXuTM+azvSs6cesj8aj1PfW0xnspLq7moNdB/nNu7+hO9SNy+KiM9hJR7CDjmAHAHs69/CX9/4yuL1BZ8BhcnBJ0SVMz5pOeWo5Oc4cKtIqKE0pRa+7MFo9XEhMJvX5RknJycdpGvj90NAAb72lgtjs7KFw9p13oK5OVbUGAmryr9YjhdiRCDz66Mn37/VCMKjC2Usugfz8oXA2GoWZM1U4m5mpit8vkK4jQgghhBBCCCEuYP39/cycOZP/9//+H5/4xCeGtU00GuXKK69kx44d/PjHP2bJkiW43W42bdrEz372M2bPns2sWbNG98BHiVS4nqajJs1KJOCaa+Dvf1cPLlsG//RPat0IJfAXqt5IL4e6D/HUvqd4peYVzHozGhqHeg7R2Nt40m0NOgOT0ycPtiroi/YxNXMqHy77MOWp5Rj18jmDUOJx1We2vl71l62thZwc6OhQLQy2bFHBrcmkKmeH22t2gMGgAl2TCRYuhLw8FcT296tQePZsuOwytS4jQ23jdKoJxIQQQgghhBBCXHiOqcTUNFX1Mxbs9jO6QNXpdMOqcP3pT3/K7bffzpYtW5j9gTvHY7EY0WgUh2Nk5v6RlgLj3FGBq9ut7kNeuhT27BkalJMD116rql6Li8fsWM9XveFe1u5by7aWbRR6Cjncc5jqnmo2N27GH/GfdFuj3kixtxin2QkazMubxxWlV1CSUkJpSikp1hRpVSCOK5kEn0+1NXj1VRXElpWpYLa9faia1mZTla49PWf+XFOmqKrc9HQV9HZ2wvTpcPnlal16OnR3Q2mp+hUjPWiFEEIIIYQQ4vxwTDDY368qc8ZCXx+cQeA53MB15syZZGdn8+KLL57hAQ6fBK7j3DGBK6hPG95+G556Ch588OjZfS6/HG66Ca68Uk2dLkZVW18b9f56ukPdVHdXs7N9J4/veZy+aB86dEQSJ2/uqdfpcZgcXFZ8GRVpFZR4S0ixpVDiLWFW9ixsJts5OhMx0UWjQy0MamvVf/+BcHbTJjh4UIWz8Ti0tamJvM6ExTIUwsbjKuidNEm1OxhYf/iwqq5dskR9tdtH9FSFEEIIIYQQQoyQCylwtdvt3HLLLfziF784wwMcPunhOhHpdLBggVr+4z/gpz9VX+NxVQr36qtqXFmZuq/4S1+Ciy+WsrRRkOXMIsupgu0ruRKAB65+AICklqSpt4lDPYd4fM/jbG7cjNviJpwIc7jnMK19rSS1JIFogP878H/H3X+uK5cSbwnF3mI6g51MTp/MtZXXUpFWQa4rV3rHikFmMxQWqmU4gkFVNVtdrVoQdHZCV5f6LGfvXhWSmkxqXXs7tLSo7SIRaGpSy4DmZnj99RM/l82metCGQiqAnT0bUlPVsnOn6j171VXqsdRUNRmZy6Wqbo3yqiGEEEIIIYQQ547droLPsXruUXQ+14BKhetpOm6F6/EEg/Daa+o+4xdegHffPfrxlBTVimDuXFi8WC1SdjamOoOdPLP/GQ52HyTHmUONr4bDPYdZX7ceX9h3yu3NBjMeiwe9Ts9FORextHApxd5i8t35uM1upmdNx6CXkF2MDE1Tk351dw+Fs++8A7t3qzDVZlPrWltVL9poVE3gFYud+XMaDKrf7EA4e/Cgeq6PfERNHpaaqkJcvx+qqtR8gqmp4HZLX1ohhBBCCCGEGI6TVWJOFNJSQALX0zbswPWD6urg85+HrVtV6Zv/A71G9XqYMwcWLVJfZ85UiYVMvjUuhGIhOoOdtPS1UNNTwzvN7/DE3icIxoI4zU7q/fXEk/FT7qc0pZRibzFFniIi8QgWo4UPlXyIJYVLyHfny4ReYlRp2lBP2J07VThrNqtwtrtbVc6++KIaU1ysetYOBLrJ5Jk/78DkYXq96kWbnQ1er/pcqrlZtUC49FK1zutVLRYyMtTYzEz5NSiEEEIIIYS4cFxIges999zD9773PZk0S5xF4DpA09RU51u3wptvwn//N9TUHH+sTqcShzlzYNYsFcLOmgUez9mcghgF8WScxt5Gntj7BFubt5JqS6Uv2kedv459nfto7Ws95T4MOgNZziz8YT8Zjgy+MOMLFHmKKPIWodfpKXQXUpxSLKGsOOc0TQWvHR2qgnUgnH3pJRXGTpqkPkPq7lbzBzY0qNYDsZgaf7bsdhUMB4MqgJ05cyicfe89VWX74Q+r9g1erzrecFh1cZk0Sbq3CCGEEEIIISaOiRq49vX1UV1dDcDs2bO59957ueyyy0hNTaXwBL32IpEIV1xxBbt37+bHP/4xS5cuxeVysWXLFu655x5+97vfMWvWrBE5Pglcx7mzDlw/SNNU08bubjh0SM2m8/e/H9uC4P0yMlQ7glmzVCBbVaWSBWmuOG4FY0H2duwlGAtS66ulzl/HU/ueotZXi81ooz3YTjQRPeV+vBYvlemVFHoKSbWlcrDrIJPTJ/PF2V+k0FNIhj0Dndy7LcaRcPjoINbjgd5eVT27fbsKTB0Otd7nU8uePaoF9khxuVQQ29mpfp4zR/WndbtVIFxbC6WlcNll6jjcbtWKweOBadPUhGdWq7RFEEIIIYQQQoy+iRq4vv7661x22WXHrL/pppt46KGHTrhdJBLh5z//OWvWrOHgwYPY7XamTJnCLbfcwg033IBxhLIuCVzHuREPXI8nFlOz5GzfrpKC7dthxw7429/UDDnHo9PB1KkwZYpa8vNVqlBVpZICMa4ltSStfa3satvFupp1tPW3YTPaqPPXUeur5UDXAZLaqe/pthgsmAwmklqSRfmLWJS/iEJPISm2FAKRAHNz5zI9a/o5OCMhzl4ioSpnfT44cEAFsTrdUDjb0wPPPacC3PJyNXmnzweNjWq7kXx1M5mGJhobCGLdbvX9wYOqinbpUtWKwe1WLRS6uqCoaOjGBLdbjZPgVgghhBBCCHEiEzVwHe8kcB3nzkngeiKtrbBhg7pir69XQewbb5y4JQGopoklJSqELStTM9hceqmalvxcH784Y5qm0drXSq2/lpZAC/X+era1buPN+jcJxUODga3Gqf87e61eCtwFFHoK6Qh2YNQZuXrS1SwpXEKBu4AsRxZWkxW9Tn8OzkyI0ROLDQW2PT1q8rD2dsjJUZW3fr8KcXfvVn1s09JUeOv3q4B3oMp2JF8l9Xq1v/x81R7B5VLBcm2tqqRdtkytc7nUzQ96PSxYoIJbl0u1VtA0yM1VVbsS3gohhBBCCHF+kcB1dEjgOs6NaeB6PNGoSgYOHVJX4nv3qgThkUdUonAyubmqAtZuV+Vhl10GM2aoJEAvYdtEE01EaeptYlPjJna27cRkMNHR30FDbwPvdbxHvb9+WFWyA1xmF5cUXUK+O588Vx494R7S7GlcVnwZUzOm4ra4pX2BOO8lk2oSMb9ffc61d68KYlNShsLZ559XbRMqK1XI29urAtSGhqH+sSPRy/aDjEZwOlUQGwio4LayUvWydbnUsR8+rILcyy4bCnI7O8FiUWNzc4fWS1caIYQQQgghxp4ErqNDAtdxbtwFrieSTEJdnbraP3RIpQRPPQWvvaauqk8WxlqtUFCg7qGdPh0+8hE180xFBaSnS0nVBBaIBGjobaDeX0+Dv4H/2/9/1Phq8Fg8tAfbafA3EEmcoG3FBzhMDtLt6fRF+yhwF7CiYgV5rjzy3HnYjDYmpU2i0FOIQS8zFgkRjapQtLoamptVUPr+de++q37lFhSodYGAmlfR74fsbBXyBgKqWvdEnWVGwsAxDISw7e3q+aZPV5/LuVzq87jaWlWhu3Tp0NhEQrVOyM5WN1DI53ZCCCGEEEKcPglcR4cEruPchAlcT0TT1NVzOAz79qkZa+68E9ra1CwydXWqROtETCZ1H+vSpaotwaRJQ2Gs03nOTkOMDk3TaOtrY3vrdhp6GwBo7G2kKdDE8wefpzvUjdVoxR/xD2t/Bp2BXFcuee48XGYXPeEeKtMq+WjFR8lz55HvzifLkYXD7BjN0xLivBKLqf6wAyFsIABvvaWC3MJC9ZlYIDA0D6PJpCYFGxi7e7fqd+twqMrbk/3KPxsOh7qBordXfY43a9ZQONvWpl6Gpk5VlbYul6q6bW9X80LOmDE01uVS+5HP+oQQQgghxIVAAtfRIYHrODfhA9dTicdV6PqXv8Azz6irZJNJtS2orz/5ti4XXHTRUAibmqqaD1ZUqHYH4rwRjAVp6m1ic+NmXq19lXgyjtfqHQxntzRvOa32BTp0FHuLKU8tJ8eVg8VgwRf2UZVRxfLS5eS6cslx5mAz2UbxrIS4MEUiKgA9eFC1T0hPHwpnX3tNtUYoL1eBaCCgXgrefltVsBYWDo1tbR3qezsarFb1suJyqZ67zc0qiF2yZCiY7elRQe7kyeplyOsdaq+Qk6Oqb23ya0QIIYQQQoxjEriODglcx7nzPnA9GZ8P7r9fTdY1c6a6Oj9wQN0Le7IGhXq9mvGlvFxd/Xo8sGgRzJ+vyq7s9nN0AuJc0TSNjmAHoViI1r5WmgJNbKjbwBt1b2DQG7Cb7DQFmmjqbSIUH35zS4POgElvYkbWDCrSKsh15WI1WgnFQkzPms6i/EXkuHJwmqXaWohzTdNU2BmJqKWvD5qaVPVtJKIqWQfC2VdfVcFtSYmqhO3rU4Htjh1qX2lpalxf38hOWgYqOPZ4VAgbjarQePJktc7pVC0eUlNV39vUVLXYbOprXp7aXgghhBBCiNEigevokMB1nBsIXH0+Hx6PZ6wPZ3zo7FTlTv396qr6wAF11fzMM8O7Us7LU1e++flwzTXqqry8HMrK1BWwOG9pmsbBroPs7tiNxWChK9RFS6CFNxveZEvLFkx6Ewa9gZZAy2kFsy6zCw0Ni8HCwvyFVKZVkuPKwWa0kSTJ9MzpXJRzES6zSyb+EmIcSyahpkaFs3a7akEeCKjq1hdfVAHv/PlDQe6GDeomjfx8Nd7vV+0XmptH7picThUIp6RAY6P6eflyNTlZaupQj93Jk2HKlKHQViYlE0IIIYQQwyGB6+iQwHWcGwhcb3v3Xf5n1iwJa04mkYCWFlXheuiQKhv6wx9g2zZ1pdrTo66GT8ZohOJiWLhQBbClpWqmlosuUo3+5M//gqBpGr2RXl45/AoHug6Q5cyiK9hFS18Lmxo3sat9Fya9iWgiSn+sf9j7tRqtZDuzCUQCmA1mLim6hMq0SrKd2ZiNZjRNY2rGVGZkzZA+s0JMYMnkULWsz6deejZvVq3MMzJU2wGfTwWzzz+vKl+nT1cvUz09KvA928nK9HrVXWfePMjNVQFtOKyW2bPVjR+ZmWq93PghhBBCCHHhksB1dEjgOs4NBK48+yy3lJezetIkDBL6nRlNg+5uddX761+rJoKTJqlg9tAh9fPJuN0qhC0vV/d7pqfD4sVqZpbCQtV7VlxwApEAzYFm1u5dy6GeQ5SnltMZ7KSlr4V3m9/lUM8hdDod0UT0tPbrMDnIdmbjj/ixGCxcWnwplWmVZDmysBrVL+uqjCqmZU6TXrNCnGc0TU3+1durgteuLlXd+vzzKpCdMUOt6+5WN3w0NKgQNx5XQe7p0unUS9isWar3bFaWCo31ehXOzp2rbg7JzASDYaTPVgghhBBCjCUJXEeHBK7j3PsDVxwOrs/M5KHJkzHp9WN9aOefzZvVjC1Op2pXUF2t+sW+++6pt9XrVQkRQEEBfPSjqjq2pER9lerYC14wFqStr42WQAtP7X+Ken89ZSlldIe6ae1vZVfbLmp9teh0OuLJ05sJyG1xk+3Mxhf2YTVaWVa0jElpk8hyZmExWEhqSaZkTKEqowqHySGV8kKcxxIJVTm7fr0KYouKoL1dfab4xhuqHfpAONvWdnqVtAaDermz2+HSS9W+8/LUy5vVqoLZmTNVn1whhBBCCDExSOA6OiRwHecGAtcHq6v5x8ZG4prGtWlp/KWqCquUmZwbwaC6ak0mh6phH3xQfU1Ph46Ok0/iBeoKtaBAXYmWlqqGfw4HLFigesjK/ZziCE3T6Iv20davwtkn9z1Jg7+BslQVzrb1t7GzbScN/gYAElritPZvM9rIdGTSG+nFZDCxtHAp5SnlZDoy0ev0hOIhqtKrmJs3lwx7BhajzNgjxPlK01Sl7EAv2sLCoXD2xRdh714192QkotYlk8Pbr9erPoPs6lIvkx//uHrpKypS1bNlZepGESGEEEIIMfYmauB69913s3btWvbt24fNZmPx4sXcc889VFZWnnS73t5e7rnnHp544glqa2vxer1MmzaN2267jX/4h38YsQIlCVzHuYHA1e/3syEW45O7dxPRNBa6XLwyaxYOCV3HnqapK9HXXoNHHlHha2Ghmnnl8GEV1p5KdraqhtXrVb/Ziy9WfWOLitS+ZJpqcRyapuGP+AcrZ5/Y+wQNvQ1MSptEV7CL1v5W9rTvoaH3zMJZUNWzsUQMk97EvLx5lKaUDoaz/rCfKRlTWFKwhExHJmn2NIx6malHiPNRPK5e1p5/XvWYLS6Gpia1bNigqmr1+uFXzLpcUFWlXuIKClTLhMrKoXDW45EbQ4QQQgghzoWJGrh+5CMf4brrrmPevHnE43G+973vsXv3bvbs2YPjBLdc+Xw+li5dit/v584772TevHkYjUbWr1/PPffcw5YtW/B6vSNyfBK4jnPvD1zdbjfX7NzJs93dAJTbbNxbVsbVaWlyi/B4tn07bNqkgtTubhXC/v3vqoWBXq+uYk/FbFZtCS65RIWwxcWqbKiqSv0sFbLiFDRNoz/WT0d/B219bTx94Gma/E2Up5XjC/to729ne+t2DnUfwqg3Ek6ET7u1gQ4dBr0BvU7P9MzpFHoKSbenk9SSdAY7mZQ2iWXFy0i3p5NmS8NpdpJhz0AvLVKEOC8M9J5talLVsU88oSpmy8pUBW1dnWppkBjGZz8ul7oRJJFQnz9edtlQCwOLRfWbNZtH/ZSEEEIIIc57HwwGNU0jONxbm0aYXa8/43yro6ODzMxM1q9fzyWXXHLcMbfddht/+MMfOHDgALkDbSGP6Ovrw2q1YjSOTBGRBK7j3AcD1+5YjKt27uRQOExnLAbAlSkp3FdezmRpmjaxaJpqVxCNqhD28GH4n/9RX0tLVauCujo15lQyM9WVaGamuudz2jQVzhYXq/Uu16ifjji/aJqGL+yjra+Nlw+/TJ2/jiJPET3hHtr723m35V32dOzBbDCjodEV7ELjzH69p9vSyXZlk25PR4eOjv4OSlJK+FDJh0i3p5NuTycYC5LrymVy+mTcFrd8yCTEBBWNwnvvqcm//H71MrdzJ/ztb+rlzmSCzs7h7au0VFXIFhVBOAxGIyxbpm4SKSiQzyKFEEIIIYbjg8FgfyKBc8OGMTmWvosvPuM7uaurq6moqGDXrl1MmzbtmMeTySTp6el8+tOf5oEHHjjbQz0lCVyHafXq1axevZra2loApk6dyh133MGKFSuOO37ZsmWsX7/+mPVXXXUVzz333LCf94OB64BAPM5ddXX8vLGRqKahB76Sm8tdJSV4TabTOjcxjmkabNsGzzyjWhVkZEBtrbpCff754ZUJgSoHstthzhyYPl1dnebkqFKhqVNVwz0hzkIimaAz2MnG+o0c7jlMnjsPf9hPZ7CTLc1b2N62HZvRhsPsoDPYSWewk2BsGB8mHIfVaCXdno7FYMEX9pHnyuOSoktU5aw9jeZAM16rl8UFiyn2FpNmS8NmkoaRQkwUwaBqW/C3v6l5K+12NZdlfb2qkG1pGd5+UlPVS2dKCnz600PhrNGoAtkZM9T3QgghhBAXsvMhcE0mk3zsYx/D5/OxcePG445pb28nKyuLe++9l2984xtne6inJIHrMD3zzDMYDAYqKirQNI2HH36YVatWsW3bNqZOnXrM+O7ubqLR6ODPXV1dzJw5k9/+9rfcfPPNw37eEwWuA3b29TF/61YiR/5YM0wm/r+CAm7JyZHg9XwXjaqrT5dLXX3W1qoQ9m9/U7OROBwqmD3SguKk3O6hRnqhkJrV5NJLYfJktT4/X/rIihHX1tfGoZ5DmPQmfGHfYDj7dtPbWE1W0mxpg+Hs7vbdZ9SDdoBJbyKhJfBYPMzImkGqLZVUWyqHew5jMVi4rOQyylLKSLWlYjVaMRlMlKaUkmJNkWpaIcaZWEy1LOjtVS9zDQ3w9NOqx6zdripkA4FT78doVO3TCwvV55lNTVBeDjfcoILZ/HyYQG3MhBBCCCHOyPnQUuDWW2/lhRdeYOPGjScMMNva2sjOzpbAdSJITU1l1apVrFy58pRj77vvPu644w5aWlpO2Lz3eE4VuGqaxgvd3fyothZ/IsG+I7ef2/V6bsjK4tsFBZTLPXUXtt5e+M1v1D2c06apcLauTvWVHc6EXgNMJhXEzpungtmBybymTFHBbHY2yCRuYhQlkgmCsSBdoS46g51sb9nO201vo9frSbenD4azr9a8SjAWxGVx4Qv7TrsX7ftZDJbBcLYr1IVep2dh3kJKU0pJtaWi0+noDnVTmlLKwvyFg2MdJocEtUKMEU1T7Qp274Z169TLnsejPqOsq1M3joTDw9vXwGeN06apvrG5uapitqdHtVG/9FLVzUde/oQQQggxUU3USbMGfO1rX+Ppp5/mjTfeoKSk5ITjkskkaWlpfOYzn5GWAuNVIpHgscce46abbmLbtm1UVVWdcpvp06ezaNEifv3rX590XCQSIfK+KX6bmpqoqqo6YeD6frFkkj+2tXFfYyO7+vsB0AFXp6Xxjfx8lnm9EgCIowUC6qqxv1+Fr3V18MtfqllOqqrUVWp9vap6PRWjcehKNJlUIeyll6pwdiCgTU2VaafFOaVpGoFogJqeGna37yacCOM0OekOddMd6ub/9v8fXaEuSlNKCcaCdIe6aQo00RvpPavn1aFDp9NR6Ckk351Pqi0VNGjobaDAU8AVpVeQYk0hxZZCV7ALr9VLVWYVea48rMaJ9yZHiIkmHFYve83N6mXunXdUOBsOq5B1uC3UQc1/mZ2tXvr0epg/X030lZurOvaEQuolcdYsaWEghBBCiPFnogaumqbxT//0Tzz55JO8/vrrVFRUnHKbW2+9lT/+8Y8yadZ4s2vXLhYtWkQ4HMbpdLJmzRquuuqqU2739ttvs2DBAjZv3sz8+fNPOvaHP/wh//Ef/3HM+uEErgN8sRizt26lLhw+agqbYquV6zIyuDknh0qpehXDpWnw9tvw8svqStLjUVeptbXw+OOqj6zBMLx+sjqdmsirrEyFsA6HuvqcNUst+fkSyopxIZFM0B3qHgxhu0PdPHfwOZoDzZSnlhOOh+kOdbOnYw97OvZgMpgwG8x0BbuIJWNn9dwWgwWH2UF/tB+P1cPc3Ll4rV5SrCnU++sx6A0szl9MWWoZKdYUbCYbRr2RIk8RafY09Dr9CP0pCHHh0jTo6oK33lKVsna7+oyyqQl27VJVssmk6u4z3DvudDpIS1MVsYGA2m7hQpg5U61zOtX6SZPUOq9XKmeFEEIIMfomauB62223sWbNGp5++mkqKysH13s8Hmy248/h0d3dzZIlS+jr6+Ouu+5i7ty5mEwmNmzYwN13380777yDd4TmuJHA9TREo1Hq6+vx+/08/vjj/Pa3v2X9+vWnrHD98pe/zFtvvcXOnTtP+RxnU+H6fpqmsScYxKjT8YvGRv7Q2kr/+64I5jid3JCVxXWZmeRIb05xNnp71ZVoW5sKYp98El57TV0pOp1DlbMdHcPbn9WqQliXCxYvVtNQ5+erq9T8fBXWZmdLmZAYlzRNIxgLsrt9N7X+Wlxm12Bou6t9F+80vYNJbyLblU1PqIeecA97OvYQiavf+xpn9xKp1+nxWDx4rV76on0ktSQzsmYM9qM16A00+FWV7aVFl5JiS8Fr9WLUG8lx5uAwD7/ljRBCfdbY3q4qZZ9+Gg4cUC9R/f1q3f79qrfsmbZB83ohHlfbT5kCFRXqRhKTCaqr1cviRz+qPqtMTVXj0tNVgKuXz16EEEIIMQwTNXA90R3cv//97086d5Lf7+cnP/kJTzzxBHV1daSkpDB9+nS++tWvcu21147YneESuJ6F5cuXU1ZWdtLeD/39/eTm5vKjH/2Ir3/966f9HKfq4TpcffE4k99+m6ZoFAMwUIuoA6bY7VyXmcnH09OZ5pC+g2IUJBKwY4daioqgsVEFsc8/Dzt3qkm7YrHhh7ID92/m56tyoKwsuOyyoXA2L08tUsktJpCkliQQCdAT7uFwz2G2tWwjloiR6cykJ9SDL+zj+YPP0xnqpMRbQiwZoyfUQ0tfC76wb0SOwWwwk25Px2v1YjaYaeptIs2exodKPoTX6sVr9dLY24hep2de7jwq0irwWr24zC5cFhd2k/yfE+J4EglVMdverpaXXoJDh1TbgXBYrTtwQC2gQtazYbGoicDcbhXONjSo8HbZMrXO7VYvxfE4zJmjqmrdbvWZZzAIOTnqhhSpsBVCCCHOfxM1cB3vJHA9C5dffjmFhYU89NBDJxzz0EMP8ZWvfIWmpibS0tJO+zkGAtfaZ2sp+mjRWRwtJDWNTb29lFutPNbZySNtbbzVe3SfwiKLhavT0rg6LY2LvV4c8k5bnEuRiLoCfeghOHxY3WvZ1KSWDRugtVWFrcMtFbLZ1JTTeXkqiO3pUVNSX3yx+pqXp0qF5EMGMcElkgl8Yd9gCNsT7uGVw6/Q2NtIsbeYpJakJ9TDga4DbGnZglFvJN2ePhjk+iP+ETkOvU5Prit3MJxt728nGo9yUe5FVKVXkWJLwaQ3UeuvJc+Vx7LiZYNjXWYXJoNpRI5DiIkuFgOfD7q7VVuD2loVoMbjat2BA7B5sxqbkaHWDSwjyeFQN6uEQuoluqhIvZw6HOrleP9+FdRefrla53Col+xQSE00Vlam1pnN6nxSU9XLst2ugmAhhBBCjD0JXEeHBK7DdPvtt7NixQoKCwsJBAKsWbOGe+65hxdffJErrriCG2+8kby8PO6+++6jtrv44ovJy8vjL3/5yxk970Dg+pzxOZZsWIJnoWckTmfQ9w4f5ldNTRRZrRwMhQi/L8jSA4s9Hj7k9XJ5SgoL3G4scn+aGCsD004PNM1rbFRXnPffr0qD5s8fCmcPHRp+eZDJpNoTFBaqMp+cnKGSo8mTYepUFcw6naN7fkKMoUg8QlOgiUg8QjAWxB/xc7DrIJsaN5HQEhR5ivCFffgiPjbWb6Q71E26PZ1oIoov7KMv2jdix5JqSyXfnY/X6sVisHCw+yCptlSurrh6MJztCKpq+FnZs5iUNgmv1Yvb4pb+teKCl0yqm0V6etQNIIGA6uqzebMKccvKVCeg3l71OWZ7u3rJ0+nUuq4uFYyeCyaTCm1jMRXEZmaqCtuBdglmMyxZoj47tVrVy3tPj6rGnTx5qAPRrl0q1L3kEhXk2mzqvKNRdW65uWqdxaIWCXqFEEKIo0ngOjokcB2mlStXsm7dOlpaWvB4PMyYMYPvfOc7XHHFFQAsW7aM4uLio6pd9+/fz+TJk3nppZcGx52ugcD1WZ7F7XEza90sXHNcI3FKg5KaRkLTiGka63p6+O+mJl7u6TlmnFmnY6rDwUdSU7nM62WB241b+miK8aitTc1yEo+rq8iBcPaPf1RXYaWl6sqts3P4+3S5hkLXSZNg7lx1FZeTo67gpkxRE4FJMCsuQOFYmMbeRtr72zEbzSqcDft4veZ1GnobyHPnodfp8YV91Pnq2Nmuepo7zU58YR/B2DCngz8JHTo0NEx6E1UZVUdV2fZH+5mTO4eZWTPxWr3YTDYa/A3kuHJYUrBEVdhaXBLYCnFEKKT60Pr90NenXlKbmlQ1rcUy1KP2jTfU56EzZqh1/f3w7rsqyM3KUqHpwH5O5yV3tLlc6jzMZlW529c31CrebFaP7d6twtkFC9RLu9mszqulRX1GO3360NitW9X3ixeril+LRZ1zV5d6m1BRMTS2q0sFw7m56qvFIm3phRBCjC0JXEeHBK7j3EDg+saiN0i8lcCYamTWa7Nwzhi9UKcrFuOFri5ao1E8RiOv+ny82tNDe+zombf1qP6v0x0OVqSlMdflotJuxyC3Z4uJIhyGl1+GN99UV0hms7qSOngQ/vY3Fdja7epKbLhcLnV1ZTarq9CpU9XV2kDlbHq6uidTglkhBkUTURr8DdT769HpdMQSMXxhHzW+GjbUbSCWjFGaUjoY5G5r3UZPqAeH2UEoFiIUD531Meh1ekx6E/FknDx3HmUpZYPh7Hvt7+G1evnklE8OBrmBaACDzsDk9MmUppTiNDulB7oQp6BpqvJ0IJytqVHdghwO9XIbDqtgdsMGVfk6d64Kf8NhFWrW1qrPNtPT1Tq/X1XvJhJQWanWhUJqn4GAeilOJtW+xjOdTv3ZmEyq09FANW5zszr+ioqhtym9vepz5LS0odDXbFaVvgM9edPTh8bW1KifZ88eGnvggPozmzZN7WcgeG5vV71+y8qGxvb1qeNKS1N/R2az6u0rv+6EEOL8IYHr6JDAdZwbCFy7Gruo/VQtvZt6MaYYmb1xNo6qczebdDiR4Lo9e/i7389lKSm8EwhQGw4fM86h1zPL6WSS3c4lHg9zXS4m2+0YpRWBmIgSCXVVEQioIPapp9SVXXa2uj+xuVn1mh1opHc6BoLZgXs5dTqYORPmzRuqnM3OVuPkqkaIk4rEI3QEO9jfuZ+uUBcei2cwnH2j7g3q/HXkOHMwGoz4wj5aAi3s69xHUkui1+mJJCJnfQwGnQG3xU1vpBezwczigsWD4WxXsItANMDM7JnMzp492AIhnohT5C0i25mN3WSXwFaIUZJIqMnAmptVSJuaqgLGgdbx9fUqaMzMVOuCQXjlFRXgLligto9EYO9eFVZmZqoq10hEBcivv65C3WnThsa2tKjg125Xn7EOjPWPTMvsMTUQEOv16s/NZFJBbE+POs+8PBXQGo3qz+XQIfW2afZstc5kUuv8ftUeIi9PrYtGVXBss6l2EgNdnw4fVq0uJk1Sf+5Go/pz3r1bjV24cGi/TU0qJC4oUBXLA9XDDQ3qGKdMGRobDKr9eDzq7ZbROBQmS6gshLiQSOA6OiRwHecGAle/34+h0cDWOVtJhpOYMkzM3jAbe+XYzQjdHInwL9XVrO3oINdioSsWI3icyYxsR0LY6Q4H891u5rlcTLHbMUkIK84XiYSaqcRqVVdYzc2wdi1s2aLCU7NZraurU+U5p0OnU1cXOTnq/kxQVwHTp6uANjtbLVlZ6qpOCHHawvEwvrCP/Z37qfXVYjFaAPCFfdT761lft55IPEJ5ajk9YTXR2IGuA/RGetGhI6ElzvoYjHojKdYUjHojiWSC8rRyZmfPJtORSbo9nfb+dkq9pczLm0e2Mxuv1SsBrRATlKapIDIaVUtPj+qGBCqcjUZVcLltmwoPy8rUS38spt5KvPeeqnitqFBjYzF47TX1+fDMmertSCymguRdu1SYWFk5NHbbNlVhXFw8FHT6fCogNpnU+IGxoZA63guRxaL+3AeC43BY/f2kpAytr6tTb9UqK9XbPaNRtY0YaKtRWjo09p131NhFi9RbNqNRvW2srVWh84wZQ2M3blR/7pdcov6uB8YeOqTeWl500dDYt99WldALFgwdW3f30GR706cPjT1wQO23slKF5QaD+jvu7lbPU1AwNLavT4XqA88PQ/8W5OVHiPOLBK6jQwLXce79gavD6GDbxdvo39mPFtfQO/SU31tOzi05Y3rRFU0miSST2A0G9geDPNPZyb/V1KADrAYDfYljL0TNOh2T7XamOhyU22zMd7mY5nBQaLWil1dwcb7SNPUuvK5OXc00N6t3z2vXwo4dKlTV6dT65mZ1NXQ69Hr1zn4ghNW0oXB21qyhYHagsZ4Q4qxpmjYY2Lb2tbK9dTtdoS7yXHn4wj56wj1sqNvAYd9hsh3ZWIwWfGEf7f3t1Pnrzvh5jXojDpODpJakxFvC9KzpZDoySbOl0RvtpSK1grm5c8l355NmS5NwVghxxmIx9dlyPK6C2EBAhbPxuKpkHQhn9+1TwV12tgomYzH1tmfHDhXYTZumtonH1WfSbW0qNE5PV2O7u1WXJ6NRhYexmBq7fbsaW1ysAsRYTB3D9u3qbdPUqUNj6+tVeJySolpVxOMqKO3oUG+LBo5r4DjEqen1Kog3GNTPwaD6OSNjqCq4rU19SFBQMBT6hsMqIHY41NvQgbF796p2F9OmqaDZYFD7fPdd9fezbNnQ2D171L+L6dNVRy6jUe132za134svHhpbWzs0VUNBwdBb3bo6Nfb9LTiCQfXvIS1NHa+0yhAXOglcR4cEruPc+wNXt9tNtCNKpClC9T9X49+g7klKXZFK5W8rseRaxvhoh8STSRojEQqsVg4Gg2zt6+P2w4dpiESw6vWEj1MJC+A0GJhqt1NitTLT6WSuy8V0p5NMk0kuFsWFZeC+upoa9W60tVUtTz6pSlsG3qG2tqrQNnKat0QbDKq8IStLXRklk+od9cyZ6l3xwPqMjKF32EKIEaVpGoFIYLDVQXeom5cOvcSO1h1kOjKxGq2097dT46thY/1G4sk4dpMdf+T07kk2G8w4zapv9KS0SczLnUeuK5dsZzZ69MzMnkl5ajkO87lrVSSEEGNN04aC5IGvAyFxJKJaDSQSamlrU/2FHQ5VGToQPu/Yob5OmTK0v5oa1cIgNVW1NRjY9/r1av8XXaTecg2Mra5WoXNJydDYgT7GM2aocDMeV717a2rUcRUWDo3duVMdQ0mJCh/jcRVStrWpaufU1KGAub1dbTdwU9TAOR+nPuaCM9AqA1QQOzCxXl+fqoHIyFDVxQM9lvfuVY9fcon6d2GzqT/zjg4oL1eBss2mlt27VaXw3Lnqq82m3l67XEPtN4QYSxK4jg4JXMe5DwauA7SERuN9jRy+/TBaTMPoNVKxuoKs67LG8GhPLqFpHAwGyTSZ8CcS7O7v5/GODv7Q1oZNryeuacRO8M8jxWikzGYjw2RijtPJRUd6w5bZbJilNYG40IVC6n6yujpV/tHaqt7xPfWUKg0Y6BM7sP50SzqMRnUlkZmplnhcXSlMm6bKBQbWZ2aq0gT5cESIUTXQs3bd4XVsb91OhiMDi8FCe3871T3V/K36b8STcdwWN53B4U8Nn2pLpcBdgMlgQq/TMy93HosLFlPoKaTQU0iuKxejXq4KhRDifKNp6rP3gRB3IFAe+Dw/mVTrgkEV5iaTKvgdGFtfr0LJgaAyHleVxocPq2CxuHho7N69qo1GYaEKHAcC4p071VvO6dOHxu7YoYLu0tKh4NjnU9WwRqOqExgYO1BhnZurjm2gGrumRr01TU8f6qUcDKpzGC9sNvVn4XKpOgqjUU3rkJam3lr39akPAiZNUj2LU1LU0tOjQv1Jk1ToK8SZksB1dEjgOs6dKHAFSIQTvFXwFvHOofAk4zMZVPxPBeaMiXO7cCAepyMWo8Bi4WAoxO7+fr5+8CCtR9Y1RiKc6B+NAcgwmcizWFjgdjPL6aTSbmey3U6GVMUKcaxgEN56S4WzRUVDIezTT8P+/UN9Yltbh+6/Ox06nbqP6/3hrKYN9ZzNzFQlAgOPW8ZPZb4Q56NIPEJrXysvVL/AluYtpNvT0aGjua+ZA10H2NyoJv3TTvhKezST3sTc3LkUegopcBeg1+lxW9wszF/IzOyZ0r5ACCHEhPDBXsrBoAqOg0HV5Wtg/f79av3AW9dwWE349tJL6vvFi1XtQyikQuOamqG3u6GQCkt37FBviXNz1TahkAqDRzL0tVpVCDvQf7egQB3bQDexxkZ1XpdcogJsp3PknltMfBK4jg4JXMe5kwWuAP6/+6m7pw7nDCf1P6mHBBhcBgq+XUDBNwowOCburcBdsRgug4G4pnEwFOLhlhYebG3FazSSbjKxPxQ6bn/YARadjgKrlSVuN5V2O5V2OxU2G2U2G3a5RVqIUwsE1KwN9fXqPrX2drX83/+pMoL8fFW20N6uAtpw+PSfw+MZelc6UFoxbZqaynhgvdOpSiPS0qS9gRAjTNM0AtEAmqZR76+nobeBv+z+CzvadpDlyCKaiA6ujydPXR1vM9rIc+fRF+0jy5HFJ6d8kiJvkaqQdeZSnFKM2TBxPhQWQgghRkMspipUB/rJ9vWpnzdsUFW906apxwIB2LpVtSXwetXS06OWQ4fOfFI7u129xQ6HVQ3GsmWq6riwcKhNxkBLC3H+m6iB6+rVq1m9ejW1Ryamnjp1KnfccQcrVqw46Xa9vb3cc889PPHEE9TW1uL1epk2bRq33XYb//AP/zBixQMSuI5zpwpc3693Sy8H/vEAfdv6ADBlmSj+YTE5K3PQm86f2+6TmoZep0PTNJoiEb60fz/7gkGuSE2lIRxmfyhEXTh80lqdLJOJSrudcpuNIouFiiOBbJnNhkea6Ahx+sJhNXtFfb161zYQzg60NRiYvaC9XU0I1tFx+s+h16vQNTNz6N62yZNV5Wx6urrXLJlU4XBRkRpns430mQpxQQrFQrzT9A6Heg7hsrho8DdQ76/nmQPP0NjbiM1kwxf2DWtfXquXKelTKPQUku3Mxh/2U5VZxZVlV1LoKcRj8UiVrBBCCDEM8fhQWNvTo8LZbdvUTWQul7qRrbUVXntNvV03mVSQOxwmkwpgi4rU/oJBmDMHrrpKTXiXn6/enouJb6IGrs888wwGg4GKigo0TePhhx9m1apVbNu2jalTpx53G5/Px9KlS/H7/dx5553MmzcPo9HI+vXrueeee9iyZQter3dEjk8C13HudAJXAN9GH9sv3n7UOtskG6X/WUr6J9IvmAuYV3t6eLilBZfRSLbZzL5gkH1HJu86lQyTiTKbDYdeT6XdznyXi8kOB+U2G6lG4wXzZyjEqInHoalJLampKoTt6FBtDXbtUuGsxaLWNzSoe7POlN2u7rHSNPVucepUFc6mpanygfx81SQrM3MotJUPXYQ4I5F4hIbeBrY2b2Xt3rX4Ij7yXfnU+euo99dT3V09rNYFLrMLm8mGUW9kXu48FuYvHOwhm+PMoSy1THrJCiGEEGeor08FsZs2qWkgjEZVF1FXB7W1at1wJlKzWtW2qanwsY+pG9SqqtQyjOhCjCMfDAY1TSMZHJtGx3q7/qwyl9TUVFatWsXKlSuP+/htt93GH/7wBw4cOEBubu5Rj/X19WG1WjGO0PWgBK7j3OkGrpqm0flkJ8EDQQxOA3U/qiPWEQPAPsVOwbcKyLohC73lwvsoKpZM8lJPD7v6+ljm9XI4HOZQKMSf29vZGwxi1+sJnqKRjkOvx2M0MslmY57bTYnVSonVSqnNRpHVikU+4hNi5PX3q0DWah2aJvjZZ1WjrMJCFap2dqrq2s2qHyVGo7pX63TpdOqj+vR0KCtTLQ1SU1U4nJWl7rfKylKPp6erx10umShMiGEIRAJsb91OLBmjJ9RDnb+Od5re4eXDL5PQEhj1xmFP8lXoKaTIo1oVxBIxPFYPlxRdwkU5F1HoKcRpluZ0QgghxJmKRNQEXvX1annxRdiyRdVEhEJqQrSTzcNrNKrJvK66Sk3lMGsWVFbK9A3j1QeDwUR/gg3ODWNyLBf3XXxGrTETiQSPPfYYN910E9u2baOqquqYMclkkvT0dD796U/zwAMPjMThnpQEruPc6QauHxQPxKn5QQ1NP28aXGfONpP3tTxyv5KLKU2asiSPtCYw6nQ4DQYOhUK8Gwjw/dpaumMxFrjdHAqHaRyYpvMkcs1mSm02SqxWcs1mymw2Ku129bPFgkFCGSFGXzis3s0FAiqoffFFNVtBbq6awrWzU1XNvvCCeqdYUKDWdXef2fMZDENVsykp6r4trxcuvVRVzqalqXedLpeapSA7W60zSx9LIT4oGAtS76/nr7v/yo62HeS4cghEA9T769nbsZe2/rZh7cdldpHQEqTaUrms+DJynDnkuHKIJ+JkObO4KOciSlJKsJvso3xGQgghxPknHoe9e+HPf1Zf8/PVFA/vvaeC2hNxOOAzn4H581V7gpkz5S3xeDCRA9ddu3axaNEiwuEwTqeTNWvWcNVVVx13bHt7O1lZWdx777184xvfGKlDPiEJXMe5sw1cAXwbfOz9wl50Oh1aXCPSqIJDvV1P9s3Z5N6ai3OaVIKcSiiRYHVzM892dlJis+EyGKgJhzkQCrFvGI1wTDodRVYrZp2OEquVxW435UfC2BKrlTSTSdoVCDGW4nFVIbtzp7oPympVQezhw/DEE+rj/OnTVYjb2amqXodzv9WJOJ3qOa1W9Y4zI0MFsbGY6kU7ebK6JystbWjxeKSaVlzQuoPdHOo5RDwZp95fT72/nj/u/CONvY2k29PpCHYMu5csgMfiIdWWSn+snxxnDleUXqFaF7hyMOlN5LvzKUstI9WWil4nd7EIIYQQp7J7t6p3CIVUh7CdO1V1bH//sWONRvWW+Ior4HOfg0WLVG2COLcmckuBaDRKfX09fr+fxx9/nN/+9resX7/+uBWubW1tZGdnS+AqlJEIXAG0hEa0PYop3UTHXzuoX1VP/46h33iu+S5yVuaQeV0mRrf0RTsdffE4r/T0UBMKscTrpSYcpiYU4rctLRwKh0k3mfDF48RP8U/fqNORb7Eww+GgyGql0GKh0Gql1GqlyGolXQJZIcYXn081utI01Yagq0tVzv7lL6q6dvFita6rS73LbGtT7Q9CoTOfUhaG2hmkpqrANxJRMxfMnj0UzEYiqmdtUZEaO4Ga3wtxtnojvbzb8i6vHn6Vvlgf2c5smgPNtPS18NKhl+gN92IymIgkTn3nygCDzkCaPQ2X2UVvpJdMRyaXFF1Chj2DDEcGwWgQr9XLjOwZFHmKSLenYzLIXURCCCEEqHqC116Dt95Sb1O3boV33lETfX1QUZGqhL3qKvjWt1Q3LzG6JuqkWcezfPlyysrKjtsyIJlMkpaWxmc+8xlpKSBGLnD9oJ4NPexYtgOdSQcJ0OLqr0Vv15Px6Qxy/l8OnqUedHoJ+M6Upml0xWKY9HqcBgNNkQgb/X7urq+nP5FgicdDTShETThMczR6yv3Z9XoKrVZSjEbSTSZmOBxUHQlni61Wcsxm9BLICjF+aZqqaDUYVFhbX68mCuvsVBN3dXWptgbr1ql7srKzVWuEri41JhQ68+cemDwsHoe8PDV5WGqqCmcbG9U72QUL1HOmpg4tco+XOE8lk0kC0QDNgWbeaX6Hlw+9TCwZI8eZQ0tfC82BZjY3bSaaOPXr84k4zU7iyThei5c5uXPwWr2kWFNo62/DqDcyJ2cOpSmlpNhScJqdOM1Ocpw5uC1u+YBVCCHEeS+ZhGeegbVr1c/btqnq2A8mRlOnwuWXq/qCz39edfASI+t8Clwvv/xyCgsLeeihh477+K233sof//hHmTRLjF7gChBpjhDcG8QxzUHrH1tp/V0rwX1Dt8Zb8i1kfDqDjM9k4F4gb/5H0zOdnazr6aHcZsOg01EXDrMlEGCdz4cBGM5Ny3rAYzQyy+mk+EhVbIHFQsmRQDbfYsEkk3oJMTHF47Bnjwppi4qGKmdfegm2b1f9ab1eta61VVXUapoKd8+m7YHZDDk5Q+FsU5Nqt3DJJUO9aE0mVYZQUjIU2I7QmxQhxlokHiGejOML++gIdrCjdQev1bxGkiRFniI6g510BDt4teZVeiO9uCyqAjapnflteHqdfjCcDcaCJLUk0zKnUZpSqsJYdDQFmshyZLEwfyEuiwuX2QWAx+ohy5GF1+rFYpSZSYQQQkwsfj/87nfwhz+ot7WNjceOufRSuO46+OhH1VQM4uxN1MD19ttvZ8WKFRQWFhIIBFizZg333HMPL774IldcccVxt+nu7mbJkiX09fVx1113MXfuXEwmExs2bODuu+/mnXfewev1jsjxSeA6zo1m4PpB4eYwm0s3o0U1DA4Dib6hi3Rzrpms67PI+HQGrrkuqXw9BxJHJvPqiceZbLfTEA5TF4lwV10d2/v6mO10ktA06iIRGsLhU4ayeiDXYlHBq05Hhc3GArebIquVIouFIqsVm+H0ZwMUQoxTkYgKQ3t7VeXshg2qdGBgcq+uLmhuhqeegmBQlQ8MVNme6QRiA9xude+YxaJaHWRlqSA2mYRoVE1bO22aOpaBatqUFBUQCzHBJZIJesI97OnYw4a6DYTjYYq9xfjCPnrCPTyz/xnag+2UpZSR1JL0hHtoDbTii/hG9DhMehN2k51QPITNaGNq5lRcZhcui4v2/nZiiRjTMqdRkVqBy+LCqDfS1tdGmj2N6ZnTsZvsOMwOrAYrLosLp9kpbRKEEEKcU11d8Prrqgr20UePrSMoLFTTHvz7v8PFF4/JIZ4XJmrgunLlStatW0dLSwsej4cZM2bwne9854Rh6wC/389PfvITnnjiCerq6khJSWH69Ol89atf5dprrx2xYkMJXMe5cxm4AsR74/Ru7sVzsYeeF3to/2s7HX/tGGw5AGDONpP60VTSr0knZXnKac0gJ0bH4SM9YwPxOAvcbuoiEerCYR5tb6c3kcCs0xEdxn89m15PidWqWhUc6SE7UCU70M5AKp2FuAC0tqopZ3U6sNlUANvUBI88opptLV06FMzu3Kmmo7VaIRw+u+f1eIb60+p0anaFkhI1qdhAlW04rKbCLSlRY71e1UNXiAlM0zT6on30hHowGUz0hHvoCfXw/MHnqfXVUpFWgUFnIBANcKj7EG82volZb6Y0tZTeSC+BSIBaXy2xZGxUj3MgxLUarfgjfiwGC9Myp+EwO7Cb7HT0d9Af66cyrZKK1ArsJjsmg4laXy1ei5cF+QsGx2qahs1kI82WhtvixmayYdJLv3ohhBDHl0jAK6/Au+/Cs8+qfrDvv8SdPVtNvPXZz6ogVgzfRA1cxzsJXMe5cx24fpCmaey+djddz3bh/ZCXwKbAUZWvOpOOlOUppH00jZQrUrBV2OSN8jjij8epC4cpMJuJArXhMC92d/PblhbMOh1THA5qw2HqwmECw7jteKCPrEOvJ8NsZpbDQaXdTuGRSb7yLRasUqEmxIUnmVRtD/R61Z+2pUWVIrS0qOlme3pUicIbb6jmXOnp4HINVdT29p75c+t0qqVBMgkZGUP9aVNTVXCcmgrz56u2C++vqPV41LZCnCc0TSMcVx96RBNRAtEAdb46NjVuIpaMMSltEoFIgEA0wN+q/0ZDbwOVaZVYjVYC0QAtgRZ2tO3AoDOQ48qhP9pPf6yfQCSAxrl5+67X6dE0Db1OT64rF5vJht1kpzfSS1+kj3xPPqUppdiMNswGM3s69mAz2rik6BIcZgc2ow1f2Ed/tJ9Cb+HgWKvRSigewmv1km5Lx262D+5D3rcKIcTE1N4O//qv8Nxz6q3k+y9nPR4VvP7Xf4HTOXbHOFFI4Do6JHAd58Y6cB0Q7YhiSjehRTV8633U3FFDYHPgmHHmHDOpH0kl5YoUUj6UgjlTJlyZCDRN48HWVt7u7WWO00lI06gLh9no9/NOIIBJpyM2zP+6Dr2eKoeDgiMVsqlGI5NsNkptNgqtVjJMJpncSwgxJJGAAwegtlb1p+3pUSHsq6+q6WszM1VLgoHJw9avV+Gq0wl9fWf+vDqdCmAHQtiODhXcLlyoKmgHJg0zm1U1bUGBWudySVArLii94V46g50YDUb0Oj390X5aAi282fgmkXiEmdkzB8PZ9bXrqfHVUJZSRqotlf5YP239bbzZ8CYAk9ImDY5tCbQQSUTQoTtnge4H6VD/lwcqbW0mGzajjXp/PUa9kdk5s1X1rdFGV7CL9v528j35TMuYhtVoxWK08F77e1iNVubmzsVtcWM1WgnHwwRjQTIdmeS587AarViNVox6Iw6TA6vRKmGvEEKMoI4OeOIJ+POf1ef7A1wuNdHWl78MM2eO3fGNdxK4jg4JXMe58RK4fpD/735a/9SKbZINLaLR81IP/r/70aJH//U6ZjjwXuLFc4kHz8UeLNkygcNEktA0WqNRQokE+RYLjZEI9ZEI36+poToUYonHQ38iQX0kQk0oRGQY/73NOh3ZZjNmnY5im41FbvdgOFtosVBgseCUCXeEECcTj6t+r9GoCmg3bYK331bvqrOyVGDb3g5r1qi2BDNmqMrb7m5oa1P9Zc+U0aie22yGykpVOZuaqvrVGgxqCt0pU4aC4owM1UtXCHEUTdOIJqKDP4fiIXpCPWxv3U5ftI/J6ZMJxUOEYiHeaX6H6q5q8j35ZDuzCcVCdIe6eaH6BaKJKBcXXkwoHiIYC7K7fTeNvY1kODLwWDyE4iH6o/00BZoAxjTg/SCr0YpBZyAcD+MwOchx5QwGuQ3+BjQ0pmVMI9WeisVgGTyPTEcmM7NmDga5h3oOATA9czrp9nQsRguxRIzeaC+p1lQKPYVYjBYsBgsGnUH15j3yPNLGQQhxvnnzTfjRj1R3rPr6ofUeD9x8M9x9t+qYJYZI4Do6JHAd58Zr4Ho83a91s2vFLgw2A5YiC/07+o8ZY6uwqfB1qQf3fDf2yXaZgOs8cSgY5P+6uognk1Q6HNSHw9RHIvyqqYlgMkmmyURnLMZw5m32GAyU2GwUWCxkmky4DQamOZ1U2u3kWyzkms2YpGejEOJM+Hxw+LAKbQf603Z0wIMPqiraJUsgEFDf79gBNTVgt6vxkciZPafRqJqJ5eerINZiUVW6FRWq3CIzcyigdbulglaIUZDUkkTiEaxGK9FElFA8xKHuQ9T6anGanaTZ0wjGgvRF+3j+4PMEY0EW5i8klogRiofY1baLvZ17yXJkUewtJpKIEI6HeenQS8SSMWZmzSShJQjHwzT2NtLW14bdZMdushOOhwnHw0QSZ/g7ZBTp0GEymIgn45j0JrKcWVgMFixGCx39HcSSMUpTSsmwZwwGuXW+OtwWN3Ny56jg1mCh3l9PNBFlcvpkclw5WAwWklqSjv4OPFYPFWkVg/tNakksBgtui3uwJ/BAICzhrxBipCST8NprcP/9qtNV8siFaFYW/Mu/wK23qhBWSOA6Ws7bwNXn8/Hkk0+yYcMG6urqCAaDZGRkMHv2bK688koWL158Lg7jrE2kwBUgGU0SaYxgK7URbY/ie8PH/lv2k/AlQAcfLCjQ2/W4F7pxL3Djmu/CPd+NJVeqYM8nmqbREYuRcqRqtTka5amODv7c3o7TYKDCbqfhSDi7u79/WDUnOsBhMOAxGJjucFBxJIjNM5spsFrV9xYLFgllhRAjIZFQ1avBoApnH30UGhpg8eKhcHbdOlVlO9Aftr1djR1Gf+yj6PWqfUFRkaqedThUYFtVpcLZ3Nyh9UKICaU72I0/4sdtcRNPxgnHw9T769nftR+b0UaBp4BIXAW5z1c/T2+4l7m5czEZTITjYfZ37mdry1ZSrClUZVSpIDcR5vXa1+mP9jMlfQpGg5FIPEJbfxsN/gYsRgteq3dwv/2xYwsixhMdOpxm52AAG4wFiSaiZDmzyHRkYjFY0Ov01PhqBls5WA0qsG3ta6Uv2kdpSilFniIsRlXRW++vx26yMyNrxuB+Y4kYer2eFGsKHqtnMDgeeNxqtGLQy7wEQpwvNm+Gb39bVb12dKh1Hg/MmwcPPAClpWN7fGNtIBgsLi7GJuW/IyYUClFbW3v+BK7Nzc3ccccdPPLII+Tm5jJ//nxyc3Ox2Wx0d3eze/dutm7dSlFRET/4wQ/47Gc/O5qHc9YmWuB6PL4NPvwb/GRen0nwvSC+N3x0PtlJ6GDouONNGSbci914FntwzXfhmuvC6JRbzC8Ef/f72dffz3SHg854nIZIhGc6O3m5pwev0YjDYKApEiE6zF8jmSYT+RYLBp2OLJOJi1wuKmw28o9M8JVnsWCTSb6EEKMlHlcNxQ4fhrlzVfuDtjZ4+WU1tW56OqSkqHC2vV2Ft8NlMqkK3RkzVI/Z3FxVHetywbRpUFYGOTnSzkAIcZSBCcjsJjsaGpFEhNZAK3s692DQGShNKSWSiBCJR3it9jU6+juYljkNl8U1GBBvrN+IzWhjbu7cwbFv1L1BR7CDKelTcFlcRBIROvo72N2+G5PBRJGnaLAquK2vjVgyhklvIpY8ixYvo8ygM2AxWkCDWDKG2+IebNlgMVio89dh0puYnjUdh8mBxWihJ9RDT7iHfFc+ZallgyFudXc1NqNt8M/SZrQRS8ZIJBOkO9LJdmQP9hB+/1e9TooHhBhJsZjq8/qTn6jwFdSNRbfeCt/7HuTlje3xjZVYLEZ1dTW5ubl4pOx3xHR1ddHe3s6kSZMwfCB3mJCBa1ZWFjfddBM333wzVVVVxx0TCoV46qmn+OUvf8knP/lJvvWtb43mIZ2V8yFwPZ6e13tofbgVS64Fa7GVwNsBejf30r/rOJ+668FSYME1x4VrngvnTCfOmU7MOTLZwIUkoWkYdDqSmkZHNMoPams5FArx4dRUuuNxGiMR3vT7ORwOYwCGW1Nm1+upsNkGK2PNOh1lNhtT7HYKj6xzSCgrhBhtySS8/rp69z91qgpmm5vhhRdUWYbXq1oRNDWpStvh0OkgOxs0TW2/dCmUl6vWBjk5Q20O5NYxIcQ5FE/GicQjmAwmTHoT0USUnnAPezr2EEvEKEstIxKPEElE2NS4iQZ/A+Wp5aTb0wnHw7T3t/PioRfRoePS4ksHx/69/u/U+euoSKsg25lNJB6hJ9zDxvqNAMzKnjVY6Vvvr8cf8eMyuzDoDYP7SGrDaX517pgNZmxGVW0WiodwW9wUeYoGQ9kDXQfQ6/TMy51Hmj0Nm9FGIBqgOdBMriuX2dmzB8c29DZgNViZnDGZFGsKNpMNk96E1WhVk8Md+Vmur8SFIJlUAesvfgHhsFpns8E3vwn/+q/q8/ALiaZp1NfXE4vFyM3NRS93ip4VTdMIBoO0t7fj9XrJyck5ZsyEDFy7urpIS0sbtfHn2vkauB5PrCvG/n/cT9/2PnK/nEvgnQC9b/cSqT9+vyuD24BjmgP3QjfOWSqEtU+2ozfLL4cLVX8iQV04jBFINZlojERoiET4t5oa2qJRLvN66YjFaIxEqAmHiQ3z15HLYMCi11NgsTDX5RqskM0xm1VPWYuFVKNR3qAKIUafpqm+sw8/DNXVsGCBCmebmuDVV2HPHtVuIBRSFbbDkZ6uSjry89UkY3l5cPHF6v66/Hy1uFyje15CCHGOaZpGUksOtg6IJ+NUd1cTiASOqvTd2baTQ92HyHXnkuvKJRwP0xvp5al9TxGOh7my/EpiiRiReIR3mt9hd/tuir3FlKeWE4lHCMaCPHPgGeLJOIsKFhFPxgnFQtT4amgJtAxWvA5MEjeWVb96nX6w+hYg351Pmi0Nu8lOPBmnOdBMhj2DOblzcJgcOMwOqrur0aHjopyLyHPnqeppTcMf8ZNpz6QirQK7yY7D7MBmtEmrBjGuxOOqK9Sdd8JG9dkMer2aXOuBB1RXpwtFNBqlpqaGZHJ8ffA0kXm9XrKzs4+bE0zIwBXg2Wef5aqrrjovUvkLKXA9kYP/fJC2NW14L/Wit+jp295HcH+Q482+pDPqsBZbcV6kAljHVAf2Kju2Uhs6g4RhYsiB/n5e9/sx6XRkmc00RiLUh8Pc29hIKJlkks1GczRK3zD7L1p0OnItFrxGIyadjnKbjdlOJ7lHJvnKOxLMSrWsEGJUaZrqG6vXqyZl1dXwq19BXZ2qcG1uVuHstm1qArHhMhggI0O1MBgIZxMJqKxU6woLVTmIfPAkhBBnTNM0mnqb8Ef8pNnSiCajhGIhDvUc4mDXQRxmB7muXEKxEKF4iOcOPkdftI+5OXPR0AjFQuzr3Mfu9t2k2FIo8hYNjn2n6R2iiSh5rjzimgp9A9EA0UT0nJ+n2+LGY/HgMKt+5O397bjNbi7KvUiFsyYHDb0NxJNxZmTOoMhbpKqRdQZ8ER9ZjiymZEzBZXbhsrhwmV2YDNJCR5w5TYOnn4YvfAH6+tS6yZPhnnvgmmsunLc3yWSSaPTc/044H5lMpmPaCLzfhA1cjUYjWVlZ3HzzzXzxi1+kvLx8tJ9y1EjgOkTTtMFPBno397L3pr0kI0nSr0mnb0cffTv6SPiPH47pjDosRRbc89zYq+w4qo4EseU29KaJH8yLkRVLJjEO/FtLJHiio4PnurpIM5nIs1gGJ/l6uafntPftMhhUVazZjNtoxKbXM9lup8rhIM9sJvdI5azpPPjASAgxjoVC0Nio7qPTNBXCHjwI//u/ahKw2bPVuqYm8PuHt0+bTbUu0OuhogIWLlTBbF6emhK4pAQyM9XjQgghxlwsEaM30kssEcNqsg6GsxvrN9IZ7KQitQK9Tk9/rJ8DnQfY3LQZh9nB5PTJ9Ef7CcaCrKtZhz/sZ1LaJHQ6HcFYkNa+VpoDzRj0Box6I8HYMFvhnCWX2UWaPW0wgG3qbcJtcbO0cOlgONve304imWBq5lRKvCW4LW4cZgdWo5U8Vx5eq1fC2wuYz6dC1zffVDcUAXzkIyp4nTFjTA9NnGcmbODa0NDA73//ex5++GFqa2tZunQpX/rSl/jUpz414WZck8D15N4fwmqaxnvXvYfvFR/pn0hHC2v07+mn/71+tMgJ/tnpwVZmwzHNgW2SDfsku/paaceULj2MxIlpmkZbNEpjJMJUh4PWaJTmaJS/tLXxYnc3+VYrOWYzzdEoTZEIB0PHnyTuRKx6PeU2G0VHKmM9BgOpJhOT7XaKrVbyLBbSTSb08m9UCDHatm6FN95QvV5tNhXUVlfDX/+qAtu0NOjsHN6+jEYVwOblqQm9kkmYMkUFvFlZasnMVL1nnc7RPS8hhBDnhKZphOIhDnUfoq2/Da/VC0B/tJ8aXw2bGzdj1BupyqiiP6aC3BerX6Q92M6U9ClYjBYCkQCNvY0c6DqAQW/AaXYSiASIJI7ffu5sOEwOvFYv0USUWDJGeWo5k9Im4bV4sZlsHO45TKYjkw+VfAiv1YvH6sFpdpLlyCLFliKTnZ0H/H41sda990I0OjSx1n//t3xuLEbGhA1c3++1117joYce4oknnsBoNHLdddexcuVK5s2bdy4P44xJ4Hpm3h/E+jb4qLmjBr1VT8rlKQT3BOl/r5/A1sBx2xIMMHqNR4ewR77aKmwYnRdQMxdx1uLJJH/3+zkUCrHI46E5GqU5EuGvHR1s8PnIs1jwGI00RSK0RKPD7i1r1Omw6vV4DAZmOZ0UWa1km82qEtdsHgx9M00mjPLOQAgxGpJJdeURDqt2BU88ARs2qNDUblfhbE0NbN9++vt2OIZCWINhKJy96KKjw9n09KHKWiGEEBeUWCJGdXc1zYFmHGYHmqYRiAao89WxoX4DSS3J1IypBKIBApEAr9e+Tmt/K0WeIkwGE4FIgI7+DtqD7SNyPDp0pNnTSLen47a4aQ20kmJL4cNlHybdnk6aLY1YMobT7GR65nTKUstwmuUDxvFq2zbVkWlgvtJLLoHf/AYmTRrb4xIT33kRuA4IBAL85S9/4aGHHmLTpk1MmzaNHTt2jMWhnBYJXEfP4e8fpndTL9k3ZhP3xQnuD+J71Udw76lveTHnmbGVqfDVUTVUHWsttsqkXeKshBIJHmpt5UAwyPKUFBXORqP8X2cn2/v6yDCpW5zaYzGG+8tUB2SYTOiAFJOJOU4nBUfC2CyzmRyTSbUykB6zQojRoGnQ06PC15QU9bWpCZ58ErZsUW0HnE41+VdzMzQ0nP5z6PVq32lp6vtYTE36NWOGWpeWptooFBRAWZnqR5uWBhbLyJ+vEEKICWWgAjcQCWDUG/GFffjCPtbXredQ9yHy3HlYjVZ8YR81PTWsr1tPUktSmlI6OLY50ExCG97cDx/kMDnIsGfgj/jxWD1cWXYlOc4cspxZgwHu9KzplHpLpd3BGAgG4Z//Gf7yFzW3qMUC3/oW/OAH6mYdIc7EeRW4Ahw+fJgHH3yQ1atX09vbSyw2djNADpcErudW/3v9dL/YjSnThHOWk9CBEMH9QWq+XwMJMKYYifecZNZnPZgyTFiLrDimObBX2rGV2bCWWbGV2TC6pDJWnDlN04hrGia9nlgyyeFwmPsaGqiLRLgqNZW2WIyWSIRXenqoi0RwGgwEE4mTFXIfw2kwkGM2E9c00oxG5rvdFBwJY7PNZnKOLGnSzkAIMRoSCTXBV2Mj5OaqELatTc1ksWWLakUwEM42NamxZ8NiUQFtRoYKYr1etTQ0qAB3wQL1mNerWip4vaqyNiVFHYdU1QohhEC1R2gONJNIJoglY3QGOznYfZDnDjxHOBFmasZUukJddAW7eLvpbbpCXRj1xtOatEyv05PtzCbHmUNPuIccZw6fmPIJCj2FFLgLyHXlkuvKxaCXAorRUFsLX/4yvPSS+jkzE/7+d5jAUwaJMXReBK6hUIjHHnuMBx98kA0bNlBSUsIXv/hFbr75ZvLy8s7loZwRCVzHnpbU6NvZR+hAiPR/SCcRSBA6GKLu7jq6nu7CWm7F6DQSPBAkGTx5tGX0GrFPOTqEtZXbsJXZMGVIz1gxMpKaRlcshl6nw2s00hmLUR0M8tOGBtqiUT6Wnk5rNEpLNMrf/X5aolFMOt2wWxmAameQZTKRbTYTSSbJMJtZ4HJReKStQdbAYjLhNMoHDUKIUZBIQGsrdHSonq9dXWp5/nnVdzY/X7Ub6OpSlbOvvKK2SU9XM2EkT+fjqOPQ6cDjgUgEzGaYPh1SU8HtVuv6+6GoSLVBcLnU+lhMBbhZWWpbl0sFt3J3gRBCXHA0TaM/1k9rXyv7Ovfx7IFnBycga+1rpa2/jY31G+kKdWHQGYZdQZtmS2NG1gzKU8sp8haBBksKljA/fz52k32Uz+r8pmnwr/8KP/+5+jkrCx59FC69dGyPS0w8Ezpw3bRpEw8++CB//etfiUajfOITn2DlypVcdtllZ7S/1atXs3r1amprawGYOnUqd9xxBytWrDjhNj6fj3/7t39j7dq1dHd3U1RUxH333cdVV1017OeVwHX80jSNWFcMLa5hybaoW1EOhdh19S4iTRHy/ymfcF2Y0KEQ/bv6TxnGGpwGrGVWrMVW1S/2SBBrLbNiLbCiM0gYK0ZeMJGgJRrFrtfjNBhoiUapDoX4WX09HbEYV6Wl0RqN0hqNsiUQoDt+kgrvE7Dr9YMBbCiRINNsZq7TSb7VOhjKDjzuMhjkgwchxOhJJIb6wfr9sH69qpzNzFSLz6dC3EceUYHpxRercT4fHDigWiMYDGo/I0mvVz1rc3JUMOtyqQpeiwXmzlXH5nKpmTv6+lSYXF6uwlqHQ01m5nar751OtZ38LhVCiPOCpmnEk3G6Ql00+Bt4t+Vd1u5dSyAaoMBTQIO/gYbeBhp7G0+5rzxXHuWp5ZgMJlKtqVxecjmLChYxKW0SVqP1HJzN+eEvf4F//3c4dEi9hN95J3znO3Ljixi+CRu4VlVVsX//fmbPns3KlSu5/vrr8Xg8Z7XPZ555BoPBQEVFBZqm8fDDD7Nq1Sq2bdvG1KlTjxkfjUZZsmQJmZmZfO973yMvL4+6ujq8Xi8zZ84c9vNK4Hp+6Hiqg47HO3BMc2Ars6kQ9r1+2v90pDm7Dk7WkFNn0mHKNGErteGY4VBh7EAgW2LFYJXKGDH6emIxGiIRnHo9Zr2e1miUg6EQP29spCsW44qUlMG2BnuCQQJnEEhY9frBADaUTJJhMjHP5SLPYjmqajbLbMZrNEo4K4Q4d5JJFXbqdKppm8+nqmvXrlVfFy9WjwcCauKw7dtVC4T8fOjtVcubb6p9paSocWfwIdaw6PWqBUI8roLagoKhMLapCYxGmDlTBbkOhxrn96tK4SlThsYmk+pYPR61zm5X1bzyu1cIIcadjv4OtrVuI5FM0BHsoLq7mjcb3mRD/QYSycRJK2T1Oj1FniL0Oj3TM6fzmamfYVrmNCanT5a+sScQDMJtt8HDD6ufc3LUy3xx8ZgelpggJmzg+s///M+sXLnytILNM5GamsqqVatYuXLlMY/df//9rFq1in379mE6i07KErievxLBBD0v9xBpiZB9czbh2jCh6hC1P6ylb2sftsk2SEK4JowWO/l/G1O6CVulDVupCmAHvlpLrVhyLej0cmEkzq3WSITDoRBOoxG7Xk9bLEZ1KMR/NTTgj8dZkZpKeyxGWzTK7v5+es8gnDXrdGQeCWBDySTpJhPzPxjOHnk8VXrOCiHGA01Tk3fZbOrnSAR27VLLQIVrIKDCzz/8QYW0l1yixvX2qnF796rWBVlZqgq3rw/q68/dOeh0ajGbVWBrt6ulo0M9XlExtD4eV+vT02Hq1KGxfr/avrBQBbp2uwqIB0Jdm019lVYLQggxIhLJBL6wj+ruaqq7q1m9ZTXV3dVkOjJp6G3AF/YddzuzwUyaLY0sZxYfq/wYy0uWMzN7Jm6LZBOgXtYfeABuvVX97PGoz1ynTx/b4xLj34QNXEdbIpHgscce46abbmLbtm1UVVUdM+aqq64iNTUVu93O008/TUZGBtdffz3f+c53MJzkzWMkEiESiQz+3NTURFVVlQSuF5BkPEmkPoLBacCcaUZLaPj/7mffF/eR6EuQfXM2oUMhwofC9L/Xf8owVmfWYS2yYs4zYyu2YZ9ix1o6FMqaUuQTSzG2WiIRasJhPAYDdoOBtiNtDX7e2IgvHufK1FTao1HaYjF29fXhP4NwVg+km0xkms2EEwlSTSbmuFzkms1kHpkELNtsJtNkIsNkwiPVs0KIiaSrSwWvWVlD/WN37YJ33lFXfxUVQ+HsI4+owHPpUhWc9vXBnj2wY4caW1Cg1vX3Q02NqnIdjTYKw2E2q6+apnrfpqYOhdXNzap6d9Ystc5mU9W7sRhMnqwqiwfGtrerCc+mTBkaq9OpgNftHgp5TSap3hVCXHA0TaO1r5Un9z3Jo7sfJZKIYNAb2N2+m95I7zHjdego9hZjN9m5rPgybp51M9OzpmM2mMfg6MeHP/0JvvIV9dLp8ai5PqWvqziZCRm4fuQjH+GHP/whCxcuPOm4QCDA//7v/+J0OvnqV786rH3v2rWLRYsWEQ6HcTqdrFmz5oT9WCdPnkxtbS033HADt912G9XV1dx222388z//Mz/4wQ9O+Bw//OEP+Y//+I9j1kvgKo6n9U+t9Lzcg2exB6PXSKgmRODtAJ1PdoIBdDodWvzk/+X0Nj3mXDPOWU5sJTaspdahKtkiK3qLNKIR40d7NEpdOIzbaMSq1w+Gs79obKQnHucjqam0RaO0RaPs6OvDd4YBgUmnI9NkIq5peIxGLjoSzg6EsdlmM3kWCxlHQlyHVGEJIc5HsZi6Z9LhUKFnMKimad65U4WTZWVqXTAITz6pgs2FC1V4GQyqatwNG1SoedFFQ2M3blSVvCUlqv1BMKhaNPT1je356vXqPA0G1V5hIJz1+1VlclGRCqRtNjX2wAH1Z7N06dDYzk4Ih9W5FRWpyl2zWZ2b16uqmAcCXptNtXcQQohxSNM0Dvcc5s437mRb6zZyXbnsat913F6xFoOFi3IuIs2WxofLPsznpn+OdHv6GBz12OnpgWuvVS97ZjPcey8MM2oSF6AJGbj+7ne/44477sDj8XDNNdcwd+5ccnNzsVqt9PT0sGfPHjZu3Mjzzz/PRz/6UVatWkVhYeGw9h2NRqmvr8fv9/P444/z29/+lvXr1x+3wnXSpEmEw2FqamoGK1rvvfdeVq1aRUtLywmfQypcxdmK98Xp29pHPBAn9SOpRBojhGvCVH+zmv7t/bgXuyEJoZoQsbbYyXemA0ueRbUnKFETetnKbYPfW3IsMpmXGLe639dz1mYw0HGkrcF9DQ30xON8LD2d9miU9liMzb29tMdimHU6omfwMmXX6zHodDgNBmY4HOQeCWMdBgMpRiOlVis5FouqoDWbsUhHfSGEOFokoip1k0lISxsKZ998U1WzlpergDIcVqHv66+rK9r581UYGgrBK6+oSc+qqlSJUSikft62TQXExcVDY9vbVaCs06mQdSxZLCqUttnU9y0tKoidPXsonO3shO5uFeROnqzWWa2qktluhyVLVMXvQNgdDkNuruojPBDuWiwqIJbXICHEWWjvb+c3W3/D2n1riSfiNPQ20BPuOWbctMxpXFx4MXNz5nJVxVVku7LH4GjPrVAIbrhBfQYJ8MUvwoMPju0xifFpQgauoELLxx57jEcffZSNGzfi9/vVk+t0VFVVceWVV7Jy5UqmTJly2vv+1a9+xapVq2htbcVisXD55Zfz5MD/pve59NJLMZlMXH311axevZr6+nocDgddXV2nFZ5KD1cxkhKhBOgYnGTLv8lPzb/XQJL/n737jq+qvv84/jp3r+wd9t5DUBF3FfceraMWq6jVal3Vn2LrqgNHHXWvKtatuK2WKgpKRUUQWbIJIZC9193n98cJQRSVkeQm8H4+Hnnck3tPzvcDenPJO5/7+ZJ5YibNa5oJrg1S/VE18ab4L15v02ZerhwXvqE+fAN9eHp78PRqCWS7KZCVrqEhGqUkHCZgt5PicFAeibC6qYl/bNhARSTCCZmZlEcilIXDfFxTw/pQiBS7nZBpEoz/8nPlhxyGgc9mY4jPR15LOOs2DHx2O/29Xnp7PGS1hLNZTicu/XAsItK2wmErmHS7rQCyudkKNefPt8LfIUM2h7OzZ8O6dVbom5lp3VdaCv/9rxXW7rvv5nO/+cYKTPPzrQC0udnq5t00Z9fttq6fSC7X5hA2ErHeA5ueboW5m+5fssTq9N13X+sxj8dq39q40Zq9u8cem89ds8YKfYcNs7p4N3Xuejybg2SNahDZZZmmyerq1by17C3u/+J+KpoqCMV+/H2uR3IPTht2Gof1O4wDeh6A1+lNQLXtLxq1fu+3cqX1+eTJcNtt+jYoW+qygesP1dbW0tzcTEZGxk5tYPXKK68wceJEHnvsMcaNG8eECROoqKigqKiI7OzsLc697rrrePLJJ6mrq+OZZ55h33335c477+SJJ57gsssu4957792mNRW4SiJUvl9Jw6IG0n6VBiY0r2mm8r1Kyl4sw55sx5nuJFQU+sVxBdiwwtdNAWwvN94+Xty93Nbn3d3YHAqSpGsxTZOaaBSbYZBst9MQi7EmGOThoiJKI5EtOmf/U1XFsqYmcls27SqLRIjuwMug0zDo0bIZWJbTidtmw2EY9Pd6Gej1kt1yf5Y6aEVEOq9YzAox43ErdC0osObO+v0QCFjhbHU1fPKJFQbvs4/VqdrcDF9/bf303r27NZagudnaSO2TT6zQdPTozeeuX291C3u91nrNzdY5ieTxbO6+BWs0w6ZwNhSyOo6zsmD48M1BbkGBFYYPH2495vVaAXcwaH3evbsV9Pr9W37oNVAkoUzTpLypnNmFs/ls3Wc8Nu8xgtHgFue47C6yfFkcO/BYrt3/Wnqn9k5Mse0kGoWzzoJXXrE+P/tseOopTZGRzXaZwLUtTJ48mbfeeou99tqLq6++mhdffJE777yTtLQ0rr76apYuXUq3bt2YMmUKAOvXr6dfv37k5OQwY8YMVq5cybnnnsuAAQMAmD179jatq8BVOgszZhLaGCLWEMM/xE88Gie8Mcyyc5fR+G0jmSdnYkZNguuCNC1pIlwS/uWL2sGd78aR5sDb34t/uN/qkG3pknX3cGNz6h/N0rXFTZOIaeK22TBNkw2hEE8WF7MxFOKojIzWcPa9ykq+bWigu9uN326nLBymIhJhR6bQJtntreMLXIaBzTDo5/Ew1O9vDWa/H9J6NINWRGTXFo1aQWxdnRV0hkJWELtyJaxaZXWi5uRY9zU1wQcfWLd77mkFxc3NsGwZLF8OaWlWB29zsxV+LlhgXT872+oc3nSNHXgHSJvweKzg1eGwZucGAtYYhk2BbFGRVdvIkVZou2lGcXn55tB307lOp/X3FQhYn7t2302BRHZUNB7lv6v/S2lDKZ8VfsaHaz780RzYEdkjOH7Q8Rzc+2AO6XMINmPX+Bnw6afhggusb6MTJsD06fqdkFgUuH7POeecw9SpU3E4HKSlpTFy5EiuueYann/+eWpqaqitraV3795MnTq19WtuvvlmbrnlFux2O927d+ekk07ivffeY+LEiVx33XVbXUczXGVX0LiskfLXyjGcBin7pxBaFyJYEGTdHeuIN8Rx93ATLg1jhn/h24EBrlyXNTe2lwd3T6sz1tPTOnb3cOMI6NeEsutqjEZ5ubyc9cEgh6alUR6JUB6J8EZ5OXPr6+nt8ZDeMgKhLBymZAc7mAItAa3HZsMAens8DPP7W0PZ9JaNwjZ97lVAKyIiPycSsTZCa262xihs6r797DOri3fTHhjNzbB0KcydawW5w4ZtHs/wzjvWOIaxY63wtLnZGu2wabOyjAxrHMKmj46waVRCKGTd9uu3OZwtL7eC54EDrbELfr/VYVxaao1lGDlyczez02nNGU5Ptz4PBJTCyG7DNE3e+O4N7v/ifsqbyllZtZK4ufkXNE6bk7NHnc35Y89nr/y9MLr4e/HffBNOOWXzBJrZszVeQBS4bmHjxo1069aNzz//nPHjx7fe/3//93/MmjWLL7/8cqtf98ADD3DVVVdhmibRaJQLL7yQRx999CfXuemmm7j55pt/dL8CV9kVhEqs4DUwIoDNayNcEqbk2RI2PLQBV46LpL2SCBYECRYEaV7RvE3XdKQ7cKQ7cOe68Q3z4e3nbQ1kPT09uHJdmiMru4VQPM7/ampYHw4zPjm5NYSdVl7O57W19PV6yXG5KI9EKAmHWbwTP5z6bTYCdjt2w6C7271F56zbZqO3x0NeSzib7XLhU0ArIiLtad06K/DMbNkVvbHR6tz94gurK3XoUKvrtrER3nvPGmEwapQVBjc2WuMLFiywQtTu3TeHuKWlVmtaR9g0U9fthgEDrBnAgYA1HiIUsrp0+/e37ne5rA3hMjKsebqBwObzk5I2nyPSBVQ2VfLBqg+YumAqM9bO2OKxvml9OazvYZy7x7ns3W3vBFW48379a5g2zTqePBluvz2x9UjiKXD9nh0JXGfOnMnpp5/Orbfeyrhx41i1ahWXXXYZ559/Ptdff/1W11GHq4il8N5CmhY3kfXrLKK1UYIFQSrfq6Tuf3XYU6zwJla7Df8AtrFlAJvnwtvP2/q5u4cbR7K6ZGX3Eo3HWd7cTEU4zKhAoLVz9tWyMj6rraWf10s3t5vycJgN4TAza2oAcBkG4R14GXcbBk6bjRynk4E+X+u4g1g8Tg+Ph/4tYfCm0NZvt3f5bgYREdkFNDZaYwl8Pit4bWy0NkCbM8dqVxs+fHM4++9/W2MbRo60RjQ0NVlB8GefbQ59N11vzRorRLXZ2m/0gtNpBa8+n9Vt7PFYs343hbK1tVbn8aBBVlduSor1WGMj5OZC797WOIXkZIW30mGKaot4fuHzfFv2Le8sf4emSFPrY31S+3Dl+Cv57YjfkuZNS2CV28804cYb4ZZbrM9vv90KXmX3pcD1e8LhMD6fj2nTpnHiiSe23n/22WdTU1PD22+//aOvOeCAA9hnn324++67W+97/vnnueCCC2hoaMC2DW8b0QxXkc2iDVGCa4MYDgP/EL8VxBYG+e6s7witD5F9ZjbRmiihwhCNSxqJVkW36br2ZDuOFAee3h78I/xbBLTuHm5c+S5t7iW7rbhpUhmJUBGJMNjnoy4Wozwc5vWKCmbV1NDb46GH2015JEJRKMRr5eUAdG/ppg3twMu+DUh1OOjj8ZDVEsQG43HyXC6G+/1bjDfIcjoJKKAVEZGuJBy2gk232wpd6+th40brvcahkDVaob7eCmc//BDWrrW6XlNSrPs3bLBCX8OAPn02n1tZ2T4B7qadftLTrXEJyclWaFtQYB0feKAVzqakWGMVnE7rvO7drVERaWlW4CuyHRrDjby8+GUu/c+lWwSvHoeHYwYcw3ljzuOIfkd0qX8D/v3vcPXV1vHll8N99yW0HEmgXSJwrampYdq0aaxevZqrr76a9PR05s+fT05ODt26dduua40bN469996bBx98EIB4PE7Pnj255JJLuPbaa390/tixY5kwYQJ33nln630vvfQSkyZNor6+Hvs2vMVSgavIjqn/pp6q/1bhTHfiH+YnWGiNKlh73VowwTfUR7g4TLR6G0JZmzVL1t3djSvHhTPbiXdgy+iC7m48PazOWZtLoaxIzDSpiUbJcDoxTZP6WIx3WsLZHh5Pa+fs+lCIp4uLCZkmowMBKlq6bIM78IOiwzCsELYlgG2Kx8lyOhkdCJDvdv9oo7AkBbQiIrIrqqmxxiC43db82IYGa5Ow6dOtbtZ99rHuq6+HGTOsTdP69LEC0dpaa9TCokVWO57P17azcQ3DCm779bOC27Q0K1xuarI6bEeNskLbtDRrtEKPHtbmbOnpVi163d5tmabJ3I1z+bLoS56c/ySLyha1PpYXyOOa/a5h0phJBFyBBFa57a69FjZFRFddBd/rz5PdSJcPXBcuXMiECRNISUmhoKCA5cuX07dvX/76179SWFjIv/71r+263iuvvMLZZ5/N448/zt57783999/Pq6++yrJly8jJyWHixIl069aNKVOmANY81nvvvZcnnniidaTARRddxNixY3nllVe2aU0FriJtK9YYI7g+iG+AD8NuEK2PsvHxjZQ+X4qnrwf/UD+hwhDBdUFqP63drmvbk+zWDNneVmesu/v3Pnq4cee7sbkVyop8X9w0sbX8EGWaJp/V1DCrtpYcl4v8li7ZwlCIB4uKaIzHOTglpXUEwsZwmOgO/LPCZRhWCOtyEYrHSXM42KMlnM12Osn8Xjib7XKRrIBWRER2J/G41XkbjUJdHcybB8uWWTNjk5OtcHbjRnj9dWszs/33t86rrYX586G42ApPTdMKgXc2AnC7N4916NYNRoywasnMtOrIzbXC5Nxc677MTGuDMr1273JM02RO0RyOe+k4qpqrWu9P9aRy3pjzuGzcZXRPbpvwqr1EItaEkVWrrKfZ9OkwYUKiq5KO1uUD1wkTJjBmzBjuuusukpKS+Pbbb+nbty+ff/45Z555JgUFBdt9zYceeoi7776bkpISRo8ezQMPPMC4ceMAOPjgg+nduzdTp04FIBqNctttt/Hcc8+xYcMGsrKyOO6447jttttITU3dpvUUuIokRjwSp/KdSpoLmsk+LZtwSZhQUYjip4qper8K7yAvrmwXofUhQhtCmOFt+9bmzHZiD9hx5bnwD/db4Wz3LcNZu0cbDIlsi+VNTcyrryfFbidrUzgbDHLP+vXUxmIclZ5OZUs4u7ypibod3PjE2RLQmkCKw8Eov5/ubjc5LhcZTif5Lhe5LlfrHFqHdpoWERGxxONQUQEzZ1odtEOHQnW1FcR+/LHVUZufb3W21tRY58yfb32tw2GFvjvCMKyxB717bw5nS0shJwd+9SurgzY31/o8J0dzarsY0zSZWTCTZRXLuO+L+1hZtbL1sUN6H8I/jvoHw7OHJ7DCnxcKwWmnwdtvW03cX39tNZvL7qPLB64pKSnMnz+ffv36bRG4rlu3jkGDBhEMBjuqlB2mwFWk8zFjJvFwHLvXCkajjVHWXLOG5tXN5F+QT3ijFc5WvFNB09Im7Cl24sE4ZmgbQ9lMJ65uLtzd3Nj9djx9PXj7enF3c1v357txZjoxbPqtvci22hgKsaa5mWS7nRSnk7JwmMJgkLvXr6cyEuGEzEwqo1HKw2Hm1ddTEongNAwi2/nPFgPIcDoxgGS7nT2Skqxw1ukk1eEgz+1u/Tzb5cKlcFZERGRLpmmNGtg01qCqCmbNsjptN40jqKiwxiW884517oAB1n0VFVaatb28Xivx2hTClpZaxwcdBL16WcebOmi3YTSgdJy4Geed5e9w5utn0hxtbr3/+EHHc+uvbmVEzogEVvfTgkFr/PHcudb+e7NnW2OQZffQ5QPX7Oxspk+fzh577LFF4Prhhx9y7rnnsn79+o4qZYcpcBXpuiLVEYIFQWtDrj4eIpURmr5rYuXFKwmXhsk9N7c1nG34pmHb5sluYgNnhhNvfy+ufCucdeW4cPd04+5mjS9wdXPhCDja7w8osouqj0bZGA7jt9nIcDopj0RYFwxyR2EhpeEwp2ZlUR6JUBoO81ltLYWhED6bjeZ4nO39R06aw4EBJNntjAkE6O7xkNMyxiDT6aSvx0NuS0Dr0Q94IiIiP880YcUK+OYbq2s1ELBC2MJCePZZa/TB6NFQUmKFqsXF27/RWEoKDBlibQrWvbsVCPfpAwcfDH37Ql6etXGYdKj6UD23fHoLq6pW8daytzAxMTDYI3cP/nXSvxiWPSzRJf5IUZE1vriqCgYNgu++0ySM3UWXD1zPO+88KisrefXVV0lPT2fhwoXY7XZOPPFEDjzwQO6///6OKmWHKXAV2T3Uz6+n7qs6PL08uPPdhDaEaFrexNrr1hIPxUk/Kt0KZzeGiJRFtvm6Np8Ne5IdTw8PviE+K5jdFNBuus11YXOqy05kR4TiccrDYVwt4WxFJEJBMMjdhYWUhMOcmJlJWUs4O6umhnWhEIGWcHZ7Bxwk2+0YQKClc7aH202m04nPZiPJ4aCPx0Oey0VmyxxarwJaERGRn1dTA0uWWO2GYIWwa9bA889bM2lHjtwczpaWbts1DcMKZW02q/N2//2tQLZPH8jOhmHDrI5aaTfLKpZx/SfXM23pNABsho0LxlzAXw/8K92St2/z9PZ2661w/fXW8S23wF//mth6pGN0+cC1traWU089la+//pr6+nry8/MpKSlh/PjxvP/++/j9/o4qZYcpcBWRWHOsdXwBQPlb5VT+uxLfIB/ePl4rnF3RxMaHNwLgHeQlvCFMrGEb4xzDmi3rTHda4WwfD74BPlx5Lusjd/Ot5suK7DjTNKmORjGwZsFWtYSzDxQVsTEc5piMjC3C2TXBIEl2O8F4fLtHGwDYAZ/dbnXJtgSxHpsNj81Gb4+H3h4PWS4X6Q4H6U4naQ4HXptNG4SJiIhszdq11rBNw7DGCqxfb3XTvvaaNfogO9tqWYxsQ3NETo7VDdunj9WFm5Zmbfw1ZozVNauRQzstFo9xy6xbuOeLe2gINwDgcXi4Yp8r+OsBf8Xn8iW4ws1+/3urAdtmgw8+gMMPT3RF0t66fOC6yezZs1m4cCENDQ2MGTOGCV1oCzgFriKyLUzTJFobJVIawTfI+sdDtD5K0YNFVL1fRWBUAE9PD6ENIZpXN1P1vrWrp+EwMKPb/m3ZkebAlevCkerAkerA08+Dt493cyib58Kd58aerF3dRXZWMBYjapr47XZqolHWBYM8tnEjG0IhDk9PpywSoTISYWZNDd81NVkbdhkG5ZEI0R3855bLMFrDV1fLc7ib201/r7f1/hSHg8yW47TvhbWaRysiIru9eBzKy+Hdd+Gzz6yZs2CFtcuWwfLlv3wNr9fqjA2HrdtTT7U2Gxs4EJQJbDfTNJldOJvrPr6O2YWzAfA5fTx6zKNMHDUxwdVZTBPOPx/++U8re587F/r1S3RV0p52mcC1K1PgKiJtLR6O07i4kUhFhLQJaUQqIoQ2hCj6RxHVM6pJGpOEu5ubcHGYYGGQhvkN272GzWtrDWEdqQ4c6Q68fb14enm27JzNcmHYFcyK7Ky4aRI1TVw2G6ZpUhYO88jGjRSFQpzUsiFYRSTCuxUVfFlXR0+Ph6yW+bTV0Sjl29KN8wvsQLLDQXe3uzWIrY9GSXY4GOzzkel0kuJwWBuXtQS3KXY7yS3HPnXXiojIrq621gpdbTYrhF27Fl56yQpjU1OhsvLnO2STk8Hvhz33tGbGDh9ujT3Ize2oP0GXZZomT85/kj+894fW+04cfCL/OPIf9EzpmcDKLKGQ9Z/0iy+s/8wbNljNz7Jr6vKB6wMPPLD1IgwDj8dD//79OfDAA7F34hlnClxFJJGiDVGq/lNFtCZK1slZhIvDhEvCFN1fRM2sGgJjA7jzWsLZ9UGCq4Pbt4ABrhwXrvyWcDbFgTPbibef19r4K8eFM8eJK8eFPaCuWZH2EInHmVFdzYZQiEPT0qiJRqmORnmzooKPq6sZ4PMxxOejKhKhMhplWnk5AL09HmqjUWqi0e3eLGxrDKwO22yXq7WT1mEYROJxct1u+nk8rUGt0zDIcjpJczpJstsJtHwk2e347XZs+l4hIiJdUTRqhbCzZsEzz0B1NWRlWSHtz82QdTqtMQWnnWbtwjRypLWxl8vVcbV3EV8WfcnkGZP5rPAzovEoPqePvxzwF67a9ypc9sT+fS1damXopgkTJsCHHya0HGlHXT5w7dOnD+Xl5TQ1NZGWlgZAdXU1Pp+PQCBAWVkZffv25ZNPPqFHjx4dVdZ2UeAqIl1FtC5K+RvlRCoireFsqDjEhoc2UPd5HYE9AjjTnYSKQ4Q3homUb19HneEwcHVr6YzNsTpnnVlOq2u25b5NAa0jxaFwVqQdmKZJVUvI2sfjwWYYxE2TGdXVzKypIc/lYpDPR3VLR+3dhYU0xuMcl5FBMB6nNhplcWOjtXmY3Y7TMKiNRtnO/aF/kc9maw1gg/E4LpuNXm43mS4XAbsdOxAzTbJcLrq73a3nfj+4DXzvw2/XL3xERCTBamrgqaesUQV5eVBRAYsXW3Nktxaz2GxWN+x++8Ehh1hB7NixkJHR4aV3RkvKlvDH9//Ip+s+BaBHcg8+/N2HDMoclNC6Lr0UHnzQGhU8axYccEBCy5F20uUD15deeoknnniCp556in4tAzBWrVrFH/7wBy644AL2228/Tj/9dHJzc5k2bVpHlbVdFLiKyK7CjJsYNiuwiFRHKHm2hHBpmOxTswmXWOHsxsc20jCvAf9IP3afnXCZ1VEbb9rOOMbOFh2yjmQHzkwnnt4tIw2+F846052tdYlI+2uMxSgLh3HbbOS73ZimSWMsxj3r11MeiXByVhZR06QuGuXD6mo+rK6ml9vN6KQk6qJRalu6b+NAX4+HsGlSH41SF4u1SaftT9k033ZTCFsfjWI3DPp6vWS13G8DmmIxMpxO+nq9+FvCWp/N1tp9uynA9dvt+G02HJp9KyIiO6OgwBpLUFgIDgd8+y0sXGiNL9gav9/qfj3jDNhrL2ujri6woXh7ME2T377xW15a/BJgzXb9x5H/YNIekxL6i9ZJk+Dpp6191b79VqMFdkVdPnDt168fr7/+OqNHj97i/m+++YZTTjmFNWvW8Pnnn3PKKadQXFzcUWVtFwWuIrK7MWMmZtTE5rZCiHBFmA0PbSBSHiHnrBzCpWEipRE2PLaBxgWN+Ef5cSQ5CJda4WysPrZ9C9rYsjs2uWXmbB+v1VG76bFsJ85MJzaHwhGRRAvH49THYqQ6HNhbfiBa1dTEF3V1pDgcDPf7aYjFqI/FuLuwkIpIhFOzs3EYBg2xGF/U1vJRS0fumECA+liMhliMOXV1REyTTKeTUDxOQzuHuGDNvk1xOFqD2Oa49QumXm43OS4X/u91Aqc5HAzy+VoDW6dhtM7B/X6QG7Db8WomrojI7isehzfegP/8x+qGXb4cvvkGVq368bk2G2Rnw+DB8NvfwmGHQc+eVovlbsA0TW7/7HbeXv42czfOBeDkISfzxLFPkOFLTDdwXR2MGGFl6GeeCS+8kJAypB11+cDV5/Px6aefsueee25x/9y5cznooINoamqioKCA4cOH09Cw/ZvCdAQFriIiPy0eimPGTexeaxZ3pDJC0SNFRCuj5JxphbPh0nBr52xgjwD2ZDuR0gih4hCx2u0MZwFHujXKwJVtzZW1J9vx9Pbg7ubGle3CmeVsfdyR4VBAK9KFFIdC1Mdi9HS78djtmKbJksZGPq6pIWCzMSYpifpYjMZYjKeKiykOhzk8LQ2f3U5jLMaixkY+qq4mzeFgTFISjS3nzquvJ2iaZDgcREyThliszccobI2/ZbSC324nFI9jAt3dbvJaglyXzUZFJEKK3c6IQAC/zWaNT8Aay5DpdJLidLbev+laLnXlioh0Tf/7H0ydCs3N0NgIX30FGzf++Lxu3axhoiNGwFlnWTNhd/EANm7Guefze/jLx38hEo+Q5Epi2q+ncXj/wxNSz/vvwzHHWMdPPAHnn5+QMqSddPnA9ZhjjqGkpISnnnqKPfbYA7C6W88//3xyc3N57733ePfdd7nuuutYtGhRR5W1XRS4ioi0DTNmYsZMbC4rKIhURij+ZzGRigjZp2dvDmcf3Uj91/UE9gjgSHW0hrPRyuj2L2pYAa0ry4XNb8MesOPp5cHTy4Mz24krq6VzNss6VkArsmuqjkSoj8XIcjrxtgS5a4NBPq6uxmEY7J2cTGNLl+1zJSWsC4U4KDWVVIeDxliM5U1N/LuyEr/dzr4pKTS0BLlf1dfTEIuR7XRiAg2xWGuHbHtyGEZrmBs1TWKmSZ7bTTe3G7/NhsdmY2M4TJLdztikpNbxCfGWr811uch0Olvv3xTm+uz21o5lERHpIIsWwU03WbcpKVYnbOwHTQnZ2XDwwXDQQTBunDWGYBf9fj2rYBaH/OsQ4mYcA4ObD76Zvx741w5/14hpQp8+sG4dpKVZe6mlpHRoCdKOunzgWlJSwu9+9ztmzJiB0+kEIBqNcuihh/Lcc8+Rk5PDJ598QiQS4fDDE/Nbi1+iwFVEpOOZZks42xJ+RqoilDxfQrQySvZp2YTLrE2/Njy4gbov60jeOxlXrotweZjQhhDBVcEdW7gloHUkO7B5bbh7uPH29bZ2zG7qnlVAKyIAwViMxnjcCjntVqd/RTjMrJoaoqbJni1BbmMsxuvl5axsbmavpCSyXC4aYzHWBoO8WV6O0zA4OC2tNfT9sq6OqmiUXJcLh2HQ2DKeIdoB/4z3tAS5pmkSbdnYrIfb3RrOrguF8Nts7JmURLLDgd9uJxqPEwfyXS7yWjZB23T+pmOPRiyIiGybxkaYOxcmT4avv7ZGDoTDW57j8VjjB448Eg491EoEdyHPL3yeaz+6lg31GwA4Y/gZ/PP4f+J1eju0jlWrrP3O1q+H3/8ennmmQ5eXdtTlA9dNli1bxooVKwAYNGgQgwYldte57aHAVUSk8zNNs/UH+WhtlIp3K4g1xsg6KYtIeYRwWZiiB4qom11H0j5JeHp4CJeFCa0PUf9VvXURA3ZkWKQ9yY7NY8OV77I6Z7OcODOsebOtIW2ms/XWHtBu6yLyy8LxOAbgbBkf0BiL8VVdHY2xGKMDARrjcRpjMT6orOS7piaG+f3ku900xGJsCIV4rayMOHBYWhqNLfNw59bVsTEcJs/lwmuztd7f8MNOqnZgA/wtG5tFTJMMp7M1yA3Y7RQEg3htNsYEAqS3dN9GW0LffJeLbt8PcjeNV2jpznVqxIKI7KricWv8wPz5MHMmvPwyLF265Tk2G/TuDePHwyWXWB2wu8C/NU3T5Kn5T/HH9/9INB5lSOYQZkycQV5SXofW8fnncMAB1n+Kt9+G44/v0OWlnewygWtXpsBVRGTXFWuMUft5LbGGGBnHZRCtjBIuD7PhgQ3UzKoheZ9kPL08RMojNK9tpnp6tdUFm+YgWrUDIw4A7OBMc1obgmW5cGQ6sHvsuLq5cHdzbxHOqotWRNrD939JFY7HWdLQQG0s1rrZWWM8zqyaGhY1NNDX66WH201jPE5ZOMyLpaUETZOj09Ot7t14nK/r6igIhch1OkluGcPQEItRGd3B75PbyWUYuGw24qZJssNBt5YZuX67nYpIBI/NxmCfj/SWjlybYWAHMp1OMp1OkhwOkux2klo2QEtqCXRtu0BgISK7oMWLrfB19WqYPh2++27Lx3v2tFLB44+3RhC4XAkps618svYTjn3pWJoiTfidfmafM5vReaM7tIZrroG77gK/39pIKz29Q5eXdrBLBK5FRUW88847FBYWEv5BG/y9997bkaXsEAWuIiIC1gZhTcubiNZGST0glXg0TrQ6yoaHN1DziRXOevt6CZeHaV7VTOmzpWCDwKgAkYoIkfII8eCOzXZ0pDpaQ1gMcOW48Pb3bp5D+72Q1pmlLloRSbyYaVIQDFIXjdLP46GppZv2y7o6FjY2kuty0dvjoTEWoyYa5V+lpTTFYhyZnk5zS/fuNw0NLGtqItvlItvpbJ2du+GHb61tJ5vm4cZMkxSHgz5erxXK2u1UR6N4bTYG+XxktQTNXpsNn81GRsvnyXZ7a5irjc5EpN0sXAiXXmrdBoNWR+wmDgfsvz9cfrk1fsDtTliZO2PyR5O54393AOB3+nnh5Bc4YfAJHbZ+Y6M1tSESgcMOg//+t8OWlnbS5QPXGTNmcPzxx9O3b1+WLVvG8OHDKSgowDRNxowZw8cff9xRpewwBa4iIrK9zLhJpDxCpDqCf7C/9f6S50qonlFN0l5JePt5iZRHaFrRROHthRg2g4xjM4hURAiXhwkWBDFDO/aSbbgMnJlODLuBI92Bb5Bvq+MNWm8znNicCgNEpPMzTZNVzc3Ux2IM9noJmWZrkPtFXR15LhdD/P7W2bn3FxXRGI9zQkYGcazRDPPq65nf0ECuy0Uvj4eGWIz6aJTCUGhHJstsEwNIcThIawlgHYZBUyxGhtPJYJ+vNZgNx+NWl67bTfqmztuW8DbF4SDF4cCt8FZEfkpzM3z0kfXe92efhe+/0yAlxep6PekkOPZYaNlnp6uYVTCLWz+9lY/WfoSBwSPHPMKFe17YYetfeCE8/rj117Z0KfTv32FLSzvo8oHr3nvvzVFHHcXNN99MUlIS3377LdnZ2fz2t7/lyCOP5KKLLuqoUnaYAlcREWlvpmkSb4pj99tb76ueWU391/X4hvnw9m4JZ5c3sfb6tcSDcXJ/n9vaOVs/r55oZRTDaWBGduxl3uaxYU+y4+nraQ1nHSkOq4s21/WjTlp7krpoRaRri5vmFmMDvqqroy4aZWxSElHTpC4WY05tLR9VV5PtcjEmEKC+ZQOzxzdupCYa5VepqRiGQX00yvLmZlY1N7fOlq2PxWiO79g7G36OyzBIcTgwAQfQ2+Mh1+0mxW7HbbPRFI+T3RLkbgppU1rGJWw69tv1PVxkl7dkCTzwANjtVgC7cePmxzweOOccOPdcGDu2y8x8jcQiXPrBpTw27zEArt3vWqZMmNIha0ejcMwxVnfrCSfAW291yLLSTrp84JqUlMSCBQvo168faWlpzJ49m2HDhvHtt99ywgknUFBQ0FGl7DAFriIi0tk1LmmkaXkTvsE+PL09reHsmslriDXEyL8wvzWcrfpvFaF1IWs8ARCpjOzQZmHY2dw1m+XE7rdbgW1PD+7u7h930qqLVkR2cXHTpDEWI2aapLZ0jkXjcaaVl7MhFGJ8SgoGUN/Skft+ZSUZTifjU1Koj0apj8V4uayMmmiUMUlJreeWhsNUt/E8XBsQsNuJA0l2O/29XlJaOmlD8Tgem42+Xi/5LhdpTiepLd25aQ4HqS0f2qxMpAuJx2H2bGtTrUWLtnxs5Ehr3MDVV0NmZmLq2w6mabL/0/vzedHnANx92N1cte9VHbL2d9/BiBEQi8GHH8KECR2yrLSDLh+45ubm8sknnzBkyBCGDh3KHXfcwfHHH8+3337LfvvtR0NDQ0eVssMUuIqIyK4kXBYmVBTCmeXE08ODGTNpXt3M2hvWEqmK0O3Cbq3hbNnLZTQubsQ7wIs92W6NSaiIEG/aiVm0mU5sfhs2jw1PDw+evp4fbRTWOotWXbQispv6/sZmFeEwy5qacNlsDPR6qY3FqI1GeXLjRtaHQhyUmorHZqM2GuXbhgamV1eTbLczIhCgNhqlLhplaVMTEdPEBrRVz23AbscB2A2Dnh4PPdxu0lo2JmuOx8l1uRji87WGtGlOZ+ux12bT93eRRDBNmDcPli2D99+HN96AUMh6zDDg1FPhggvg0EM7dddrTXMNIx8byfq69QDce/i9XDH+ig5Z+9JL4cEHISkJKiu73GQGadHlA9cTTzyRY445hvPPP5+rrrqKt99+m9///ve88cYbpKWl8dFHH3VUKTtMgauIiOyuzLhJtCZqzYJNcQAQrYtS9I8iwqVh8s7Ja505W/JMCbWzawmMCuDu4SZSHiFcGqZ5RfMvrPLTDJeBI9mB4TZw57nx9PO0hrKOdAeunM3hrCPNgTPdic2rH+JFRH5ofTBIZSRCH48Hx/fC2TfKy/HYbByYmkptNEptLMbU4mKKQiHGJSfjsdmoaZlvuzYYbLPA1mUYpLWMOAjF46Q7nYwJBMhwOkl3OnEaBplOJz3cbrJdLjIcDjKcTnXVirS16mq44gr417+sMHaTYcNg0iRrcKnXm7j6fkY8HueGmTdw22e3AXDXhLu4er+r233dJUtg+HDr+IILrLmu0vV0+cB1zZo1NDQ0MHLkSBobG/nzn//M559/zoABA7j33nvp1atXR5WywxS4ioiIbLvvd2XFmmJUvldJpCpC1qlZrZ2zxU8VUz2jmsAeAXwDfETKI4Q2hqj5uAYAm8+2w1202MGRbIWxjjQHjjQHht3AmeXE3c2NI92BM80KaLc4TnNg99p/+foiIruhpliMwmCQODDQ66UmGqUmGuW50lJWNDWxV3Iyfrud6kiEJY2NvFtZidtmY1QgQHU0SnUkwrpQiOhO/ihqB3x2OwO9XjKcTjKcTmKmiddmY4DPR2+PpzWczXI6yXK58Nv1vV3kF9XVwWefWV2vzz4LjY3W/U4n/OlPcOWV0K1bYmvcCtM0uXnWzdw862YALtrzIh455pF2X/fUU+H11yE9HVautG6la+nygeuuQIGriIhI+4uH4zQsaCBSFSHjyAxiTTEiFRGKny6m6r9VBEYF8A/2Ey4PE1ofovRfpQD4BvusILc6ArGdq8FwG9jcNuwBaxatM9MKY21+a0MxV5YLR6oDR4r1YU+2bz5OsWP3awSCiMhP+a6xkQ2hEL09Hlw2G1WRCN80NPB8aSk2w+Dg1FQqIxEqIxHer6qiIhIhz+UiFI9THY3u0LhxAJ/NRrbLRXbL+35dhsEQv5/BPh/ZTieZLeFsrttNltOJS120srurqYHLL7eC100cDjjjDPjLX2DQoERV9pPGPTmOrzZ+BXTMeIFoFPbYAxYvtkYM/OMf7bqctIMuH7j27duXuXPnkpGRscX9NTU1jBkzhjVr1nRUKTtMgauIiEjnYsZNK2StjOAf4rfuM03KXi2jaroVzgZGBohWRwltCLHmmjXEm+PkXZBHtDZKtDpK48JGwiVhbB4b8XC8bd4ja8MKXZ0GjlQH7nw39hQrlMUAZ7rT6rxNdbTe70hxYE+y40i2bu1JdmwejUUQEQnH4xiA02YjZpqsaGri7YoKmuJx9k5KojIapTIS4YXSUlY3NzPc78dts1EZiVAcDlMWiezQuil2O9kuFzbAaRgM8vkY4ve3hrOZTid5Lhe5LhfpTic2fb+WXdXy5fDWW1bX66efWvcZhrVT1IMPdqrgtTHcyP7P7M+CkgXYDBvvnvEuRw84ul3XnDHD+quw2+Hzz2Hvvdt1OWljXT5wtdlslJSUkJ2dvcX9paWl9OzZk9Cm4cydmAJXERGRrs+Mmxi2zT8UNy5rpOm7Jjx9PARGBojVxwhtDLHqz6uIVkbpfkV3YvUxotVRKt6soO6LOnxDfHj7eYnWRYnWWKEtYL3HdSe7a7dgA5vLhiPDYXXUJllzbA2bgSPDgTvXbYWzyXYcSZuDWntSy+fJ3zsO2DHsCgNEZPdSF42yoL6e+liMwX4/ZS0B7DPFxXzX1MRwvx+PzUZZJMLa5mZWB4M7tI7TMMhxuXAbBi6bjaE+H8P8fvJcLrJdLnKcTnp4PNY56pyVruzrr+EPf4D5863PbTY480z46187TfBqmibnvXMeTy94moArwGfnfMbo3NHtuuaECVbwmp0NpaXtupS0sS4buL7zzjuAtWnWs88+S0pKSutjsViMGTNm8OGHH7J8+fL2LmWnKXAVERGReCiOGTWx+61ZgPFonPLXyonVxcg5Jwci1oZiZS+XUfFOBf6RflIPSCVWGyNaG2XNtWswIybZZ2YTD8WJ1cZoWtZEqChkbfRlN4g1tGVqu5nNZ41EMGwGNp8NV64LZ5rT6qb1WSMUHGmO1i7bH4W43+++dav7VkR2LdF4nNJIhPpolGyXqzWcnVpSwuKGBkYEAnhsNsojEVY2NbGgsXGHNg9LdzjIdblwGAZum43hfj/D/X66ud10c7no5naT73YrmJXOq6TEev/8kiWwdKl1n80Gv/kN3Hsv5OUltj4gEotw1AtHMWPtDJLdySz941K6Jbff7Nnp0+HII63jt9+G449vt6WkjXXZwNXW8iJhGAY/XM7pdNK7d2/uuecejj322G2+5qOPPsqjjz5KQUEBAMOGDeOGG27gqKOO2ur5U6dO5ZxzztniPrfbTXA7f3upwFVERETaghkzwUZrYNlc0EzTsiZcuS6SRidhxk1ijTFWX7WaSGWE/IvyMQyDWH2MincqqHijAu8QL2mHpBGrjxGrj1HyXAlEIbBHADNiEq2PEqmIEG9sixkJW2EDu8+OI31zQIsd7IHNM25/GNJuEeImf68L12/foutYRKSzi5km5eEwDbEYPT0eSsNhSsJhniwu5tuGBkb4/TgMg5JwmBXNzXzX1LTda2Q5nXRzu+nudmMDku12xiQlMawlnO3udpNs18xwSbB58+Bvf4OWZjtcLrj5ZrjiCnC7E1ra2uq19H+wP3EzTs/kniy5eAkBV6Dd1jvoIGviwtFHw7//3W7LSBvrsoHrJn369GHu3LlkZmbu9LXeffdd7HY7AwYMwDRNnn32We6++26++eYbhg0b9qPzp06dymWXXbZFF61hGOTk5GzXugpcRUREpLNqWt5EtC6Kf4Qfu8fqvq2fV0/Vf6tw93aTfli6Fc7WxVhxyQoipRG6XdoNu9dOtD5K9Yxqqt6rwtPbQ/K+ya1Bbu3ntZhhE0eGg3gw3j4BrmEFtZvGHthT7LhyXVZYm2zd/8MZt61BbvKW99l86rwVkc4lZppUtnTOpjqdlITDFIdC/LO4mEWNjQz3+zEMgw2hEKuamykOh7f52jYgqSWI7d4SwqY4HPTxeBjm89HL4yHgcLTfH05kk/POg3/+c/Pn/frBPfdYrZ4JfF2+5sNruO+L+4jEIxwz4BjePv1t7DZ7u6y1apU1VSEetyYvjB3bLstIG+vygWt7S09P5+6772bSpEk/emzq1Klcfvnl1NTU7NQaClxFRERkV2XGTWINMcyYiTPN2Xp/5b8riVREyDguA2e6EzNmUvVhFcVPFuPu6SbnjBxrxm19lNVXriZUFCLvgjwcKQ5i9TEavm2g9tNaHOkOfIN8ROui1pzcDaG2nXe7iQ1sbps18zatJZQN2K1ZuKkOnFnObQpu7Ul2a8SDwlsR6UBNsRjLGhspi0TIc7spCoXY0BLOrmpupp/XS3M8zoZQiOpodJuume5wkOdyETVNenk8HJ6eTk+3m14eDz3dbnJcLn2vk51nmtbGWmvXwt//DsXF1v377ANPPw1DhiSoLJP/Ff6Pw54/jGA0yJ/2/hMPHPVAu633u9/B88/DscfCu++22zLShrpk4PrAA9v+P/Gll166Q2vEYjFee+01zj77bL755huGDh36o3OmTp3KeeedR7du3YjH44wZM4bbb799q92w3xcKhbbYzGvDhg0MHTpUgauIiIjINopURWha0YTNYyNpdFLr/Rse3kDzumbyfp+HM9NJtC5K9UfVFN1bhLubm7zz81rD2fV/X0+kLELa4WnYk+zE6mMEC4M0L2vGcBrY/XaiddHtH6T4S+y0jj0wnAZ2rx1nlhNnhnPzhmTbENzakzXzVkTa3trmZt6sqKAmGmWg18uGcJj1wSAvlZVRFY0SsNloiP/yN0YDyHO5GOLz0dPjId/lItluZ6/kZAb7fOQqkJXt1dAAU6bAnXdCLAYOB9x+O1x5Jdjbp7v0l0xbOo1fv/ZrAB4/5nEu2POCdlln8WIYMcI6/uCDzXNdpfPqkoFrnz59tq0Qw2DNmjXbde1FixYxfvx4gsEggUCAF198kaOPPnqr586ZM4eVK1cycuRIamtr+fvf/86nn37KkiVLfvYv86abbuLmm2/+0f0KXEVEREQ6TrQ2SrQ2ijPTid1n/aAWXB+k+sNq7Ml2sk/NxjRN4k1x1kxeQ+OiRnJ/n4u7p5tYfYy6OXUU/r0QV7aL3N/nWh25dVEq/11JtCKKt78Xw2nNyI1Ut8/cW8NhYLgN7D5rXMKmGbY2rw1HisOahZvmsI5TrJB3a8c2lzbREZFf1hSL4TAMgvE4hcEgn9TU8HJZGSbQ2+NhXTBIYShE0fcajH6K12ajr8eDx2Yjw+nkwJQUxiQl0dfrpbfHo829ZOtME04+2ep63WTffWHqVBgwICElnfzKyby57E0cNgdL/riEgRkD22Wd3FwoLYVRo2DBgnZZQtpQlwxc21M4HKawsJDa2lqmTZvGU089xaxZs7ba4fpDkUiEIUOGcMYZZ3DLLbf85HnqcBURERHZdcSjcWyOzcFA/YJ6wiVhAqMCuPOsjT2aVzdT9EgRdo+dbhd3aw1n1964loZ5DWSfkY1vgDUaoWlZE6X/KsUWsJE+IZ1ofZRYXYzG7xqJN8Qx3AZmqG3/uW04DOxJdpyZzta5tqZp4kx14u7htkYppDiwp1gdtls7tvu1yY6IWObX1TG3vp5cl4u6WIx1LeHszJoa3DYboXj8Z988YABuwyDX7ebQ1FT6er3083rp6/HQz+sl3en8ma+W3cK8eVbqeMUVUF8PHg/ceCP83/9BB4f1/131X4544QgARueO5otJX+B2tP3GXq+8Aqefbo2uXbIkYdMUZBvtUoHrpmXb8h96EyZMoF+/fjz++OPbdP6vf/1rHA4HL7300javoRmuIiIiIrJJPBQntDGEGTbxDfK13l/570qaljeRdnga/qF+Yg0xGhY2sHbyWgyXQc/JPYnVWUFu0X1FNC5sJG1CGp7eHqJ1UUIbQ9TNrgM7uHJdxGpjxBracOCtweYgNsWBYTcw7AauXBfu7u7WgHbT4z/Vdfv98FpEdi2madIYi+G22VgXDLI6GOTOdetYHQwyyOejNBxmdXMzjb8wsiDFbmeY388gn4+ebjfd3G7GJycz0OfDpc7Y3cu6dXDOOfDJJ9bnY8bA669D794dWsb7K99n4psTqWyu5Ip9ruDeI+5tl3VOOslq7j3rLHjuuXZZQtrILhG4/utf/+Luu+9m5cqVAAwcOJCrr76a3/3udzt97UMOOYSePXsyderUXzw3FosxbNgwjj76aO69d9ufXApcRURERKQtmaZJPBTHMAxsbit8iNZHqf20FjNqknlCpnVezGTDoxuo/ayW1ENS8Q/zE6uN0bSyiXV/WwdA/oX5RGutLtvqmdWEi8K48l3Y3LbWsQxtuVGZPWC3xiCkOjBjJo5UB56+HpwZTqvTNtWBM83ZuoHZ92/tAXXZinR1pmkyt76etyoqCMbjpDgcrGluZnVzM5/X1fFzYYMN6OPxMMjnw2uzMczv58CUFEYGAmS5XB31R5COtmIFHHgglJVZIwfS0+Hll+Gwwzq0jHeXv8vxLx8PwL/P/DdHD9j6eMqdMW8e7Lmn1cS7YMHmua7S+XT5wPXee+/l+uuv55JLLmG//fYDYPbs2Tz88MPceuutXHHFFdt8rcmTJ3PUUUfRs2dP6uvrefHFF7nzzjuZPn06hx12GBMnTqRbt25MmTIFgL/97W/ss88+9O/fn5qaGu6++27eeust5s2bt00jCDZR4CoiIiIiXUFoY4hwSdjqWs233i4ZqYmw4eENRGuj5J2bZ3XZ1kYpea6Euv/VkbRXEr7BPmK1MUIlIcpfLgcgaa8kawOz2hiRighmtA1+hDCwwtc0K5Q1XAaGw8CV58LTy9Ma2G4KaLcIbtMd6q4V6cRM02RDKMS3DQ1ku1ysCQZZ3tTEWxUVLGhowGEYRH4mishyOunt8ZBqt7NfSgqHpqUxzO8nTeMJdg3BoNXlesMN8PXXViJ5883wl79Y78HvIJe8fwkPz30Yv9PPyj+tJC8pr83X2GMPK2wdM8YKYKVz6vKBa58+fbj55puZOHHiFvc/++yz3HTTTaxdu3abrzVp0iRmzJhBcXExKSkpjBw5kmuuuYbDWn4rcvDBB9O7d+/WbtcrrriCN954g5KSEtLS0hg7diy33nore+yxx3b9GRS4ioiIiMjuwIybRCoiROui+PpvHpdQPbOaui/q8A/14xvqI1oTJbQ+xJrJa4jVx+h2aTditTGi1VGqP66meUUzrnwX9iQ70eoo0eooZmTnfwQx3AaOFAfuHm6c6U4c6Q4wwJXjwtPDY824zXC0PubMcOJIU1ArkmjReJymWIyGeJzlTU0saGjgzsJCKqNRurvdFASDP/m1+S4XAbudQT4fx6Sns0dSEiP8frwJ2vVedlIwCH/8IzzzjPX5gQfCv/8NgUCHLP/Sopc4840zAdivx358es6n2Iy2fY3429+scbUAa9bANu4rLx2syweuHo+HxYsX079//y3uX7lyJSNGjCD4M99YOwsFriIiIiIi2yZaGyVcHsYesOPOtbpso01RNvxjA+HSMHmTWrpsa6KUvVxG9Yxq/MP9+Ef4iVZHiVRGqHynEgB3LzfRGqvLdmfYk+04052tx97+Xly5LiuYTXPgzHLizHS23rqyXNh8No0/EGlnpmliGAaNsRjfNTby8IYNfFRdTYrDQV0sxvrvbWb9fTagh9tNtsvFr1JTOTI9ndGBgLphuwrThOHDYelS6/Nhw6zBpz/IjdpnaZMzXj+Dt5a9RSgWYsqhU7h2/2vbdI1YDEaOtP54F14Ijz7appeXNtLlA9fhw4dz5plnct11121x/6233sorr7zCokWLOqqUHabAVURERESkY5gxk+ZVzUSqIySPS8YwDMyYSdlrZVS+X4lvsI+kUUlEqiKES8MU3FhAvDlO1ilZROujRCujNK9uJlod3ak6DIeBzWfD08tjhbNZTuxJdhxJDty93LhyXbiyXK1BrUYeiLS92miUmdXV3LV+PSXhMH29Xr5taKA8Etnq+b3cbpLsdvZKTuakzEzGJCWR73LplyedUXMz/OlPVndrSQmkpMC0aTBhQocs//Q3TzPpnUnYDTtfX/A1o3NHt+n1Z86EX/0KvF7YsAHS0tr08tIGunzg+vrrr3PaaacxYcKE1hmu//vf/5gxYwavvvoqJ510UkeVssMUuIqIiIiIdF6bfrzZFKo0LW+iYVED7m5uvAO8RKuihEvCrLlmDZGqiNVl2xAjUhWh5pMampY24ch0YPfYCZeHMUM79uOSPcWOK8cKYe1JdmxuG+6ebrx9vbhyXNZj2U7rNsOJYVcIJLK9TNOkOBzmbwUFfFJTQ47LxfpQ6CfHEuQ4nfT3ehnh93NiZib7pqSQ5HB0cNXykzZuhFNPhTlzwOGAf/0Lzjij3Zc1TZNfv/ZrXv/udUZkj2DBhQvadLSAacKoUbBoEdx1F1x9dZtdWtpIlw1cFy9ezPDhwwGYN28e9913H9999x0AQ4YM4c9//vN2z1JNFAWuIiIiIiK7pmiDFcbaPDY83T2Ypkm0NsrayWsJFYfInZhrhbMVESreqaD201o8vT248l1EyiPWzNuqHeimtWGNL8hxYU9uCWd7ufEP8uPMse53Zbus42wXNpe6Z0V+TnUkwqvl5Ty8YQPN8Thuw+C7pibiPzjPBgzz+xkTCNDf6+XEzEyG+v3Y1AWbOKEQ7L03LFxobaD11FNw7rntvuynBZ9y8LMHY2Jy3xH3cfk+l7fp9f/6V7jtNvD5oL7e2idMOo8uG7jabDb22msvzjvvPE4//XSSkpLae8l2o8BVRERERETA2ljMjJjY3LbWz0umlhBcFyTzpExitVY4Wz6tnMoPKvH28+Id4CVSZo1AaFratEPrOtIcOLOdOJIdGC4DTy8PvsE+a7RBngt3nhtXntVBq9EGIpbmWIz59fVctHIlK5qayHQ62RAO/+i8JLudvZOSGJeczLjkZA5KTSVFXbAd67PP4JBDINryC6yHHoKLL27XJZsjzfS4rweVzZUkuZJY+aeV5ARy2uz6ixfDiBHW8Usvwemnt9mlpQ102cD1s88+45lnnmHatGnE43FOPfVUJk2axAEHHNDeS7c5Ba4iIiIiIrKzzJhJ5XuVhDaGSD863dokrDRC2atlVL1fhae3B+8AL+GyMJHSCA0LGrZ/ERu4sq0Q1pHqwOa14e3vtcLZ7wWzrlx1zcruZVMMUhIO82VdHQ9u2MDMmhrshkFkKxHJCL+fw9LSODg1lQNSUkjVhlztr7AQ7rkHHnjA+vy222DyZKvrtZ0sLF3I2W+ezYLSBZw18iyeO+m5Nr3+YYfBRx/BSSfBG2+06aVlJ3XZwHWTxsZGXn31VaZOncpnn31G//79mTRpEmeffTa5ubkdVcZOUeAqIiIiIiIdyYyb1HxaQ7gkTOrBqdYc2rIwZa+UUfleJd4+XrwDvURKI4Q2hmiYv/3hrCPdgbubuzWctQfseAd48fb3bg5m813YPfZ2+BOKJF44Hqc+GmVDOMwXdXVMr6rijYqKH51nAGkOB4ekpvLbnBwOSE0lQwFs+zBNuOkm+NvfrM///Ge4++52DV3nbpjLuKfGYWIyY+IMDulzSJtde8kSGD7cGiewbh20Ua4nbaDLB67ft2rVKp555hmee+45SkpKOPLII3nnnXcSUcp2UeAqIiIiIiKdlRkzqf6omtDGEGmHpREpjxDeGKb0pVKq/lOFp5cHT08PoeIQ4eIwocLQdl3f5rXh7ee1Ztd2szb/cnd34+njwd3Njbu7G0eS3n4tXZ9pmqwNBvm8thabYTCrpoZZNTUsb27+0bnDfD76e72ckZ3NkRkZGkHQ1saNg6++so6vusoKXdvRJe9fwsNzHybbn03h5YW4He42u/bBB8OsWdZM11tuabPLyk7apQJXsDpeX3jhBSZPnkxNTQ2xWCxRpWwzBa4iIiIiIrIriEfjVLxZQXBNkLTDW8LZ4jBlr5ZR80kN7h5unFlOwsVhwhvDxIM/3HJo6+zJdmxeG84MJ4ExAbx9vLi7W2Fsayib7sDQxkTSBb1WVsazJSWkOBwsaGhgadOWs5jtwD7JyRyWns7haWnslZSEQzsk7Zz//heOP97aUAvgvvvg8svbbbmP137Mof86FICL97qYh45+qM2u/fjjcOGF4HRam2e52y7LlZ2wywSun376KU8//TSvv/46NpuN3/zmN0yaNIl99tmno0vZbgpcRURERERkd2CaZmsoGgvGWP/39TSvbCbr11lWd2xRiIo3Kmhc3Igjw4EZNYnVblsTjc1jw93djaubC8Nh4OntITAqgLevF3cvN56eHhzJ6hKUzq8sHGbKunU8WVyMwzCo/UEjmdMwmJCWxm+ysjgqI4MclytBlXZxlZXw5JPWHFeAl1+G005rl6VM02TcU+OYu3EubrubxX9cTP/0/m1y7ZISyMuzjm+7Da67rk0uKzupSweuGzduZOrUqUydOpVVq1ax7777MmnSJH7zm9/g9/s7ooQ2ocBVRERERETEYpom0SprJ3FnhpNoQ5SmFU0U3FBAqChE1qlZhDeECW0IUfdlHZGyyDZf23AZuPJdBEYE8PTy4O7pbh1f4OnlwZXjwrCpS1Y6B9M0aYrHKQ+H+bC6mrcrKvh3VdWPzhvu93NQSgpn5+YyNikJmzq9t51pwqWXwkMPWS2i06fDr37VLktFY1GOfvFoPlzzIYf1PYzpZ01vs678Y46B99+Hgw6CmTPb5JKyk7ps4HrUUUfx0UcfkZmZycSJEzn33HMZNGhQey/bLhS4ioiIiIiIbL9waZimlU04UhzY/XZCG0I0ftdIwU0FxGpjpE1II1QYIlgYbA1xf47hMqyxB+lO7AE7/uF+AqNbwtlebjw9PNjceiu3JEYkHufz2lo+qKrCZbPx78pK5jdsuaFdN5eLEzIzOSkzk4NSU3Fq9MAvKy+HAQOgthb8fvjf/2DUqHZZalXVKoY/MpxQLMT7Z77PUQOOapPrFhVB794Qi8HixTBsWJtcVnZClw1cjz/+eCZNmsSxxx6L3d61d7VU4CoiIiIiItK+yt8pp+r9Ktw9rUA1VBiicWkjlW9XWifYgG0YKWvz2fAP9ePp62ntjPX08+Dt68XT04PNpYBLOs7GUIgTFy3i64YGvDYbTfHN/xM7DYN9k5P5U/fuHJWejq+LZyftJh6HX/8a3n0XIhHr/flz5kCvXu2y3AXvXsCT858k259N8Z+LsRlt8z3jlFPgjTfg4outhl1JrC4buO5KFLiKiIiIiIh0PNM0iZRFCJeE8Q31Ed4YJrguyMbHNlLzWQ3efl5sHhuhdSGC64LEm38hkbVhjSjo7cFwGvgG+Ugel4y3vxdPHw+uXJc29pJ2UR+NYjMMZtXU8GZFBS+VltL4vfDVb7NxfGYmp2RmcmxmJm51vm6puRlWrYIzz7RaRAcPtjpd09PbfKlpS6bx62m/BuDp45/mnD3OaZPr/vvfcOyxYLdDRQWkprbJZWUHKXDtBBS4ioiIiIiIdG7hyjClU0sJFgZJPTiVYEGQ4JogZa+VESmNYDgNzMjP/yhsOA3syXZ8g3wE9rA29PL281qBbF8Pdq86EKVtfF1Xx31FRTTEYixsbKQgGGx9zGkY/Dori9/l5DAhLQ2HwtfNiopg/Hjr9ogjrMGobfz3Y5om+z+9P58Xfc6gjEEs/uNiHLad39CvuRkCAath98IL4dFH26BY2WEKXDsBBa4iIiIiIiJdU6wpRnBdEEe6A0wIrglSO7uWon8UEQ/HCQwP0LymmVBR6BdHFrh7uPH2twJYV54L/1A/viE+vP282P0KY2XHmKbJV/X13LpuHe9VVm7xWK7LxcmZmVyQl8eopKQEVdjJvPgi/Pa31vGUKXDttW2+RF2ojr7/6Etlc2Wbdrmeeiq8/ro1w3XRIlBDfeIocO0EFLiKiIiIiIjs2uLhOEUPFFHzaQ2BYQEAmtc007CggeYVzb/49a58F54+1qZd/pF+UvZNwTvA6pB1JO18d5zs+kzTZF59PbNqa1nb3Myr5eWURyKtjw/2+fhDXh5n5uSQ7XIlsNIE+/hjmDABTNN6f/7MmbD//m2+zD2f38NVH15Fz+SerPjTCtwO905fs6YGzj4bzjvPGi+gwDVxFLh2AgpcRUREREREdk/R2ii1X9YSKY/g7euleVUzzaua2fDwBqKVUWx+G/HGn2+NtSfbcWY7Sd4rmaQ9k/AN9uEb7MPTy4NhV+IiWxeJx3mquJhLVq7covnaYRgcl5HBJfn5/CotbfecO/zuu/DCC/DKK5CfDwsWQFZWmy7RFG4i/958akO1XLf/ddx26G1ten1JLAWunYACVxEREREREfm+TRt62ZPsxINxmlc1U/luJcXPFGPYDdz5bppXNROpiPz0RWzgzHaSun8q3kHe1iDWN8inrlhpVRWJMLOmhuJwmGdLSphbX9/6WC+3myt69ODsnBxSnc4EVpkADQ2w116wbFm7zXMd/dhovi39Fq/DS/nV5fhd/ja9viSOAtdOQIGriIiIiIiI7IhITYQ1/7eG+vn1JO+TTKQ0QtPyJpq+a8KM/vSP5s4sJ/7hfnyDfXgHePEN8eEf4sfd0717djRKq89qajhkwQKi37vPa7NxRnY2V/bowTD/bhQKLl4MY8dCOAy33AJ//WubXn5B8QIOf/5wypvKuf2Q25l8wOQ2vb4kjgLXTkCBq4iIiIiIiLSlxqWNlL1URjwSx5XjskLYZU3UfloLP/MTuz1gxzfMh3+YH09vD0l7JxEYHsCV71IQuxupCId5p6KCoGny6MaNLG5sbH3sgJQU/ta7Nwelpu76/09s3Ai9ekE0ag1E/fhjOPjgNl3ixUUv8ts3fkuqJ5U1l64hzZvWpteXxFDg2gkocBUREREREZH2ZpomjYsaqf+6Hu9AL8E1QZqWNVH+RjnNy5vBBvzEuFh7ih3/MD/evl68A7wk75tMYGQAV/ZuvLnSbsI0TT6uruaoRYuIfC/u2TMpiat79ODkzEwcbfxW+07ln/+E+++3ul1zc+HbbyE7u80uHzfjjH5sNIvKFnHlPldyzxH3tNm1JXEUuHYCClxFREREREQkUeLhOE0rmsAGBgaNS6xQdv1d660T7EBs61/rzHESGBXAlesi9aBUkvZOwjfIh825Cwdwu6nFDQ38o6gIh83G1JISgnErnc90OrmjTx9+n5eHfVfteG1qgr33hiVL4PTT4aWX2vTyD375IJf+51Jsho3yq8tJ96a36fWl4ylw7QQUuIqIiIiIiEhnEw/FaV7bjLePl6YVTTQubqTo/iLqv6rHkeEgWhXd6ngCw2VYIwn6evAP95NyQApJo5NwZuxmmy7twsrDYR7asIHb1q1rzeIH+3zc3Ls3p2ZlYdsVg9d586zQNR6H996DY45ps0sXVBfQ54E+AFyz3zXcMeGONru2JIYC105AgauIiIiIiIh0FfFQnHgkjmEYNC5upPL9StbfvR4zZmJz24jVb70d1t3DTWBsAG8fL2mHpZG0VxKuTI0k6Koi8Tj3rl/PfUVFhE2T6qi1zdZwv59bevfmhMzMXWvGq2nCoEGwciVkZEBBAQQCbXb5P73/Jx6a+xD5SfmsvWwtLrueG12ZAtdOQIGriIiIiIiIdHXRuij2gJ1gQZCGbxtY+5e1NH3XhDPHSaQ0stWvcfdyk7RnEv5hfpL3TSZ5z2R1wnYxpmlSH4txX1ER965fT13MCtwHe708N2QIe+5KOcctt8ANN1jHl11mzXZtI+FYmN7396a4oZjnTnqOs0ae1WbXlo6nwLUTUOAqIiIiIiIiu6J4yJrzGQ/GaVjQQPHUYkr/VYrNYyPetPUdujy9PSTtmUTSXkkk75tM0tgk7F57R5YtO2hNUxMjvv6appb5rgZwXl4et/fpQ6ZrF+jYjMXgX/+Cc88Fw4AvvrDGDLSRKZ9N4bqPr2N07mjmXzB/1+oQ3s0ocO0EFLiKiIiIiIjI7iIeiROpjGD32qn/pp76ufWs/ctazMhPRAk2SBqbRPI+ySSPtz48vTwKozqphmiUD6qqeKeykudLSwFIsdu5vW9f/pCfv2tsrDVxIjz3HIwYYc12dbZNV/aqylUMeWQI0XiUD8/6kAn9JrTJdaXjKXDtBBS4ioiIiIiIyO4sHorTsKgBbx8vDQsbqJ9bT/FTxTSvbN7q+Ta/jdSDUkk9KJXkfZJJ2jMJu09dsJ3NZzU1/GHFCr5ragJghN/P4wMHMj4lJcGV7aSKCmuea1UVXH013HVXm1w2HAuTdmcaTZEm9u62N1+e92WbXFc6ngLXTkCBq4iIiIiIiMiWovVR6r6qw4yYRGui1M2po+bTGhoXNP74ZLu1KVf64elknpBJyn4pOFIcHV+0/MjrZWWcvnQpJhDDGjNwTc+e3Ny7Ny6bLcHV7YRRo2DhQrDZ4LvvYODANrnsQ189xKUfXIqJyfJLljMwo22uKx1LgWsnoMBVRERERERE5JdFa6OUvVZG/Vf1eAd6qfuijro5dYQ3hrc80QaBUQF8Q31knZxF6kGp2owrgZY1NlIZifBEcTH/ahkzMMLv56WhQxnm9ye4uh20YgXsuy9UVsLBB8PHH1tzXdvACS+fwDvL3+GiPS/ikWMeaZNrSsdS4NoJKHAVERERERER2TGmaVLwtwLKXyvH289L09Immlf9eBSBf7iflINSSD3QGkXgytkFNnHqgt4oL+fsZctoiMVwGQZ39O3LZd27Y+uKs13XroXhw6GpCV56CU4/vU0uO6tgFgc/ezBeh5f1V6wnw5fRJteVjqPAtRNQ4CoiIiIiIiLSdkIbQqybso7ix4ux+WzE6mI/Osc7wEvGsRmkHZZG6oGp2P2aAdsRovE4g7/6itXBYOt9h6SmMnXwYHp4PAmsbAfdcgvccAP07WuNFnDtfJAfjUXp9Y9ebKzfyLX7X8uUQ6e0QaHSkdojcO2yAzgeffRRRo4cSXJyMsnJyYwfP54PPvhgm7725ZdfxjAMTjzxxPYtUkRERERERER+lrubm4EPDeSA5gMYt3oc+5buy7Bpw8i7IK/1nOaVzRTdV8SioxcxO202Xw35itWTV1M3tw4zpj6y9uKw2fhy7Fhu7tWLRwYMwGez8XFNDXt8/TWzamoSXd72u+wy8PthzRq44442uaTD7sDn9AHw8FcPE4qG2uS60rV12Q7Xd999F7vdzoABAzBNk2effZa7776bb775hmHDhv3k1xUUFLD//vvTt29f0tPTeeutt7ZrXXW4ioiIiIiIiLQ/M27SsLCBmpk1uPPcVH1YRfWH1YQKtwy0HOkOUg9JJfXAVDKOzcDbx5ugind9K5uaOGPpUuY1NOAwDB4ZMIDz8/MTXdb2GTIEli2zgtfiYkhK2ulLvr/ifc568yyqg9U8e+KzTBw1sQ0KlY6ikQK/ID09nbvvvptJkyZt9fFYLMaBBx7Iueeey2effUZNTY0CVxEREREREZEuwjRN1ly3htIXSvF099C4pPFHIwi8/a3xAxnHZpByQAo2V5d9c2+ndPPatdy0bl3r55d268Y9/frhsHWRv+cvv4QTT4SSErjpJrjxxja57J2z7+TaGdcyOnc08y+Yj9EV59zupjRS4CfEYjFefvllGhsbGT9+/E+e97e//Y3s7OyfDGS3JhQKUVdX1/pRX1/fFiWLiIiIiIiIyHYyDIN+U/qxb+G+jPl8DPtV7sce/9uDjONaNioyoHlVM0X3F/HthG+ZnTKbbw74ho1PbSRcFk5s8buIP+Tns09SEqdmZQHwwIYNHL1oEdWRSIIr20bjxsEDD1jHf/87lJa2yWXPH3s+brubBSUL+Kbkmza5pnRdXTpwXbRoEYFAALfbzYUXXsibb77J0KFDt3ru7Nmz+ec//8mTTz65XWtMmTKFlJSU1o+fur6IiIiIiIiIdCybw0bKvikMf3s4e6/Ym9GzRjPsjWHknpuLM9tJPBindnYtK85fwee5nzNvn3kU3FJA/YJ6dqE3/HaoXLeb2WPG8NqwYbwxbBg+m40Pq6sZN38+K5qaEl3etjn1VNhrL2hosDbRagPp3nTG5o0F4O+f/71NrildV5ceKRAOhyksLKS2tpZp06bx1FNPMWvWrB+FovX19YwcOZJHHnmEo446CoDf//732zRSIBQKEQptng+zYcMGhg4dqpECIiIiIiIiIp1YqDTEigtXUP9VPa5cFw3zG7Z43JHhIOvULDKPzyTt0DRs7i7dk5Ywc2prOWjBAiKmSabTySejRjE8EEh0Wb/sH/+Ayy8Hw4CVK6Ffv52+5H7/3I/Piz7H4/BQc00Nbod75+uUdqcZrr9gwoQJ9OvXj8cff3yL+xcsWMAee+yB3W5vvS8ejwNgs9lYvnw5/bbxiaUZriIiIiIiIiJdT2hDiMr3K1l91eofzX21J9tJHpdM/kX5pB+Vjt1j/4mryA99UVvLoQsWEAaiLaHrx6NGMaKzh65ffWWNFwA45RSYNm2nL/nh6g859dVTqQvX8eqpr/LrYb/e6WtK+9MM118Qj8e36EbdZPDgwSxatIgFCxa0fhx//PH86le/YsGCBfTo0SMB1YqIiIiIiIhIR3F3c5N/fj57L9ub/o/0Z+irQ8n/Yz6ufBexuhjVH1az5OQlfJ71OUvPXEr5G+XEmmK/fOHd3D4pKcwZO5Z3hw9nz6QkKiIRfrVgAQsbGn75ixNp773h1lutDtfXX4d583b6kof1O4xL9r4EgGcWPLPT15Ouq8t2uE6ePJmjjjqKnj17Ul9fz4svvsidd97J9OnTOeyww5g4cSLdunVjypQpW/36bR0p8EPqcBURERERERHZdZhxk9VXr2bDIxuwOW3E6jeHrIbDIHkfq/M14/gMHAFHAivt/GoiEQ5fuJC59fWk2O3M2mMPRnX2Ttff/Q6efx4mTIAPP9zpy62sXMnAhwZiM2wUXl5It+RubVCktCd1uH5PWVkZEydOZNCgQRx66KHMnTu3NWwFKCwspLi4OMFVioiIiIiIiEhnZtgM+t/TnwPqD2Cfon0Y88UYelzVA1eeCzNqUju7lu9++x2fZ33O4pMXU/ZKmTpff0Kq08lTgwbhNAxqYzEO/OYbFtTXJ7qsn3fLLeBywUcfwWef7fTlBmQMYHTOaOJmnEfmPtIGBUpX1GU7XBNFHa4iIiIiIiIiu77GlY0UXF9A86pmojVRgquDrY8ZToOMEzLIvyCftEPSMOxGAivtXBpjMY5ftIg5dXU0x+OkOxx8NGoUeyQlJbq0rYtGYdAgWLMGfvUr+Pjjnb5kvwf6saZ6DVm+LEqvKsUw9P9HZ6YOVxERERERERGRDuAf4GfYy8PY8+s9GbdyHHsu2JNul1lvDzcjJhXTKlh4+ELmdJ/DqitXUb+gk3dydhC/3c4HI0eycM892Sc5mapolKMWLmR9MPjLX5wIDgf06mUdf/IJfPfdTl/y6vFXYzfslDeVM6dozk5fT7oeBa4iIiIiIiIiIj/DMAwCowL0u6sfQ98YSvaZ2eRflI8j3UG4JEzRfUXM22MeXw76kqIHiohURhJdckK5bDb6+3xMHzmSEX4/pZEIh377LY2xTjqK4cEH4ZBDrON77tnpy12414WcNfIsAKYumLrT15OuRyMFtpNGCoiIiIiIiIgIQDwcp/L9SpaevhQztDleMVwGGcdlkDcpj/TD03frkQPPlZQwcdkyAI5MT+ffI0Zg64xvsf/f/2D//a15rgUFkJe3U5ebVTCLg589mCRXEiVXleBz+tqkTGl7GikgIiIiIiIiItJJ2Fw2sk7MYuxXY+l2eTf63d+PwJgAZtik4vUKFh29iP/l/I+CWwsIFYcSXW5CnJKVRT+PBxvwn6oqrl+7NtElbd1++8H48RAOw/337/TlDux1IL1SelEfruelxS/tfH3SpShwFRERERERERHZCYGRAQbcN4Ael/Vgz3l7sueCPXHluwCIVkYpuL6AL3p+weJfL6bqoyrM+O7zZmOf3c7csWN5ZvBgAG4vLOT5kpIEV/UTBgywbh96COp3fiZvQ7gBgHvn3LvT15KuRYGriIiIiIiIiEgbCowKsNfCvej7974MeGQAyfslY0ZbNto6bCGf539O0T+KiNZHE11qh0hzOpmYm8u1PXsCMGn5cubU1ia4qq3o18+6bWqCf/5zpy5lGAa/G/k7AJaWL6WgpmAni5OuRIGriIiIiIiIiEgbc2Y46fnnnnS7qBtjZo9hz4V74h3gBSBSGmHV5auY030Oq65aRXBdMMHVdoybe/Wiv8dD2DQ5dtEi1gU72Z/7j3+Eiy6yju+7DyI7t/nZ3w//O4f2ORSAZxc8u7PVSReiwFVEREREREREpJ0FRgQY88UY+tzRh55/6Yl3oJdYXYyie4r4os8XLDxqIbVzOmHXZxty2Gz091mbR1VFo5y2ZAnReDzBVX1PZibccw9kZUFhIbz22k5dzm6zc87ocwB4buFzaN/63YcCVxERERERERGRDuBMd9Lrml70vbUve3+3NyPeG4G7lxtMqPpPFd/s+w3fHPgNVR9W7ZLhnM0weGnIEJ4aOJAUu50v6+uZUliY6LK25PXCn/5kHd95J+zkf4cTBp+Ax+FhdfVqFpYubIMCpStQ4CoiIiIiIiIi0sEMm0HGMRkMfXEoKQelkDohFcNlUPtZLQsPX8jXo76m4p2KXS54TXU6mZSfz8MDBwJwc0EBc+vqElzVDwwdCoYBCxfCjBk7dSmvw4uBAcDDcx9ui+qkC1DgKiIiIiIiIiKSICn7prDHzD0Y/eFo9lmzD90v747hMmhc1MjiExbz9eivKXutDDO+awWvZ2Znc1JGBjHgjKVLaYrFEl3SZsnJmztb77xzpy5lt9kZnj0cgOmrp+9sZdJFKHAVEREREREREekE3N3c9L+vP7nn5AJYwevCRpb+Zinzxs6j6r+7zqgBwzBobJnfujoY5No1axJc0fdMmAC33QY2G3z0ESxbtlOXe+HkF3DYHBTWFrKickUbFSmdmQJXEREREREREZFOZNBjgxj7zVjGrRpHrxt7YU+207CggYVHLGTBrxZQP68+0SW2ibv69aOn2w3Agxs28N+qqgRX1MIw4Lrr4Oijrc+ffnqnLjcgYwCH9jkUgNeXvr6z1UkXoMBVRERERERERKSTSRqdhKeHhz439WGfNftYm2sBtbNqmbfnPJacvoTm1c0JrnLnjAoEWLvPPlzSrRsA5yxbRlUkkuCqvmfSJOv22WdhJ+s6ZcgpAExbOm1nq5IuQIGriIiIiIiIiEgn5kh3MODBAXgHe8k8MRMMKH+lnK+GfMXqa1cTa+pE80+3k80wuLNvXwZ5vWwMh7loxYrOMzahb19wu6GsDN57b6cuNSJ7BADzS+ZTUF3QBsVJZ6bAVURERERERESkEzMMg8zjMtl76d4Mf3M4e36zJ+lHpmNGTNbfuZ4vB35JxXsViS5zh/nsdibl5QHwank5r5SVJbiiFi4XhELW8SOP7NSlRuSMwNYSwz0x/4mdrUw6OQWuIiIiIiIiIiJdgGEYAARGBRj2+jBsfivWCW8Is/i4xSw+eTHB9cFElrjDaqPR1uMrV6+m/nufJ8zgwXDttdbxxx/Dhg07fCm/y8/5Y88H4LPCz9qiOunEFLiKiIiIiIiIiHQxdp+dvb7dix7X9qD7Vd0xHAYVb1bw1eCvKPx7IWask7wtfxtd37s3d/TtS1+Ph+JwmNsLCxNdkmXKFDjgAIjHYerUnbrUXw74CwD/K/wfJQ0lbVCcdFYKXEVEREREREREuiBvPy/9pvSj/939GTt/LMn7JhNvirPm6jXM329+l+p2ddtsXNOzJ/f17w/AvevXs6qpKcFVtdi0edbTT1vB6w7qkdKDvbvtjYnJW8veapvapFNS4CoiIiIiIiIi0sUFRgToe2dfDIcBBtR/Wc/XI7+mbFonmYe6jY7LyODwtDTCpsmfV69OdDmW/fazNs9aswZmztypS43rNg6Aez6/pw0Kk85KgauIiIiIiIiIyC4gdf9U9l62N0NfHUrSXklEa6Is/fVSlp+/nFhjLNHlbRPDMDBajt+prOTDqqqE1gNAY+PmzbMefninLtU3rS8Aq6pXUdlUubOVSSelwFVEREREREREZBfh7ecl+9Rs9vjfHvS8ricYUPxUMXNHz6V+fn2iy9smv0pLaw2sLlu1ishOvI2/TYwaBaedZh3/+99QXb3DlzpvzHlkejMBeGf5O21RnXRCClxFRERERERERHYxNqeN3jf3xpXnAiC4Ksj88fMpeb7zb9Z0effuzB0zhkynk++amnhk48ZElwQvvQQjR1qdri+8sMOXCbgCXLL3JQC8uezNtqpOOhkFriIiIiIiIiIiuyCbw8aId0eQemgq6cekY4ZNlv1uGQU3F2CaZqLL+0lum40xycnc1qcPADeuXUt5OJzYogxj8+ZZTz0FO/H3d8rQUwD47+r/Uh/qGl3Hsn0UuIqIiIiIiIiI7KKSxiQx+qPRjHhnBD2u6QFAwU0FLJu4jHgowW/V/wWT8vIY6vNRG4tx/dq1iS4HTjoJHA749luYP3+HLzMsaxi9U3sTioV4Yt4TbVigdBYKXEVEREREREREdnGGzaDfHf3InZQLQOnzpXx72LdEKiMJruynNcRiFASDADxRXMyC+gR3g27YANGodfzIIzt8GcMwcNmsUQ9PffNUW1QmnYwCVxERERERERGR3YRhN6xbl0HtZ7XMHz+fplVNCa5q61IcDv6Qn0+m04kJ3FBQkNiCxo2D8eOt42nTrHmuO+jK8VdiN+z0T+/fqcc7yI4xTP1X3S5FRUX06NGD2tpakpOTE12OiIiIiIiIiMg2M+MmZS+X4R/hZ9FxiwitC+HMdDLqk1EEhgcSXd6PhOJxCpqbGTp3LnHgm7FjGZ2UlLiC4nHo2dPqdn3nHTjuuB26jGma1ARrSPOmtXGBsr3q6upISUlh/fr1dO/evU2uqQ5XEREREREREZHdhGEzyDkzh8CIAGO+GENgbIBIRYRvJ3xL04rO1+nqttkY5PdzWnY2ALeuW5fYgmw2OMXa9Ipp03b4MoZhKGzdhSlwFRERERERERHZDblz3aTsnwJApDTCt4d+S3NBc4Kr2rqre1gbfr1eUcGSxsbEFrMpcH399Z0aKyC7LgWuIiIiIiIiIiK7KXc3NwDOHCehohDfHvItoQ2dL0Rc0bw5CL5x7doEVoI1TgCgsRE+/DCxtUinpMBVRERERERERGQ31fPqnoydP5Y95++Jp5+H4NogCw5dQLg0nOjStnBqVhbjW2a3vllRwYqmBI4/OOYY8Hqt41deSVwd0ml12cD10UcfZeTIkSQnJ5OcnMz48eP54IMPfvL8N954gz333JPU1FT8fj+jR4/mueee68CKRUREREREREQ6n6Q9knDnuxk9YzTuHm6alzfz7WHfEqmKJLq0VnbD4POxYzkuI4M4cHsiZ7kmJ8N771nH770H4c4VTkviddnAtXv37txxxx3MmzePr7/+mkMOOYQTTjiBJUuWbPX89PR0/vKXvzBnzhwWLlzIOeecwznnnMP06dM7uHIRERERERERkc7HmenE3dMNBjQuamThkQuJNccSXdYWru/VC4DnS0tZ05zAebMHHQQ5OVBTAx9/nLg6pFPqsoHrcccdx9FHH82AAQMYOHAgt912G4FAgC+++GKr5x988MGcdNJJDBkyhH79+nHZZZcxcuRIZs+e3cGVi4iIiIiIiIh0PobDwO6zY7gN7Ml26ufWs/KPKzFNM9GltdorOZm9kpKIAdcncpar3b5586xnn01cHdIpddnA9ftisRgvv/wyjY2NjB8//hfPN02TGTNmsHz5cg488MCfPTcUClFXV9f6UV9f31Zli4iIiIiIiIh0Gja3jWFvDGPM/8Yw/I3hYIOSqSUUP1Gc6NJamaZJOB4H4OWyMtYHg4krZtMc2bfegkjnGb8gidelA9dFixYRCARwu91ceOGFvPnmmwwdOvQnz6+trSUQCOByuTjmmGN48MEHOeyww352jSlTppCSktL68XPXFxERERERERHpyhwBB0ljkkg7NI2+t/cFYOWfVlL3ZV2CK7MYhsFDAwaQ73IRB+5avz5xxRx1lHUbDMInnySuDul0unTgOmjQIBYsWMCXX37JRRddxNlnn83SpUt/8vykpCQWLFjA3Llzue2227jyyiuZOXPmz64xefJkamtrWz9+7voiIiIiIiIiIruKjJMzsAfsmBGTxacsJlzWOTaH2j81leeGDAHgyY0bKQ6FElPI8cfDb39rHb/2WmJqkE7JMDvTII6dNGHCBPr168fjjz++Teefd955rF+/frs2zioqKqJHjx7U1taSnJy8o6WKiIiIiIiIiHRqNbNqWHDoAgzDwIyapP4qlZH/HYnNkfj+PdM02f+bb/i8ro7revbktr59E1PIjBkwYQJkZEBxMTidialDdlhdXR0pKSmsX7+e7t27t8k1E/8MaUPxeJzQdvxWY3vPFxERERERERHZXaQelMqw14Yx8qOR2Pw2aj6pYe11Cdyo6nsMw+CM7GwA7i8qap3r2uEOOggyM6GyUmMFpFWXDVwnT57Mp59+SkFBAYsWLWLy5MnMnDmT37a0ck+cOJHJkye3nj9lyhQ+/PBD1qxZw3fffcc999zDc889x1lnnZWoP4KIiIiIiIiISKeWdVIWaQelMfiZwQCsv3s9ZdPKElyVZaDPB0BTPM5LZQmqyeGAbt2s44ceSkwN0uk4El3AjiorK2PixIkUFxeTkpLCyJEjmT59eusmWIWFhdhsm/PkxsZG/vjHP1JUVITX62Xw4ME8//zznHbaaYn6I4iIiIiIiIiIdAnZv86m/PRyyl8uZ/m5y0kZn4K7mzuhNU1IS2OvQIC5DQ08XVzM2bm5iSmkpdOWjz6CaNQKYWW3tkvNcO0ImuEqIiIiIiIiIrsbM2Yyd9RcmpY0AZBxQgbD3xyOYRgJrWtDKESvOXOIAYv23JPhgUDHFzFvHhxyCNTVWTNdDzmk42uQHaYZriIiIiIiIiIi0uEMu8HwN4aTMzEHw2FQ+XYl5a+XJ7osurndnJCZCcBjGzcmpoixY+E3v7GOX3stMTVIp6LAVUREREREREREfpFvoI8hzw6h5+SeAKy8ZCWRqkiCq4Lf5eQA8FRxMQ3RaGKKOPVU6/bNNyFRG3hJp9HlA9eHH36Y3r174/F4GDduHF999dXPnl9TU8PFF19MXl4ebrebgQMH8v7773dQtSIiIiIiIiIiXVuvv/TCN8RHpDTC6j+vTnQ5OFvGGoRMk6klJYkpYp99wOWC0lL45pvE1CCdRpcOXF955RWuvPJKbrzxRubPn8+oUaM44ogjKPuJnenC4TCHHXYYBQUFTJs2jeXLl/Pkk0/SbdNuciIiIiIiIiIi8rMMl4GnrweAkqklVH1UldB6jszIYIjPB8ATxcUkZLsirxdiMev4mWc6fn3pVLp04Hrvvfdy/vnnc8455zB06FAee+wxfD4fTz/99FbPf/rpp6mqquKtt95iv/32o3fv3hx00EGMGjWqgysXEREREREREemaDMMgaWxS6+cr/rCCWFMsYfXYDYP/7bEHXpuNRY2NzKmr6/giXC44+mjreN68jl9fOpUuG7iGw2HmzZvHhAkTWu+z2WxMmDCBOXPmbPVr3nnnHcaPH8/FF19MTk4Ow4cP5/bbbycW++lvCqFQiLq6utaP+vr6Nv+ziIiIiIiIiIh0Jb2u68XoWaNx93ATXBNk7Q1rE1pPmtPJGdnZADyyYUNiivjHP6zbuXOhtjYxNUin0GUD14qKCmKxGDktg5E3ycnJoeQn5nWsWbOGadOmEYvFeP/997n++uu55557uPXWW39ynSlTppCSktL6MXTo0Db9c4iIiIiIiIiIdDU2t43UA1MZ+NhAAIruK6JubgI6S7/nD/n5ALxSXk55ONzxBfTpA4MGWaMFZszo+PWl0+iygeuOiMfjZGdn88QTTzB27FhOO+00/vKXv/DYY4/95NdMnjyZ2tra1o+lS5d2YMUiIiIiIiIiIp1XxtEZZJ6cCXFYfsFyzHgC5qe2KGkJWaOmyZPFxYkp4tBDrdsXX0zM+tIpdNnANTMzE7vdTmlp6Rb3l5aWkpubu9WvycvLY+DAgdjt9tb7hgwZQklJCeGf+M2H2+0mOTm59SMpKWmr54mIiIiIiIiI7G5M06TpuyYAGhc0Uvbq1jcy7whHpaeT73IB8PjGjcQSsXnWmjXW7fTpkIj1pVPosoGry+Vi7NixzPhei3Y8HmfGjBmMHz9+q1+z3377sWrVKuLxeOt9K1asIC8vD1fLE1JERERERERERLaNYRj0uaUPru5WrrL2r2uJh+O/8FXtw2mzsXLvvUlzOCgMhfiwqqrji/jtb8EwoKEB9C7p3VaXDVwBrrzySp588kmeffZZvvvuOy666CIaGxs555xzAJg4cSKTJ09uPf+iiy6iqqqKyy67jBUrVvDvf/+b22+/nYsvvjhRfwQRERERERERkS4t8+RM9l66N84cJ8HVQYr/maC38wM+h4OzWvb7eaEsAd22Z54JRxxhHf/nPx2/vnQKXTpwPe200/j73//ODTfcwOjRo1mwYAH/+c9/WjfSKiwspPh7Mzt69OjB9OnTmTt3LiNHjuTSSy/lsssu49prr03UH0FEREREREREpEszDANHkoPe1/cGYN3f1hFrjCWsnjOzswF4vbycplgH12GzwZFHWscffNCxa0unYZimBkpsj6KiInr06EFtbS3JycmJLkdEREREREREpFOINkb5ss+XRMoj9LmtD72u65WQOl4rLeU3330HwCtDh/KblgC2wyxfDoMHg8sFlZUQCHTs+rJd6urqSElJYf369XTv3r1NrtmlO1xFRERERERERKRzaFzYSKQ8AkDhlEIilZGE1HFIejqbtkt/7gebrXcIhwOcTgiH4eOPO359STgFriIiIiIiIiIistNSxqeQdWYWrjwXsYYYhXcUJqSODKeT2XvsAcD0qiqqIx0c/PboAZveUP7KKx27tnQKClxFRERERERERKRNDHthGIOeGgRA0YNFBIuCCaljn5QURvj9REyTNyoqOnZxlwtuucU6/uKLzeGr7DYUuIqIiIiIiIiISJtJPyqdlANTMEMmBTcVJKyOM1pmt/6rpKTjF7/4YmuswJo1sGpVx68vCaXAVURERERERERE2oxhGOT8LgeAkmdKaFzWmJA6GmMxAD6traU4FOrYxZOS4IADrOP//Kdj15aEU+AqIiIiIiIiIiJtquqDKusgDgU3FCSkhiF+f+vxK2VlHV9Afr51++KLHb+2JJQCVxERERERERERaVO9b+5N+vHpAJS/Xk7zmuYOr+GEjAz+3L07AC8lInCtr7duv/4agomZZSuJocBVRERERERERETaVGB4gJFvjyTt8DSIQ9EDRR1fg8PB1T17YgO+qq9nVVNTxxZw8cXWaIFoFD79tGPXloRS4CoiIiIiIiIiIu2ix5U9ACj5ZwmRmkiHr5/jcjEhLQ2AFzu6y/Www+A3v7GONcd1t6LAVURERERERERE2oVvuA9HhoNYQ4zip4oTUkOawwHAM8XFmKbZsYsfeaR1+9//duy6klAKXEVEREREREREpF00L28mWhkFoOj+IuLReIfXEG0JWQtCIb5taOjYxQ84wLpdsgQqKjp2bUkYBa4iIiIiIiIiItIuUn+VSu65udhT7IQ3hKl4veNDxz/36MGeSUlAAjbPWrp08/Fnn3Xs2pIwClxFRERERERERKRdGIbB4H8OpscV1izX9fes7/C39Y9PSeHanj0BK3CNd+T6++wDXq91PGNGx60rCaXAVURERERERERE2lX+RfkYboP6ufXUfV7X4esfnZ5Ost3O+lCIOXUduL7XC089ZR1/8UXHrSsJpcBVRERERERERETalSPFQfLeyQCsv3d9h69vNwyG+/0AvNPRs1QPPNC6/eYb6MiwVxJGgauIiIiIiIiIiLSrWGOMurlW2FjxZgXNq5s7vIZNG2a9Vl7esQt37w59+0I8Dp9/3rFrS0IocBURERERERERkXblTHfS67peeAd5wYSiB4o6dH2XzcbF+fkYwNpgkILmDgx8o1FoCXv54IOOW1cSRoGriIiIiIiIiIi0u97X92bAgwMAKP5nMZGaSIeuf2f//hyQkgLAe5WVHbewwwFut3X84Ycdt64kTJcNXB999FFGjhxJcnIyycnJjB8/ng9+5rcETz75JAcccABpaWmkpaUxYcIEvvrqqw6sWERERERERERk95Y2IQ3/cD/xxjjFTxR3+PrHZmQAHRy4Atx/v3W7ejV0ZHetJESXDVy7d+/OHXfcwbx58/j666855JBDOOGEE1iyZMlWz585cyZnnHEGn3zyCXPmzKFHjx4cfvjhbNiwoYMrFxERERERERHZfWUca4WeGx/biGmaHbr2psD145oaGqLRjlv4pJMgPx/CYVAD4C6vywauxx13HEcffTQDBgxg4MCB3HbbbQQCAb744outnv/CCy/wxz/+kdGjRzN48GCeeuop4vE4M2bM6ODKRURERERERER2X1X/rQIguDZI3ed1Hbr2pzU1AERMk4+qqztuYcOAAw9sKeLTjltXEsKR6ALaQiwW47XXXqOxsZHx48dv09c0NTURiURIT0//2fNCoRChUKj187q6ui1uRURERERERERk26VfmU7dTXU0r2pm1ROrGDBiQIetva/Dgb2xkRgwbd06Dtk0W7UjJCdbt2++CZdd1nHrys/alPG1Zbe1YXZ073YbWrRoEePHjycYDBIIBHjxxRc5+uijt+lr//jHPzJ9+nSWLFmCx+P5yfNuuukmbr755rYqWURERERERERERDqZpUuXMmTIkDa5VpcOXMPhMIWFhdTW1jJt2jSeeuopZs2axdChQ3/26+644w7uuusuZs6cyciRI3/23B92uK5fv57hw4ezZMkSunfv3iZ/DhFJnLq6Onr06MH69etJ3vTbRhHpsvScFtm16DktsuvR81pk17IrPKdra2vp2bMn1dXVpKamtsk1u/RIAZfLRf/+/QEYO3Ysc+fO5R//+AePP/74T37N3//+d+644w4++uijXwxbAdxuN+7vtZenpKQAkJyc3GX/RxKRH9NzWmTXoue0yK5Fz2mRXY+e1yK7ll3hOW2ztd1WV106cP2heDy+RTfqD911113cdtttTJ8+nT333LMDKxMREREREREREZHdQZcNXCdPnsxRRx1Fz549qa+v58UXX2TmzJlMnz4dgIkTJ9KtWzemTJkCwJ133skNN9zAiy++SO/evSkpKQEgEAgQCAQS9ucQERERERERERGRXUeXDVzLysqYOHEixcXFpKSkMHLkSKZPn85hhx0GQGFh4RatwI8++ijhcJhTTz11i+vceOON3HTTTdu87qbxAu6O3MVORNqN2+3mxhtv1HNaZBeh57TIrkXPaZFdj57XIrsWPae3rktvmpUIdXV1pKSkUFtb2+VnU4iIiIiIiIiIiOzO2iPra7tpsCIiIiIiIiIiIiK7OQWuIiIiIiIiIiIiIm1EgauIiIiIiIiIiIhIG1HgKiIiIiIiIiIiItJGFLiKiIiIiIiIiIiItBEFrtvh4YcfZsSIEQAccsghfPXVVwmuSER21E033YRhGFt8DB48ONFlicg2+vTTTznuuOPIz8/HMAzeeuutLR43TZMbbriBvLw8vF4vEyZMYOXKlYkpVkR+0S89p3//+9//6HX7yCOPTEyxIvKLpkyZwl577UVSUhLZ2dmceOKJLF++fItzgsEgF198MRkZGQQCAU455RRKS0sTVLGI/JxteU4ffPDBP3qtvvDCCxNUceIpcN1Gr7zyCldeeSXXXHMNAMOHD+eII46grKwswZWJyI4aNmwYxcXFrR+zZ89OdEkiso0aGxsZNWoUDz/88FYfv+uuu3jggQd47LHH+PLLL/H7/RxxxBEEg8EOrlREtsUvPacBjjzyyC1et1966aUOrFBEtsesWbO4+OKL+eKLL/jwww+JRCIcfvjhNDY2tp5zxRVX8O677/Laa68xa9YsNm7cyMknn5zAqkXkp2zLcxrg/PPP3+K1+q677kpQxYlnmKZpJrqIrmDcuHHstdde3H777aSkpFBdXc2wYcP405/+xLXXXpvo8kRkO91000289dZbLFiwINGliMhOMgyDN998kxNPPBGwulvz8/P585//zFVXXQVAbW0tOTk5TJ06ldNPPz2B1YrIL/nhcxqsDteampofdb6KSNdQXl5OdnY2s2bN4sADD6S2tpasrCxefPFFTj31VACWLVvGkCFDmDNnDvvss0+CKxaRn/PD5zRYHa6jR4/m/vvvT2xxO6Curo6UlBRqa2tJTk5uk2uqw3UbhMNh5s2bx4QJE1rvs9lsTJgwgTlz5iSwMhHZGStXriQ/P5++ffvy29/+lsLCwkSXJCJtYO3atZSUlGzxup2SksK4ceP0ui3Shc2cOZPs7GwGDRrERRddRGVlZaJLEpFtVFtbC0B6ejoA8+bNIxKJbPFaPXjwYHr27KnXapEu4IfP6U1eeOEFMjMzGT58OJMnT6apqSkR5XUKjkQX0BVUVFQQi8XIycnZ4v6cnByWLVuWoKpEZGeMGzeOqVOnMmjQIIqLi7n55ps54IADWLx4MUlJSYkuT0R2QklJCcBWX7c3PSYiXcuRRx7JySefTJ8+fVi9ejXXXXcdRx11FHPmzMFutye6PBH5GfF4nMsvv5z99tuP4cOHA9ZrtcvlIjU1dYtz9Vot0vlt7TkNcOaZZ9KrVy/y8/NZuHAh11xzDcuXL+eNN95IYLWJo8BVRHZLRx11VOvxyJEjGTduHL169eLVV19l0qRJCaxMREREfuj7o0BGjBjByJEj6devHzNnzuTQQw9NYGUi8ksuvvhiFi9erP0SRHYRP/WcvuCCC1qPR4wYQV5eHoceeiirV6+mX79+HV1mwmmkwDbIzMzEbrf/aMfE0tJScnNzE1SViLSl1NRUBg4cyKpVqxJdiojspE2vzXrdFtl19e3bl8zMTL1ui3Ryl1xyCe+99x6ffPIJ3bt3b70/NzeXcDhMTU3NFufrtVqkc/up5/TWjBs3DmC3fa1W4LoNXC4XY8eOZcaMGa33xeNxZsyYwfjx4xNYmYi0lYaGBlavXk1eXl6iSxGRndSnTx9yc3O3eN2uq6vjyy+/1Ou2yC6iqKiIyspKvW6LdFKmaXLJJZfw5ptv8vHHH9OnT58tHh87dixOp3OL1+rly5dTWFio12qRTuiXntNbMVvvzwABAABJREFUs2mD6t31tVojBbbRlVdeydlnn82wYcMAuOKKK2hsbOScc85JcGUisiOuuuoqjjvuOHr16sXGjRu58cYbsdvtnHHGGYkuTUS2QUNDwxa/LV+7di0LFiwgPT2dnj17cvnll3PrrbcyYMAA+vTpw/XXX09+fv4Wu56LSOfxc8/p9PR0br75Zk455RRyc3NZvXo1//d//0f//v054ogjEli1iPyUiy++mBdffJG3336bpKSk1rmsKSkpeL1eUlJSmDRpEldeeSXp6ekkJyfzpz/9ifHjx7PPPvskuHoR+aFfek6vXr2aF198kaOPPpqMjAwWLlzIFVdcwYEHHsjIkSMTXH1iGKZpmokuoqt46KGHuOuuu1i/fj1jx47l4Ycfbm2RFpGu5fTTT+fTTz+lsrKSrKws9t9/f2677bbdcraMSFc0c+ZMfvWrX/3o/rPPPpupU6dimiY33ngjTzzxBDU1Ney///488sgjDBw4MAHVisgv+bnn9KOPPsqJJ57IN998Q01NDfn5+Rx++OHccsstP9ocT0Q6B8Mwtnr/M888w+9//3sAgsEgf/7zn3nppZcIhUIcccQRPPLIIxopINIJ/dJzev369Zx11lksXryYxsZGevTowUknncRf//pXkpOTO7ja7VdXV0dKSgq1tbVtVq8C1+3UHv8RREREREREREREpOO1R9anGa4iIiIiIiIiIiIibUSBq4iIiIiIiIiIiEgbUeAqIiIiIiIiIiIi0kYUuIqIiIiIiMj/s3ff4VGVaRvA7zM9vUIKhF5DlSrYAEGKoCgoVhAFPxV0lXVV1rWLvSAaxY6woIgIuopIEUURpYbea4AkJKRnkmnnfH+8cyYzySSZJJPK/buu9zq9TDL1Oc95XiIiIvITBlyJiIiIiIiIiIiI/IQBVyIiIiIiIiIiIiI/YcCViIiIiIiIiIiIyE8YcCUiIiK6SEmShJUrV9Z4PwsWLEB4eHiN90Pe/frrr5AkCTk5ObWy/6eeegr33nuv389p/vz5GDduXA3PjoiIiKjxYcCViIiIqAm56667IEkSJEmCXq9HTEwMRowYgc8++wyyLHusm5qaitGjR9fTmda/tLQ0PPjgg2jXrh2MRiMSEhIwbtw4rF+/vr5PzcPgwYORmpqKsLAwAP4NcKelpeGdd97Bk08+WWbZ5s2bodVqce2111Zr33fffTd27NiB33//vaanSURERNSoMOBKRERE1MSMGjUKqampOHnyJH766ScMHToU//jHPzB27FjY7XbXerGxsTAajfV4phWz2Wy1tu+TJ0+ib9+++OWXX/D6669jz549WL16NYYOHYoZM2bU2nGrw2AwIDY2FpIk+X3fn3zyCQYPHozWrVuXWfbpp5/iwQcfxMaNG3Hu3Lkq79tgMOC2227DvHnz/HGqRERERI0GA65ERERETYzRaERsbCxatGiBPn364N///je+++47/PTTT1iwYIFrPfeSAlarFTNnzkRcXBxMJhNat26Nl19+2bVuTk4O/u///g8xMTEwmUzo3r07fvjhB4/j/vzzz+jatSuCg4NdQV/V1q1bMWLECERHRyMsLAxXXXUVduzY4bG9JEn44IMPcN111yEoKAhz5swBALz44oto3rw5QkJCMG3aNDzxxBPo3bu3x7affPIJunbtCpPJhC5duuD999+v8G/0wAMPQJIkbNmyBRMmTECnTp3QrVs3zJo1C3/99Zdrvbfeegs9evRAUFAQEhIS8MADD6CgoMC1XM02XblyJTp27AiTyYSRI0ciJSXFtc6xY8dw/fXXIyYmBsHBwejfvz/WrVvncT4WiwWPP/44EhISYDQa0aFDB3z66acAPG/f//XXXzF16lTk5ua6MpmfffZZPP/88+jevXuZx9m7d2889dRT5f4dvvrqK6+3/RcUFGDp0qW4//77ce2113o8b9xt374d/fr1Q2BgIAYPHoxDhw55LB83bhy+//57FBUVlXsORERERE0NA65EREREF4Fhw4ahV69e+Pbbb70unzdvHr7//nt8/fXXOHToEBYvXow2bdoAAGRZxujRo7Fp0yb897//xf79+/HKK69Aq9W6tjebzXjjjTewaNEibNy4EadPn8ajjz7qWp6fn48pU6bgjz/+wF9//YWOHTtizJgxyM/P9ziPZ599FjfccAP27NmDu+++G4sXL8acOXPw6quvYvv27WjVqhU++OADj20WL16Mp59+GnPmzMGBAwfw0ksv4amnnsIXX3zh9bFmZWVh9erVmDFjBoKCgsosd79dX6PRYN68edi3bx+++OIL/PLLL3jsscc81jebzZgzZw4WLlyITZs2IScnB7fccotreUFBAcaMGYP169dj586dGDVqFMaNG4fTp0+71pk8eTK+/PJLzJs3DwcOHMCHH36I4ODgMuc2ePBgzJ07F6GhoUhNTUVqaioeffRR3H333Thw4AC2bt3qWnfnzp3YvXs3pk6dWu7fYf/+/ejXr1+ZZV9//TW6dOmCzp0744477sBnn30GRVHKrPfkk0/izTffxLZt26DT6XD33Xd7LO/Xrx/sdjv+/vtvr+dARERE1CQpVCW5ubkKACU3N7e+T4WIiIiojClTpijXX3+912WTJk1Sunbt6poGoKxYsUJRFEV58MEHlWHDhimyLJfZ7ueff1Y0Go1y6NAhr/v9/PPPFQDK0aNHXfOSkpKUmJiYcs/T4XAoISEhyv/+9z+P83n44Yc91hs4cKAyY8YMj3mXXXaZ0qtXL9d0+/btlSVLlnis88ILLyiDBg3yeuy///5bAaB8++235Z5feZYtW6ZERUW5ptXH/tdff7nmHThwQAGg/P333+Xup1u3bsq7776rKIqiHDp0SAGgrF271uu6GzZsUAAo2dnZrmOGhYWVWW/06NHK/fff75p+8MEHlSFDhpR7Djt37lQAKKdPny6zbPDgwcrcuXMVRVEUm82mREdHKxs2bChzTuvWrXPN+/HHHxUASlFRkce+IiIilAULFpR7HkRERET1qTZifcxwJSIiIrpIKIpSbh3Qu+66C8nJyejcuTMeeughrFmzxrUsOTkZLVu2RKdOncrdd2BgINq3b++ajouLw/nz513T6enpmD59Ojp27IiwsDCEhoaioKDAI8sTQJlsy0OHDmHAgAEe89ynCwsLcezYMdxzzz0IDg52tRdffBHHjh0r9+/gq3Xr1uHqq69GixYtEBISgjvvvBMXLlyA2Wx2raPT6dC/f3/XdJcuXRAeHo4DBw4AEBmujz76KLp27Yrw8HAEBwfjwIEDrseenJwMrVaLq666yufz8mb69On48ssvUVxcDKvViiVLlpTJOHWn3uZvMpk85h86dAhbtmzBrbfe6np8kyZNcpU4cNezZ0/XeFxcHAB4/N8BICAgwOPvRURERNTU6er7BIiIiIiobhw4cABt27b1uqxPnz44ceIEfvrpJ6xbtw4333wzhg8fjm+++QYBAQGV7luv13tMS5LkEdicMmUKLly4gHfeeQetW7eG0WjEoEGDYLVaPbbzdot/RdR6qh9//DEGDhzoscy95IG7jh07QpIkHDx4sMJ9nzx5EmPHjsX999+POXPmIDIyEn/88QfuueceWK1WBAYG+nSOjz76KNauXYs33ngDHTp0QEBAACZOnOh67L78fX0xbtw4GI1GrFixAgaDATabDRMnTix3/ejoaABAdnY2mjVr5pr/6aefwm63Iz4+3jVPURQYjUa89957CAsLc813/7+rwXxZlj2Ok5WV5bF/IiIioqaOGa5EREREF4FffvkFe/bswYQJE8pdJzQ0FJMmTcLHH3+MpUuXYvny5cjKykLPnj1x5swZHD58uNrH37RpEx566CGMGTMG3bp1g9FoRGZmZqXbde7c2aMuKQCP6ZiYGMTHx+P48ePo0KGDRysvuBwZGYmRI0ciKSkJhYWFZZbn5OQAEB1CybKMN998E5deeik6deqEc+fOlVnfbrdj27ZtrulDhw4hJycHXbt2dT32u+66CzfccAN69OiB2NhYnDx50rV+jx49IMsyfvvtt0r/HgBgMBjgcDjKzNfpdJgyZQo+//xzfP7557jlllsqDOa2b98eoaGh2L9/v8djWbhwId58800kJye72q5duxAfH48vv/zSp3NUHTt2DMXFxbjkkkuqtB0RERFRY8YMVyIiIqImxmKxIC0tDQ6HA+np6Vi9ejVefvlljB07FpMnT/a6zVtvvYW4uDhccskl0Gg0WLZsGWJjYxEeHo6rrroKV155JSZMmIC33noLHTp0wMGDByFJEkaNGuXTOXXs2BGLFi1Cv379kJeXh3/9618+ZXY++OCDmD59Ovr164fBgwdj6dKl2L17N9q1a+da57nnnsNDDz2EsLAwjBo1ChaLBdu2bUN2djZmzZrldb9JSUm47LLLMGDAADz//PPo2bMn7HY71q5diw8++AAHDhxAhw4dYLPZ8O6772LcuHHYtGkT5s+fX2Zfer0eDz74IObNmwedToeZM2fi0ksvdZU+6NixI7799luMGzcOkiThqaee8sgCbdOmDaZMmYK7774b8+bNQ69evXDq1CmcP38eN998c5njtWnTBgUFBVi/fj169eqFwMBAV7bttGnTPAK9FdFoNBg+fDj++OMPjB8/HgDwww8/IDs7G/fcc49HJisATJgwAZ9++inuu+++Cvfr7vfff0e7du08yk0QERERNXXMcCUiIiJqYlavXo24uDi0adMGo0aNwoYNGzBv3jx899135d5mHxISgtdeew39+vVD//79cfLkSaxatQoajfi6uHz5cvTv3x+33norEhMT8dhjj3nNsizPp59+iuzsbPTp0wd33nknHnroITRv3rzS7W6//XbMnj0bjz76qKvswV133eVRd3TatGn45JNP8Pnnn6NHjx646qqrsGDBgnIzXAGgXbt22LFjB4YOHYp//vOf6N69O0aMGIH169fjgw8+AAD06tULb731Fl599VV0794dixcvxssvv1xmX4GBgXj88cdx22234bLLLkNwcDCWLl3qWv7WW28hIiICgwcPxrhx4zBy5Ej06dPHYx8ffPABJk6ciAceeABdunTB9OnTvWbfAsDgwYNx3333YdKkSWjWrBlee+0117KOHTti8ODB6NKlS5kSC95MmzYNX331lSsA/Omnn2L48OFlgq2ACLhu27YNu3fvrnS/qi+//BLTp0/3eX0iIiKipkBSqtJrACEvLw9hYWHIzc1FaGhofZ8OERER0UVnxIgRiI2NxaJFi+r7VLBgwQI8/PDDrjIE9U1RFHTs2BEPPPBAudm9pdcfOHAgHnnkEVcnWf6yb98+DBs2DIcPH/YawCUiIiJqCGoj1seSAkRERETUYJnNZsyfPx8jR46EVqvFl19+iXXr1mHt2rX1fWoNTkZGBr766iukpaVh6tSpPm0jSRI++ugj7Nmzx+/nk5qaioULFzLYSkRERBcdBlyJiIiIqMGSJAmrVq3CnDlzUFxcjM6dO2P58uUYPnx4fZ9ag9O8eXNER0fjo48+QkREhM/b9e7dG7179/b7+fB/RERERBcrlhSoIpYUICIiIiIiIiIiahpqI9bHTrOIiIiIiIiIiIiI/IQBVyIiIiIiIiIiIiI/YcCViIiIiIiIiIiIyE8YcCUiIiIiIiIiIiLyEwZciYiIiIiIiIiIiPyEAVciIiIiIiIiIiIiP2HAlYiIiIiIiIiIiMhPGHAlIiIiIiIiIiIi8hMGXImIiIiIiIiIiIj8hAFXIiIiIiIiIiIiIj9hwJWIiIiIiIiIiIjITxhwJSIiIiIiIiIiIvITBlyJiIiIiIiIiIiI/IQBVyIiIiIiIiIiIiI/YcCViIiIiIiIiIiIyE8YcCUiIiIiIiIiIiLyEwZciYiIiIiIiIiIiPyEAVciIiIiIiIiIiIiP2HAlYiIiIiIiIiIiMhPGHAlIiIiIiIiIiIi8hMGXImIiIiIiIiIiIj8hAFXIiIiIiIiIiIiIj9hwJWIiIiIiIiIiIjITxhwJSIiIiIiIiIiIvITBlyJiIiIiIiIiIiI/OSiDLjecMMNiIiIwMSJE+v7VIiIiIiIiIiIiKgJuSgDrv/4xz+wcOHC+j4NIiIiIiIiIiIiamIuyoDrkCFDEBISUt+nQURERERERERERE1Mgwu4bty4EePGjUN8fDwkScLKlSvLrJOUlIQ2bdrAZDJh4MCB2LJlS92fKBEREREREREREVEpuvo+gdIKCwvRq1cv3H333bjxxhvLLF+6dClmzZqF+fPnY+DAgZg7dy5GjhyJQ4cOoXnz5gCA3r17w263l9l2zZo1iI+Pr9H5ybIMAMjNza3RfoiIiIiIiIiIiKh+qTE+NebnDw0u4Dp69GiMHj263OVvvfUWpk+fjqlTpwIA5s+fjx9//BGfffYZnnjiCQBAcnKy387HYrHAYrG4po8cOQIAaNWqld+OQURERERERERERPUnNTUV4eHhftlXgwu4VsRqtWL79u2YPXu2a55Go8Hw4cOxefPmWjnmyy+/jOeee67M/E8++QSBgYG1ckwiIiIiIiIiIiKqfWazGdOmTUNwcLDf9tmoAq6ZmZlwOByIiYnxmB8TE4ODBw/6vJ/hw4dj165dKCwsRMuWLbFs2TIMGjTI67qzZ8/GrFmzXNNnz55FYmIirr32WkRFRVXvgRBRg2Gz2bB27VqMGDECer2+vk+HiGqIr2mipoWvaaKmh69roqalKbym8/LyMG3aNEiS5Ld9NqqAq7+sW7fO53WNRiOMRiOSkpKQlJQEm80GANDr9Y32iUREZfE1TdS08DVN1LTwNU3U9PB1TdS0NObXdG2ct8bve6xF0dHR0Gq1SE9P95ifnp6O2NjYWj32jBkzsH//fmzYsKFWj0NERERERERERESNV6MKuBoMBvTt2xfr1693zZNlGevXry+3JAARERERERERERFRXWlwJQUKCgpw9OhR1/SJEyeQnJyMyMhItGrVCrNmzcKUKVPQr18/DBgwAHPnzkVhYSGmTp1aq+dVuqQAERERERERERERUWkNLuC6bds2DB061DWtdlg1ZcoULFiwAJMmTUJGRgaefvpppKWloXfv3li9enWZjrT8bcaMGZgxYwbOnDmDhISEWj0WERERERERERERNU4NLuA6ZMgQKIpS4TozZ87EzJkz6+iMBGa4EhERERERERERUWUaVQ3X+sROs4iIiIiIiIiIiKgyDLgSERERERERERER+QkDrkRERERERERERER+0uBquDZUrOFKRERERERERHRx+TU7G/vNZgwICUG/0ND6Ph1qJJjh6iPWcCUiIiIiIiIiurh8df48Zhw5gh+zsur7VKgRYcCViIiIiIiIiIjIC4uiAACMklTPZ0KNCQOuREREREREREREXlhlGQBg0DCERr7js8VHSUlJSExMxNChQ+v7VIiIiIiIiIiIqA5YnAFXIwOuVAV8tviINVyJiIiIiIiIiC4uLClA1cGAKxERERERERERkRcsKUDVwWcLERERERERERGRFywpQNXBZwsREREREREREZEXLClA1cGAq4/YaRYRERERERER0cWFJQWoOvhs8RE7zSIiIiIiIiIiql+7x+zGrmt2oehkUZ0cjyUFqDp09X0CREREREREREREvsjZkAO5WK6z41lZUoCqgeF5IiIiIiIiIiJq8BzFDlewVR+hr5NjWlhSgKqBzxYiIiIiIiIiImrw7Dl2MSIB2hBtnRyTJQWoOhr9syUpKQlt2rSByWTCwIEDsWXLFp+2++qrryBJEsaPH1+7J0hERERERERERDVmzxYBV124DpKmbm7xV0sKGFhSgKqgUQdcly5dilmzZuGZZ57Bjh070KtXL4wcORLnz5+vcLuTJ0/i0UcfxRVXXOHzsZKSkpCYmIihQ4fW9LSJiIiIiIiIiKiKXAHXiLrrkogZrlQdjfrZ8tZbb2H69OmYOnUqEhMTMX/+fAQGBuKzzz4rdxuHw4Hbb78dzz33HNq1a+fzsWbMmIH9+/djw4YN/jh1IiIiIiIiIiKqArWkgC6cAVdq2Brts8VqtWL79u0YPny4a55Go8Hw4cOxefPmcrd7/vnn0bx5c9xzzz0+HcdisSAvL8/V8vPza3zuRERERERERERUNXWd4epQFDic4ywpQFXRaAOumZmZcDgciImJ8ZgfExODtLQ0r9v88ccf+PTTT/Hxxx/7fJyXX34ZYWFhrpaYmFij8yYiIiIiIiIioqqzZdsAAPoIfZ0cz+rMbgWY4UpVc9E8W/Lz83HnnXfi448/RnR0tM/bzZ49G7m5ua62f//+WjxLIiIiIiIiIiLypq4zXC0MuFI11V3RCz+Ljo6GVqtFenq6x/z09HTExsaWWf/YsWM4efIkxo0b55onO184Op0Ohw4dQvv27ctsZzQaYTQaXdN5eXn+eghEREREREREROSjuq7halUU17ieJQWoChpteN5gMKBv375Yv369a54sy1i/fj0GDRpUZv0uXbpgz549SE5OdrXrrrsOQ4cORXJyMhISEury9ImIiIiIiIiIqArqK8NVL0nQMOBKVdBoM1wBYNasWZgyZQr69euHAQMGYO7cuSgsLMTUqVMBAJMnT0aLFi3w8ssvw2QyoXv37h7bh4eHA0CZ+URERERERERE1LDUdcBVzXBlOQGqqkYdcJ00aRIyMjLw9NNPIy0tDb1798bq1atdHWmdPn0aGr4oiIiIiIiIiIgavbouKaBmuBqY3UpV1KgDrgAwc+ZMzJw50+uyX3/9tcJtFyxY4PNxkpKSkJSUBJvNVoWzIyIiIiIiIiIif7Bli5iMPkJfJ8dTA67McKWq4jPGRzNmzMD+/fuxYcOG+j4VIiIiIiIiIqKLDksKUGPBZwwRERERERERETV49dVpFksKUFUx4EpERERERERERA2abJfhyHcAqPsarsxwpariM4aIiIiIiIiIiBo0R67DNV5XAVeWFKDq4jOGiIiIiIiIiIgaNLXDLG2wFhp93YSzWFKAqosBVyIiIiIiIiIiatDsOc76rXWU3QqwpABVH58xRERERERERETUoNV1h1lASUkBZrhSVTHgSkREREREREREDVp9BFyZ4UrVddE9Y1JSUjBkyBAkJiaiZ8+eWLZsWX2fEhERERERERERVaBeMlwZcKVqqrtnaQOh0+kwd+5c9O7dG2lpaejbty/GjBmDoKCg+j41IiIiIiIiIiLyol5quLKkAFXTRRdwjYuLQ1xcHAAgNjYW0dHRyMrKYsCViIiIiIiIiKiBsmXbAAD6CH2dHZMlBai6GtwzZuPGjRg3bhzi4+MhSRJWrlxZZp2kpCS0adMGJpMJAwcOxJYtW6p1rO3bt8PhcCAhIaGGZ01ERERERERERLWFJQWoMWlwz5jCwkL06tULSUlJXpcvXboUs2bNwjPPPIMdO3agV69eGDlyJM6fP+9ap3fv3ujevXuZdu7cOdc6WVlZmDx5Mj766KNaf0xERERERERERFR9rpICddlpFksKUDU1uJICo0ePxujRo8td/tZbb2H69OmYOnUqAGD+/Pn48ccf8dlnn+GJJ54AACQnJ1d4DIvFgvHjx+OJJ57A4MGDK13XYrG4pvPz8wEANpsNNpvNl4dERA2Y+jrm65moaeBrmqhp4WuaqOnh65qqy5YlnjNSsFRnz58iuzPICz5ny9MUXtO1ce4NLuBaEavViu3bt2P27NmueRqNBsOHD8fmzZt92oeiKLjrrrswbNgw3HnnnZWu//LLL+O5554rM3/Dhg0IDAz0/eSJqEFbu3ZtfZ8CEfkRX9NETQtf00RND1/XVFVBJ4Oggw7Jx5JhX2Wvk2MeMZkAoxEpx45h1f79dXLMxqoxv6bNZrPf99moAq6ZmZlwOByIiYnxmB8TE4ODBw/6tI9NmzZh6dKl6Nmzp6s+7KJFi9CjRw+v68+ePRuzZs1yTZ89exaJiYkYOnQooqKiqvdAiKjBsNlsWLt2LUaMGAG9vu6KrxNR7eBrmqhp4WuaqOnh65qqa/s/t6MYxbh0xKUIHRxaJ8f8/uhRID0diZ06YQz7//GqKbym8/Ly/L7PRhVw9YfLL78csrPosS+MRiOMRiOSkpKQlJTkSjPW6/WN9olERGXxNU3UtPA1TdS08DVN1PTwdU1VpdZwNUWb6uy5o95oHqjT8flaicb8mq6N825wnWZVJDo6GlqtFunp6R7z09PTERsbW6vHnjFjBvbv348NGzbU6nGIiIiIiIiIiKiEoij10mmW1ZmwZ9Q0qvAZNQCN6hljMBjQt29frF+/3jVPlmWsX78egwYNqtVjJyUluUoJEBERERERERFR3XDkOwCHGK/LgKtFUQAABkmqs2NS09DgSgoUFBTg6NGjrukTJ04gOTkZkZGRaNWqFWbNmoUpU6agX79+GDBgAObOnYvCwkJMnTq1Vs9rxowZmDFjBs6cOYME1u0gIiIiIiIiIqoTanarZJSgDdDW2XEtzHClampwAddt27Z5ZJGqHVZNmTIFCxYswKRJk5CRkYGnn34aaWlp6N27N1avXl2mIy0iIiIiIiIiImr87NnOcgLhdRvGYkkBqq4GF3AdMmQIFGfKdnlmzpyJmTNn1tEZCaU7zSIiIiIiIiIiotpny3Z2YB5Rt50ysaQAVRdD9D5ip1lERERERERERHXPleFah/VbAWa4UvXxGUNERERERERERA2WWsO1rksKqDVcDQy4UhXxGUNERERERERERA1WfWW4qiUFjCwpQFXEgKuPkpKSkJiY6NGhFxERERERERER1S6WFKDGhs8YH7GGKxERERERERFR3bJl25DxTQYAwBBrqNNjs6QAVRefMURERERERERE1ODIFhl7b9gL80EzDC0MiLs7rk6Pz5ICVF0MuBIRERERERERUYOiyAoO3nUQub/lQhuqRc9VPWGMN9bpObCkAFVX3Ra/aMSSkpKQlJQEm81W36dCRERERERERNTgWTOsOPX8Kdhz7NA300PfTA9Dc4MYxhgQfEkwNAbvwcwTT57A+a/OQ9JJ6La8G4J7Btf4fI6azdhVWIhrIyNh0morXb/WSwooCmA2A0FBtbN/qjcMuPpoxowZmDFjBs6cOYOEhIT6Ph0iIiIiIiIiogbLmm5F8tXJMO8zl7uOvpkecdPiEHdvHALaBLjmn51/FqdfOQ0A6PxJZ0QOj6zx+WzNy8OIXbuQ63CguV6PB1u0wP0tWiBKry+z7v7CQnycmlo7JQXsduD334GVK0UbOxZISvLf/qlBYMCViIiIiIiIiIgAAIqiIOvnLJyddxa6MB06fdwJuuCqhY8sqRbsGrZL1F6NN6DlQy1hu2CDLcMG63krbBk2FJ8ohi3DhtMvn8bpV04jckwkWtzfAopdwZEZRwAAbZ5vg9gpsTV+TO7BVoMk4bzNhqdOnsRLp0/j7thYzEpIQJzBgG8yMvBRair+yM11bXtJcDCaGWrYWZfZDKxZIwKs//sfkJVVsmztWpHpyjqxTQoDrkREREREREREFznZLiNjaQZOv3YahbsLXfOLThSh56qe0EeWzQT1xnLWguRhySg6XARjSyN6beiFwA6BXo934X8XcO6Dc8hem42sH7OQ9WNJIDL2nli0/k/rGj+ubW7B1svDwvB99+74KSsLr6ekILmgAEnnzuGDc+cQrNUiz+EAAGgBjI2Kwr3x8RgZGQltTYKhJ04Al10GpKaWzIuKAq67Dhg/Hhg+nMHWJogBVx+xhisRERERERERNTUOswOpn6Ui5Y0UWE5ZAACaIA1iJ8fi/NLzyP87H8lDktFzTU8YYyvutKo4pRjJQ5NRfKwYxlZG9N7QGwHtAryuq9Fp0OyGZmh2QzOYj5hxbv45pH2eBnu2HREjI9Dpg06QahiI3JaXhxG7dyPX4cBloaFY1aMHQnQ63BYTg1ubN8cvOTl4IyUFq7OykOdwoLXRiGlxcZgaF4cWRqPISs3IACZOBIzV7LDrlVdEsDUuDrjlFhFkHTwY0DEk15Txv+sj1nAlIiIiIiIioqbEUeTAjoE7ULhXZLTqm+nR8h8tEX9/PPSResQ/EI/dI3ajcE8hkq9IRq91vWBqbfK6r+JTzmDriWKY2prQ65deHnVZKxLYMRAd3uwAx79j8emqYzjdQ4uQ40cQqtUiTKdDqFaLGIMBN0RH+9TZFQBsz8/HiN27kWO347LQUPzUsydC3IKckiTh6ogIXB0RgYOFhciy2zEwNLQkm/XLL4HbbhPjjz8OPPooMH161Tq4SksDvvhCjC9dClxxhe/bUqPGgCsRERERERER0UXo1AunULi3EPpmerR5rg1i74qFNqAkoBncPRiX/HEJdg3fhaKjRdh5+U70XNsTQV1E0NGeZ0fObznIXpeNjK8zYE2zwtTehN4besOU4D0wW57zVitGHt6DUwkWIAeilfJkq1Z4sV27Sve1Mz8fw3ftKjfYWlqX0kHUNWuAKVPEeFAQcPYs8MgjwIsvAg8/DMycCYSHV/6g5s0DLBZg0CDg8ssrX5+aDE19nwAREREREREREdWtgt0FSHk9BQDQ+ZPOaHF/C49gqyqgfQB6/94bgV0CYTljQfKVyTg++zh2XLYDf0T+gb3X7cXZeWdhTbMioFMALvntkioHW4sdDozfuxenLBZ0CAjAvA4d8GLbtvhXQgL+Ly4OoyMjAQAfpqai2FlntTyKouCugweRY7djsA/B1jK2bAFuvBGw2YBJk0RJgY8+Atq1Ay5cAJ56CmjVCnj99Yr3k58PvP++GH/sMdZpvcgww5WIiIiIiIiI6CKiOBQcuvcQFLuC6AnRiL4uusL1TS1N6L2xN3aP3I2CnQU4/cpp17KADgGIGB6BiOERiBwVCW2Qb7f8u85FUXD3oUPYnJeHcJ0OP/Togc6Bnp1s2WUZ7f7+GykWC5ZlZODO2Nhy97c+Oxu7CwsRpNHge2fNVp8dPAiMGQMUFgIjRgALFwIGgyglMHUqsGwZ8NJLwN69Iojarh0wYYL3fX30EZCbC3TuLDrIoosKM1yJiIiIiIiIiC4iZz84i/y/86EN1aLjvI4+bWNoZkDvDb0Rc2cMmk1qhk4fd8LAEwMx8MhAdPqgE5pNaFblYCsAPH/qFL48fx46ScLybt3KBFsBQKfR4P/i4wEASWfPVri/t86cAQDcHReHKL3e9xM5cwYYOVJksfbvDyxfLoKtrpPQAbfeCuzaJeq5AsC99wLnzpXdl9UKvP22GP/XvwANw28XG/7HiYiIiIiIiIguEsUpxTgx+wQAoN2r7WCMN/q8rS5Mh64Lu6LbV90QPy3e506xyvNlejqePXkSAPBBx44YFhFR7rrT4uKglyT8nZ+P7fn5XtfZX1iIn7KyIAH4R8uWvp9IVpYItp4+LTJSf/wRCAnxvq5GA8yZA/TpI7a76y5Alks9sC9F3de4OOCOO3w/D2oyGHD1UVJSEhITEzF06ND6PhUiIiIiIiIioipTFAVHZh6Bo8CB0MGhiL83vtr7KnI4cNZiwZ6CAvyWk4PvMjORarH4vP3m3FxMPXgQAPBoQgKmxVd8LjEGAyY2awYAeL+cLNe5zuzW8dHRaB9QhWDwbbcB+/cDLVoAP/8MOI9TLoMB+O9/AZMJWLsWeO+9kmWyDLz2mhh/+GHA6HtAm5oOBlx9NGPGDOzfvx8bNmwos8x82Iy/2v2FXaN2IXtDNhRFqYczJCIiIiIiIiIqX+aKTFz4/gIkvYTOH3WGpKlaR04ZViuGJScjYONGBP7+O1pu3oye27ZhSHIyxu/di6uSk2Etne3pRUpxMcbv3QuLouC6qCi80q6dT8ef0aIFAGDJ+fPIstmATZuAmTOB5GRkWK1YmJYGAJhVlezWM2dEkFWSgNWrgdatfduua1fgjTfE+OOPA/v2ifFVq0TwNjQU+L//8/08qElhwNUPzn1wDsUnipH9czZ2DduFHYN2IGNlBhSZgVciIiIiIiIiqn/2XDuOzDwCAGj1RCsEdQuq0vZmhwPj9uzBhpwcFDuDqloAzfR6dA4IQLBWiyNFRfg4NbXSfT167BjO22zoFRSExV27Qiv5FvgdHBqKXkFBKJZlfL56NTBsGJCUBPTpgw/mz4dFUdA/JASXhYX5/sB++EEMBw0Cunf3fTsAeOABYNQooLgYuOMO/HlkA7Kfny2W3XcfUJXzoCaFAdcaUhQFGcszAAARIyOgMWmQ/3c+9t2wD1u7b0XaF2mQbZVf3SEiIiIiIiIiqi3HnzgOa6oVAZ0C0Orfraq0rUNRcMeBA/g7Px8ROh02X3IJci6/HLarrsL5yy7DwYED8aozS/X5kydRYLeXu69Nubn4OiMDEoAvunZFsE7n83lIkuTKcv0gLw+yzQZ06oRinQ5JzqzWWbt2QbLZfH9wasB13Djftyk5IeCzz4CoKCA5GdnXDkPE1r2waIFRUT/hkdWPYPn+5ThfeL7q+6ZGjQHXGsrfmg9LigXaYC26r+iOS09eilb/bgVtmBbmA2YcvOsgtnTegtQFqZDtDLwSERERERERUd3K+jkL5+afAwB0+rATtCZtlbZ/9NgxrMjMhEGSsLJ7d1waFoYwnQ6SW2bq9Lg4tDeZcN5mc9VSLU1WFDxy9CgA4J64OPQKDq7aA5Fl3PbOOwgrKMCxFi2w5vnngf37sWTtWpyPjERCejom3HuvyFT98cfK92c2A+vXi/GxY6t2Lqq4OODjjwEA14oEYizqCfxctAdz/56LicsmIuaNGDy8+uHq7Z8aJQZcayjjG5HdGjU2CtoALQwxBrSb0w6DTg1Cu1fbQd9cj+ITxTg09RC2dt2KtEVpUBwsNUBEREREREREtc92wYaDU0XnVC0ebIGIIRFV2v6dM2dcAdQvunTBleHhXtfTazR4sW1bAMBrKSnItFrLrPPl+fPYmp+PYK0WL7RpU6XzgNUK3Hkngl57DVN/+gkAkHTddVA0Grzl7CDrIZ0O+uho4MgREUBdu7bifa5fL8oBtG4NdOtWtfNxs/PSNvjkEjGuSBKu/fAXLLlxCe7vdz+6NxdlCuZvm498S361j0GNCwOuNaAoiivgGj0h2mOZLkyHVo+1wqXHL0W719tBH61H0dEiHJx8EFu6bUH6knTIFma8EhEREREREVHtUBQFh+8/DGuqFYFdAtHuFd86p1KtyMhwZaS+0q4dbomJqXD9m5s3xyXBwch3OPDS6dMey8wOB544fhwA8O9WrRBrNPp+Inl5wJgxwJIlgE6H+y+/HADwY1YWPk5NxT6zGUEaDaZddx1w+DAwcaLY7t13K96vezkBH+vIejPn9zl4eBTw16BWkJ5/HnH9h+LWHrfi/Wvfx+77dqNTVCdYHBb8eMSHrFtqEhhwrYGCnQUoPlEMTYAGUaOjvK6jDdKi1aOtMPDEQLR7pR10kToUHSrCgdsP4I+IP7Br5C6cfv008nfms5MtIiIiIiIiIvKb80vOI2NZBiSdhC6LukAb6Hspgb9yc3HbgQNQAPxfXBweS0iodBuNJOFlZy3XpLNncaq42LXszZQUnLFY0NpoxCPOeqs++89/RDZqUBDwww/odOutGBERAQXAA4cPAxAlCsL1eiA0FHjhBbHdjz8CZ89636eilARcq1tOAMD+jP1YfmA5Co1AyMpV4lzdSJKECV0nAACWH1he7eNQ48KAq4+SkpKQmJiIoUOHAgD2Fha6slsjx0RCG1Txm5YuWIdWj7fCpScuRdsX28IQZ4BcJCN7TTaOP3Yc2/tsx58xf2Lfzftw+o3TyF6fDVtWFYo8ExEREREREdFFQVEU5G7ORdqiNNhzvXdQVXy6GIdniGBk66dbI7RfqM/7NzscGL93L4plGWMiI/Fex44e9Vorck1EBIaGh8OqKHjmxAkAwDmLBa84M15fbd8eJm0VasgWFwP//a8Y//JLYORIAHB1nuUAIAH4h3sQt0sX4IorAFkGPv/c+3537gTOnQOCgqBcdRXscvkdfVXk5T9eBgDc2PVGdGvuvSyBGnBddWQVzDZztY5DjQsDrj6aMWMG9u/fjw0bNgAAbjl4EKnLRC9zzSY283k/ulAdWj/ZGoPODkL/vf3RYW4HUf81WAtbpg0ZyzJw/F/HsWv4LmyK2oS/2v6FvTfuxckXTiJjRQbMh82sAUtERERERER0EbLn23F2/llsu2Qbdg7eiYOTD+Kv9n8hZW6KR9lCRVZw8K6DcOQ6EDIwBK1mt6rScb4+fx7pNhtaG41YmpgIncb38JEkSXjFmeW6MD0dewsK8OSJEzDLMgaHhuLmZr7HUAAA//sfkJ0NtGghygo4jY2KQitnWYIboqPRzlnH1WXaNDH89FMRePW2XwBpg3uh08c90GZuG5zLP1elUzuWdQxL9iwBADx5xZPlrtcnrg9ah7WG2WbGmmNrqnQMapx09X0CjZXxiA22o8WQjBKirvVeTqAikiQhqFsQgroFoeU/WkK2ycjfko+c33JQsLMA+TvyUXy8GMUnRctckenaVmPSILBLIAK7Bbr2EdQ9CKY2Jkia6tccISIiIiIiIqKGp2BvAc59cA7pi9LhyHcAELEBQ5wBxSeKceyRYzg77yzazmmL5pOa48w7Z5CzIQeaQA26LuoKja5q+XYfpaYCAP4vPh7BuqqHjgaEhmJCdDSWZ2Zi8sGDSC4oAAC81aGDz5myLl98IYaTJwNumbFaScJbHTrgtdOnXZ11eZg4EXjoIeDkSVGOYMQIj8XW71bAAGB24J84miXmvb7pdbw96m2fT+2VP16BrMgY03EM+sT1KXc9SZJwY9cb8fZfb2P5geUY32W8z8egxokB12oatVm8We2/VIuBgVKN/5AavQZhl4Uh7LIw1zxbjg0FyQUo2FGAguQCFO4rhPmAGXKRLOYnF3juI1CDwK7OIGxiEAI6BSCgYwAC2gdAG1CFdH0iIiIiIiIiqneKouDYrGM4M/eMa15ApwDE3xeP2Cmx0IZqkbYgDSefPoniE8U4cNsBpLyegsL9hQCADm91QGDHwCodc29BATbn5UEnSZgaG1vtc5/Trh1WZmZipzPYelvz5hgY6ntZAwBAaiqwerUYnzKlzOIJzZphQnkZs4GBwB13AElJwMcfuwKudtmOz3+cg+k7dwEAfu6kwY1dx+PbA99i/vb5eOLyJxATXHHnYABwOvc0vtglgsH/ueI/lawtygq8/dfb+N+h/8HqsMKgNVS6DTVeDLhW0/VbxAtjxWA71h84gKXdukFbgx7tvNGH6xExJAIRQyJc8xSHguKTxSjcW4jCfSXNfNAM2SyjYHsBCrZ7BmIhAcYEIwI6BiCwU6AIwjrHTW1N0OhZWYKIiIiIiIioIVFkBUdmHsG5D84BEhB9YzRa3N8C4cPCPbJE46fFI+bWGJyZewanXz2Ngp0iJhA5JhJx98ZV+bgfO7Nbr4uKQqzzlv3q6BwYiLvj4vBxaipMGo2rM60qWbwYcDiAQYOAzp2rvv306SLgunIlkJGBPXIaJq+cjL4/JmM6gH1tg7H6n5vQo3kPDPp0EP4++zfe3PwmXhvxWqW7fn3T67DJNgxrOwyDEgZVuv6ghEGIC45DakEq1h9fj9EdR1f98VCjwYBrNclHigF9MLYNVpCdmYlHjh7FO9VJja8iSSshoL3IWo2+PrrkfOwyio8VewRgi44UwXzYDEeuA5bTFlhOW5CzPsdzh1rA1MaEgA4BMLUxwdTaJIbOZogxsEwBERERERERUR1SZAWH7zuM1I9TAQno/FlnxN1VfvBUG6RF6ydbI+7eOJx+6TSKjhWh00edqhyjKHI4sDA9HQBwb3x8jR4DALzYti3SrFZMaNYMrUymqm2sKCXlBLxkt/qkVy+gXz9g2zYoCxZgguljHMk6gjlH9QBs6Dr1MWhiegIAnrryKYz9cize3/o+HrvsMUQHRpe727SCNHy842MAvmW3AoBG0uCGLjfg/W3vY/mB5Qy4NnEMuNZA5IgIzB8Qi0n79+Pds2eRYrFgfHQ0RkZE1OgqUHVodBoEdg5EYOdANLuxJJ1eURTYMm0oOlwE8xERhHUfl80iUFt8rNjrfiWD5BmEdY4bE4wwxBtgbGFkuQIiIiIiIiIiP1EcCg5NP4S0z9MADdDliy6IvcO3W/sNzQzo8HaHah/7m4wM5NjtaG00YkREROUbVKK5wYDve/So3sY7dgB79wJGIzBpUvVPYvp0YNs2FH+YhCN3nEKkFIDRpwDABs24ca7V1DqsO1J34O3Nb2PO1XPK3eWbf74Ji8OCwQmDMaTNEJ9P5cauN+L9be/ju0PfYb48HzoNw3JNFf+zNdBsQjP0bN4c5ywWPHLsGFZmZmJlpujc6pLgYIyOjMSoyEgMDA2FoQo9+vmTJEkwNDPA0MzgUR8WEMFY6zkrzIfNKD7h7KDrVElHXZYzFihWRQRpjxSVewxdhM4VfNVF6KAN1kIbrIUupGRcG6ItGVeng7SQ9BIknQRJ62w60TQBGmbWEhERERER0UVFcSg4ePdBpC9MBzRA1/92RcytldcT9Re1s6xpcXHQ+OsOXkUBqrOvBQvE8IYbgPDw6h//1luBWbMQcOwULj8NjIsbAMn8G9CypciAdZIkCU9d+RRuWHoD3t3yLv45+J+IDIgss7tt57bh/W3vAxDZrVXJIr6qzVWICohCpjkTv5/6HUPbDq3+46IGjQHX6tLAdUv/wwkJuCwsDN9lZmJ1Vha2FxRgp7O9dPo0jJKEPiEhuDQ0FINCQ3FpaChaGo21Xn6gMpIkwdjCCGMLI+DlNS7bZFjOWlwB2OKTxbCcsriCsZazFshFMuzZdtiz7TDvM/v1/DQBGmgCNdAGieCsJlAjgrIaCdBCDDUQAVqTxvdmrMK6Jo0IAtfz/4qIiIiIiIiaNtku4+CUgzi/5DygBRKXJKL5zc3r7PgHCgvxR24utACmxlW99msZx44Bb74pygLcfDPw2We+B14tFmDJEjFe3XICqpAQyDffDM3nn2P6duCKbs5OxMaOLXM+13W+Dj1jemJ3+m7M+3senh3yrMfy3em7cc2ia2C2mTG83XCM6jCqSqei0+hwfefr8VnyZ1h+YDkDrk0YA67VFHZlGPRRetd0/9BQ9A8NxYvt2iHdasXPWVlYnZWFtdnZyLTZsDkvD5vz8vC2c/14gwFDwsMxLCICw8LD0TYgoH4eSAU0eg0C2gQgoI33c1MUBfZcO6znrLCctcB6zgp7rh2OAgcc+Q4xdDZ7vr1k2m2ZYlegOBTAUXb/cpEsAroX7LX8SCuhgddArDbQmanrbLoQXZWmNUYNA7lEREREREQEa6YVB247gOy12ZB0EhK/SkSzCc0q39CP1M6yxkZFoUVNyiRu3w689hrwzTeALIt5CxYA7doBTz3l2z5+/BHIygLi44ERI6p/Lk7J4/qhz+ef46b9gOn8TjHTrZyASiNp8J8r/oObv7kZc/+ai0cufQRhJnG38IGMAxi+cDiyi7MxqOUgfHvzt9X6TT8hcQI+S/4M3x74FvNGz4NGYkfmTdFFF3DNycnB8OHDYbfbYbfb8Y9//APTp0+v8n4ix5VNK1fFGAyYHBuLybGxUBQFx4qK8FdenqslFxTgnNWKJefPY8n58wCAtiYThoWHY2hEBAaHhqKNydTgg3GSJEEfroc+XI+gxKAa7UtRFECGCMDaFTjMDjgKHZALZTgKHXCYHZDNMhRZBGcV2bm+Q6wvW2TIxf5rilUpOTkZkM0yZLNcw7+YJ0knVRiQ1QaKrF5toBaaAI1r2n28omUaHd+0iYiIiIiIGrq8v/Ow76Z9sKRYoAnUIHFJokcn2XWh2OHAF2lpAIDp1e0s69dfgRdfBNavL5k3ejRwySXASy8BTz8NdO8uSgRURi0ncOedgLbm/cZ8pNuFGc2BHucBpKUBAQHAUO/ZpRMSJyCxWSL2Z+zHe1vew5NXPomjWUdx9cKrkWHOQJ+4Plh1+yqEGEOqdS5Xt70aocZQpBak4q8zf2FwwuAaPDJqqC66gGtISAg2btyIwMBAFBYWonv37rjxxhsRFRVVpf1EjPGteLQkSegQGIgOgYG4I1YUuTY7HNiSl4cNOTlYn52Nv/PzcaK4GJ+mpeFT5xtcM70eA0NDMTAkBANDQzEgNBRhuqb775IkZ5kArQQYRQ+LqNuLeR4UuZIgbpEzEKxm7OY7s3jzK59WA7eKXXGVY6gNkl4qP1Ab4Baw9RbE9TYM8D5fo2dgl4iIiIiIqDTZKsOaaoUhzgCNoezvJkVRcO6Dczj68FEoNgUBnQLQbXk3BHcPrvYxD5vNeOzYMfyZl4f2AQHoFhiIxKAgJAYGoltQULnlDVdkZiLLbkdLoxGjIstPMCvXL78Aw4eLeq1araib+q9/AT17iuUFBcC8eSKA+uefJfO9SU8HVq0S4zUtJwDA5rDhmwPLYegDzFvtnDl8uAi6eqFmud727W1466+3ML7LeIxZMgapBano3rw71tyxBuGm8Gqfj1FnxNhOY7FkzxJ8e+BbBlybqKYbwSuHVqtFYKCo12GxWKAoisiurCJDM0O1zyFQq8WQiAgMiYjAc23bosBuxx+5uVifk4PfcnKQXFCADJsNP1y4gB8uXHBt1yUwEJe6BWF7BAVBV0+dcTV1kkaCNkALbUDNr6SVpjiUkjILFQRoHUUiOKtm98pFJeMey4pkj/Vcx7EpcNgccOR5qdfgR5JOqjhQ6xbU9aihaywZl4ySx7TG6DavgnUlPevr0sVFtjpf64ViqFgVyFYZiq1k6DFuVSDb3MbLuYjkKHKUzfS3KK6yL66hQ9xdoHZw6N7ZoWSQPF/3QW7vA0Faj3FNoKiPDYfzTgX17oXS487jKbICjVEDbagWulBxN4A61Jg0kAwSNAbxnuD+ma4obudvU1x3UZTbvKxTsjPP/0WZ7w5KOeOA550YFs+/cel5skV2/Y2hRdmOJZ1D92WKTXFdDHT/fyp2z7+l6/+nlcT7p7Np9Brfp3WVrGMQn5+aALf3/QCNax7ft4mIqCmyF9iRvTYb5gNmFB0vQvHxYhQdL4IlxQLIgCZQg7DLwxA+NBwRQyMQ3DcYikXB4fsOI/2/6QCA6Buj0eXzLtCFVi9Mk2Oz4YVTpzDv7FnYnd9TMmw2/JWX57FerMGAB1u0wP3x8YjQl5RJdO8sS1vVz2qbDZg5UwRbx48H5s4FWrf2XOfNN4H9+4F164DrrgO2bgWalZNltWQJ4HAAAwYAXbsCAOyyHYcvHEbX6K5V/i6x7vg6XCi6gDWDmkHZkAfJYvFaTsDdzd1uxrO/PYvDFw6j70d9YXFY0DmqM9bduQ5RgVVL2PNmQtcJWLJnCZYfWI7XR7zO70dNUIMLuG7cuBGvv/46tm/fjtTUVKxYsQLjx4/3WCcpKQmvv/460tLS0KtXL7z77rsYMGCAz8fIycnBVVddhSNHjuD1119HdHTdpuqXFqzTYVRUFEY5s2yLHQ4kFxTg7/x8/JWXh7/z8nCiuBgHzWYcNJuxwJkFG6DRoE9wMPqEhKB3cDAuCQ5Gt6AgGBiEbdAkrQRdmA66MP+//BTF+aPeWzDWPWBb3rLCioO5rmGhwxVQUOwKHHm1H9j1SoLPwdkygVyjCPgokgLjaSNObz0NrVFbEjyqatP7uJ7a8ZumpOM3SCgzz2MogR/A1aAoCqA4S5AocAXuILuVMSlvmUN0HOgKwtk8A3KuIGap5VWZL1vlkoCpc+jxWlRLqzhfc7JZ9gwAUrlCdaHYpGzyWh+c6pkGHgFY98CsxzyjZ5C3wqCv+h6sznObLrO9D+t6XEhwn9bwfZiIiErY8+248MMFZCzLQNZPWZCLyylBpxEl6rLXZCN7TTZO4IS4eBymg+WMBdAC7V9tj5azWlbrO79DUfBpair+c+IEMmw2AKIG678SEpBmtWJ/YSH2m83YV1iIw0VFSLNa8eSJE3j59GncGxeHR1q2hFmW8WtODjQA7nbemVslSUnAgQNAdLToGCvCyx3BOh2wdCkwcCBw9CgwYYIIvhpKJbMpCvD552L8rrsAAIcvHMZty2/D9tTtePrKp/Hc0OeqdHpf7v0SADCi3yRIr3YA1q4FJk2qcButRosnr3gSU1ZOgcVhQbuIdlg/eT1igmOqdOzyjOowCoH6QJzMOYmdaTvRJ66PX/ZLDUeDC7gWFhaiV69euPvuu3HjjTeWWb506VLMmjUL8+fPx8CBAzF37lyMHDkShw4dQvPmove+3r17w24ve5v2mjVrEB8fj/DwcOzatQvp6em48cYbMXHiRMTEeH/RWCwWWCwW13R+fj4AwGazweZ8M/M3LYC+gYHoGxiIB5zndd5qxdaCAmzJz8fWggJszc9HrsOBTXl52OR2xUovSegaGIgegYFobzKhfUAA2ptMaGcyIUqnY9DmYqAFEAJRBxb+z9AFnFljVsUzA9ct89aVkes27Sh0iGwui+xqSnHJtCv7ziJ7rOcxXlw240wuloFiwFGDyIoJJqQgxQ9/mVokofyArFtgttJ13AO8knO/vhze1/cOX1ZTvAQ8lZKAp/t4mYBoJcvcA6dNmhbQBmrFBQRDqUxD92mDM6hkcAadTCUZh66LECbJs1NAtwsWXjMqJakka1RtDkW8VtX3gcJS7w1uQWS5SIZcKItsSw1c2ZtqWRlJU2rcuY5skV0XdxwFDtjz7HDkOTxrbjtJ9kqeiBr4drFEK5V9Tnvbdal5ZV4vziCjx8Ug97+1Sfzf1HnQlNQohwMeGcaKQwHsbvPsiquEjMaoKRuwLP231UBcULCXZES7N68XDUovc88ELr3c6pZt69ZcZEAuFM+BRkdCyetBh7LBWS/Zx14vzqmvR0NJVrDHa9cgQaPTuOZ7vI7V56v7+7oWHq8Vj/f9Uq8jr9uUHlZ3H6Wn3T5javL9U/2+XVvfu4mo7jXm17UlxYLcX3NxYeUFZK/JhmIp+R5iam9C6OBQGNsaYWprgqmdCaa2Juib62HeZ0bub7nI/TUXeb/nwZ4t7m7Ux+rReXFnhF0R5jWGUZmdBQWYfvQodhcWAgA6BwTgjbZtMVINeAYF4Qa34KdVlvFNZibeOHsWe81mvHXmDOadPYv2JhMAYFREBGK12qr9b9LToXvmGUgA7C+8ACU4WGS8ehMSAixfDt0VV0D6/XfIDzwAx/vvA5mZQEoKpNOnIe3bB+2ePVAMBthuvBFfbPsYD695GGabGQDw0h8v4YbON6Bbs24+nV6RrQgrD64EANzU9SbYRgwCHnhALKzkcd7U5Sa83+J9XCi6gFW3rELzgOZ+e97qocfIdiOx4tAKfL33a/SI7uGX/daHxvyaVtXGuUtKde6nryOSJJXJcB04cCD69++P9957DwAgyzISEhLw4IMP4oknnqjyMR544AEMGzYMEydO9Lr82WefxXPPlb16smTJEldpgvogAzir0eCoVosTznZcq0VhBV9oAxUF0bKMEEUpabKMYEVBkKLABCBAUUQDYHJbjzmz1GDIAGyiSTbJ69CnZXYADkByiNuaIVdx2uE2LcNznuy2jlxqG2dAUVJ48aOhUjSKCBJoRVN0imvcNa0BoAMUrdsynXNbnY/r6pWScZ3YTjEqgAFQTApgFEPFoAAmsUwxivnQezvzi5QC8XpWX9N252tdC/G315b622ucjeqOAvE/sQKSVXINJasEWDznwQpIFsnz/ynD67TkcK7nZVqyl3qvdp+2e74veyyTne/hVGsUSfG8SKEGZtX3RR1K3hf1pd4P1XG9UvJadl5sVDRl56nNtS+jc2ioYGhw7t8A8V7LpwNR06AAUrYEKUeCVORsZgkoAqQicbFICVMgh8tQwhXRQhRImRJ0e3XQ7dNBt1cHTbrnlwhHvAO2wTbYBtsgt5V9e89wAJpTGmjPaWHvbocSXr2QTIpGg9lBQSjQaBCkKLi1uBijrFafsuoUADt1OnxrNGKvW18x/y4sxIAqBn57v/suWq9fj5z27fHba6/51MFV823bcOmcOZAUBQ69Hlovwa5Tgwfitpu0+DP3TwBAj+Ae0EpaJOcno2tQV8zpMAcaqfIvdX/m/InXTr6GZvpm+DDxQ5+2KU1W5GptV5mN2Rvx1qm30MLYAu91eY8JcvXIbDbjtttuQ0pKClq2bOmXfTa4DNeKWK1WbN++HbNnz3bN02g0GD58ODZv3uzTPtLT0xEYGIiQkBDk5uZi48aNuP/++8tdf/bs2Zg1a5Zr+uzZs0hMTMTQoUOr3NFWbVMUBacsFuxy3jJwvLjY1c5arTBLEk5Xo3c/nSQhRq9HjF6PWIMBcQYDYvR6MXSbjjUYYGI5A2pkbDYb1q5dixEjRkCvr7solnq7u8et7BUNfVzX23YVblPtB1CD7UqXUHDPzC01LknlZ/B67Me5nse25ZVsKH0M9/X4JafRq6/XNDUdiuzMJFazi91rGdtLZR6XrnNc3rJysoFL11yudJmzxq/rfVyte6y+/7tNu9dALjOteE6r46Wny9tXtT8D4Lzg6G17OyAVN7z3YFdGuvtdAAFu2emmko5F1eatjr1av9rr8kDn/li2gi4S/v6sVhQFjlwHrOlW2NJtsKZZYT1rRfHJYlhOWFB8ohiWU5byb/kvjwZl75rSAsGXBCN8RDiiJ0QjsEdgvXx/PGux4MHdu1FgtWJgSAhWdO2K6Cr+La8F8B8AW/Pz8e65cwjQaPCfDh2gq8LjkbZsgW79egBA8OefY8yll/q24ZgxkENCoH38cVewVYmLg9KqFZCQgNORWtwUuxHbclOh0+jw3FXPYdbAWThXcA49P+yJA4UHkNEiA1N7T630UAu/XQgAmNx3MsYOG+vzY6sLl1suR+Dfgbihyw3o3qx7o/0t0hS+f+eVqnXsD40q4JqZmQmHw1Hm9v+YmBgcPHjQp32cOnUK9957r6uzrAcffBA9epSfum00GmE0Gl3T6j9Br9c3yCdSR4MBHUNCysw3Oxw4UVyMcxYLLthsyLLbkeUcXrDZkGu3o8DhQL7D4RrmOxzIsdthVxSctVpx1moFnLcqlCdMq0VzgwHN9Xo0dwZk1fHSwwidDppG+oZCTU9DfU0TUfXwNU1Ue9zLwpQbnFVvolNKmrd5gDNQ7KW2taPIAWueFTs27UCPTj0gFUuiprVFLjmOewd75cyTrd47DFQ7mis9zz0grFgUOCwOOHJrvyi0185Hg8p2RuptnkdHhd46LAx0dlzHoC41FA5ATpVhTjfDkmKB5YyznbPAnmOHPdcOR64D9lw77Hl2yGa5pPyK2ieDQQNFVmBNt3rc2l8uDWBoboA2TAtdiA7aUK2opRqig2yTRbD2vBXWdCvsF+wi2KoFQvuHIuyqMIRfFY6wy8Kq3amVv+TYbLjuwAGkWK3oFBCAH3r0QHTpOqhVMDgyEoMjI6u+oSwDjzwixidPhu6KK6q2/WOPAWPHAiYT0KIFJKMREoBVR1Zh7JKxUKCgY2RHLJmwBP3i+wEA2hnb4fmhz+Ofa/6J2Rtm44bEG9AsqJyOtwDkWfKw6ugqAMDtPW9vcN8No/RReOHqF+r7NPymMX//ro3zblQBV38YMGAAkpOTq7xdUlISkpKSGm1NikCtFt2CgtAtKKhK29lkGelWK9KsVqSWN7RYkGq1wqooyHU4kFtUhCNFRZXuWydJaKbXlwnENtPrEajVwqTRIECjgcmtlZ5W56nrM4BLREREVLskqaQ2cG2XOLHZbLBpbYgdE1snP+IUxdnZYXkB2VLNayejhQ6vHY56W8c9SKTWva5NmoCyWbZeA7ilM3TVeQGeAVyP9dVMX5Om0WZpUeUUWYHtgs0zmzTdCluGraRmuVtHiJJegu28DZYzFhSnFIugaooFoWmh2CZvq9qxrQocVgdQ4H25NlQLQ6wBhlgDjPHOOqptTQhoFwBTWxOMCUZo9L7dkSnbZNgybNCF6aANqp1+Maqj2OHA+L17saewELEGA37u2bNGwdYaWbAA2LpV1GV99dXq7SMxscysuX/NhQIFExMn4vPrP0ewIdhj+UMDH8Ki3YuQnJaMR9c+ii/Gf1Hu7r8/9D2K7cXoHNUZvWN7V+8ciaqpUQVco6OjodVqkZ6e7jE/PT0dsdXpSa8KZsyYgRkzZuDMmTNISEio1WM1JHqNBi1NJrR0FtEuj6IoyLbbkWGzId1qxXmrFedtNo9hutu0mjmb6gzcVpY566sAjQaBzgCsUaMR5fokSdxpLEnQuo2XHhokCSFaLYKdLUSnQ7BWC6NzO1dz25cWcM3XOMeNznMI0Gpd56OOq8FhgyTxiygREQBZUWBXFNgUBTZZhk6SEKjV8gIaEdULSXJ2YqbXAGVvGvM7xaG4OhktE5Q1O7zOc++Y0Jcgr/tt1GpHdvYLVe+cpyo8grEBmjKZtt6WeVuvoqCvJkADjY7lzGqLo9iBosNFMB8wo/BAIcwHzKIdMnvtsLKqJIjXmrGFEcaWRhgTxNAQb4A+Qi+yUMN0rqYJ1IiLIWqnulbRqa4kSdDH6GGIMUAb4L/AqEavgTHeWPmKdUhWFEw+eBC/5eYiRKvFTz16oE1AQP2cTE4OoPah88wzgJ/iMWkFaVh/QpQoeHX4q2WCrQCg0+jw4dgPceknl2LhroW4q9ddGNp2qNf9fbn3SwDArd1v5e9vqnONKuBqMBjQt29frF+/3tWRlizLWL9+PWbOnFmrx27sGa61TZIkROr1iNTr0dmHzsSssowMNRBbKjibYbOhWJZR5HCgWJbLtKJS01a3ft+KnMsvVKOHx7okAa7ga6CXYVA58wOdweAgdajRuKaD3JYFaphZQER1T1YUpFmtOFVcjNMWC04XFyPVasUFm000ZxmbCzYbCh0O2BSlTGk0lcl5wUp9T2tuMKC1yYTWRqMYOltbkwmGi6B+uKIoKHSW+slVh85mVRTIzr+lL0Oj2x0iAW4XBSN1OkTp9Yhy3mlCRLVP0krQBeuAsjEFv1FkBXKR7wFcR2GprN2ikuXu46WXKTa3bN06CuxKBqlMMFYbrC0J1oW7BeyCxC3okqHkdnTJIFU+NHqZbsDfsxVZgT3XDtsFG+wX7LBl2VzjikMp+ZuE68TfKVwHe5YdhfvdgqoHzCg6XlS2fqkbXZQOhhiDq+mbiQx0NTvcUeSAXCyCo/pmeldA1djSCG2cFn8c/AOjbh0Fg7GesjMbGUVR8MjRo1iWkQG9JGFl9+7o7aWUYJ159lkgIwPo3Bl48EG/7farvV9BVmQMajkI7SLalbvegBYDcH+/+/H+tvdx34/3Yfd9u2HUeQbIL5gvYM2xNQCAW7rf4rdzJPJVgwu4FhQU4OjRo67pEydOIDk5GZGRkWjVqhVmzZqFKVOmoF+/fhgwYADmzp2LwsJCTJ1aebHkmrhYM1xri0GjQQujES2MNb9q6FAUFDkcMMsyzG7DYlmG4lxe+semA2V/hBbLMgqcNWzVlm+3w+L8IetwbudQx92m1X3aFQUWZ9C3yOFwBYDNznH1O4sCiPOUa+e2MQlAoFsw1uuwnGBt6WXBWi0C3TJ0TQzmEl3UrLKM487SMa7m7KjxjMUCm1LzrBcArotqWc4LaIeKivB7bm6Z9XSShE4BAejuLJvT2WjEBY0GNln2253OxQ4H9hQWYmdBAXbk5+Oc1YpWRiPaBwSgXUAA2jkDv8G6mn2tUhQF+c6a6wfNZhwym3HQ2Q6ZzbX2meFNgEaDKL0eETodtG7v+aU7ly89rnHe6WHSaGB0Gw/QaBCm0yFcp0OYs4XrdIjQ6RCt1yNar0ekTgedD8FzxfkZbFEUWJ0XXmVFEZ3dS5Kr03sJQAAvQhJB0kiinmuQFii/1GGNyXbZt0BtkWfQt9xlpUs1FJWsr1JvMa+LGrsqSS+JoGW4DroIZwvTQdJInnWNy6sv7Kx3LOlLsqklg3PcpIE+Sg99tB76ZmJoaGaAJkgDe7ZbEPWCzdVKB1VtWbYKA6VVoQvXIbBrIAK7BiIoMcg1bmzp+6353thsNijpykVdU9jqLN0XbzR6fM6WpigKfsvJwbyzZ7EiMxMAsLBLFwyLiKirUy3r6FHgvffE+Lx5gB9LGizesxgAcHuP2ytd96WrX8K3B7/F4QuH8dqm1/DUVU9BURRcKLqAs3lnsXTfUthlOy6JvQSdozv77RyrKzMTCA8Havh1kRqRBvev3rZtG4YOLUkHnzVrFgBgypQpWLBgASZNmoSMjAw8/fTTSEtLQ+/evbF69eoyHWnRxUMrSQjW6WozMcAvFOcts+4BWNewVLC40EsA2SzLKHQuK3A4UOgMELumnesAIqBbKMsolGWgFrKyjZLkUSbBPUOqvMzdoPIyecvJ7g3Qaiv88kFEZVllGdl2O/LsduQ7HCVD5/uERZZdASp1aFcUaCUJOmfTQgQxZcB1J0LpEjEV/Y7TAmjhzEJtZTQi3mgUWZPO7MloZwZlsFYLvSSJptFA5xy3K4p4H3R7PyxwOJDqljl7qrgYp4qLcbK4GIWyjP1mM/abzSLTAgBCQvCPv/5CW5MJnQIC0DEwEJ0CAtAhIADRej1CdTqEarUI1elgdAb4zA4HUiwWpBQXi6HFguNFRdhZUIB9hYXw5ae8+hgjncFDdRik1bou7rlfBMx3OFx/V/Vuj+JKgqo6SUKYVotwt+ClUaMpt1yO5GXa4nbHiHrXSKHDgSxnFrLd+Vl1xmLBGYvFh0fuP2oA1qTRuJ6jFrfnrDpelbC+BJSUC3IvHVSqjFCgRoMiWUae3Y48t9dPvtsdNxa1Oc/H299cJ0kexwhxO1aITuc57fwcLF2mqKJpg9tnr8ntM5jliqi+aXQaaEJqvxSDojhr7HoJ7DrMDjgKRABW7XhJbbJZLtMxm2toKWe+c6jYS951FJsCW4ZN1CxtwDRBzuCts+midJC0kvh75NhL/kY5dmhDtCWB1a5BCEwU44YYA99Xasgqy/glOxv7zWYcLSpytVPFxZABROp0GBERgZGRkRgZGYl4Z0JSnt2ORenpeP/sWfEdx2luhw64pb5jHy+9BDgcwOjRwDXX+G23hy8cxrZz26CVtLi5282Vrh9mCsPckXNxy/Jb8MLGF/BZ8mc4l38OVofVY72GkN167BjQtSvQrh2wYoUYp6avwQVchwwZUtKDaTlmzpxZ6yUESmNJAaopyVkn1uDM8qkNslugonQwtqCSYG15ywocDpgdDo9gg0VRYLHbkVMrj6KETpI8MqSMzla6Nq9aP9eg0bj+xsZS0wbnfkrP0ykKjhgMOJeWhgCdDgZn4EcN/pQe11eyvMy4MxDCL6tNn5p1pwbVvGXBq/M1bs8R92bzdgFGlpFjtyPLZkOW85Z8ddx9eMFuR4GjbjJ8gjQadAwMRMeAAFfrEBCA1iYT4gwGn7IUy2OA6Ogx2od1FUXBGYsFewsLsa+wEHvVlp8PC+DKwEVWVvnHkySYNBrkVfK3i9Lp0CckBH2Cg9HaZEKKxYJjRUU4XlyM40VF4n/jbPCh48iKROp06BIY6Gqdna2l0Vjr2Zpqlq1a+iHbbncFN5VS68HLfDXz1D04qQZ0cx0OVxmEXLtdPK/V57Qzkznbbkd2NcoCaeDR6b3nYwJcgdPUKu+5YrL6dyj13TWnHkobSYBHB6NqINZUToC2vPmlL76oF2TchxUtq2zb8pazbjP5SpIkUTogQAt9VN30hq3IChSbCPTa8+2wZ7s1Z9ASAKBxdibnHHqMa9w6m5MAxa6IYK7NGdi1idIPtgsimGvLtMGaYYUt0wa5UIYuQucKnLqCqJG6MkFVfZQe+kg9NMamX26nITtsNuOT1FQsSEtDRjkxBAlAlt2OpRkZWOq8aNzDecfO/zIzRfIMxPeuO2NjcX98PHoG13Oa0YkTwMKFYvzpp/2668W7RXbryA4j0SzIt3T8m7vdjC92fYGfjv6EkzknXfObBTZDi9AW6BLdBff2vdev51kdmzaJPKhDh4ABA4BFiwBnlUxqwhpcwLWhYkkBagw0btm+/r7uaXfLhipdMkHNkFLnlZed63Wel3Vdx3Rm3xXKtXwbbUCAuOxYi9RAbHkdt1XUoZsvnb651oFnp27ext2PVzqQoqAkkKJ4W+6WWeYeXnDt04dzLH2u6nTpDEBHqQCm7CWA6V7uQ90egMctxe63GGskqdxl7tMK4BEscg29BJKqk3FXm9RsvlBnNl2oWzadye2ig3oBQidJcDhfa3bn39PufA400+vRzGBAc70ezQ0GNNPrEWcwINbQMDJeJElCgsmEBJMJo6OiAIjbFH9YtQq9r74aJ202HC4qwmGzGUeKinCsqMhV+1R9X7EqCqzOYGuwVosEo9HVWptM6BkcjD7BwWhpNFb4mHNsNpyxWJDtHkR0BhLNDofH60N9/QVqtYhx+/vGOP/eQfVYP1WSJJEBrNOhbR12xGF3ZmdnOGu5W2VZPEdLXSwrPU/t2LL0/0ZxKxeU77wYUeAMupY3XuhwIFCjcWVAq6+fYOcdHKUv/hkkCQrKliiyKYqrLFG+2/7zy5lX5Lyo6a1kkfu0+vq0OUspqZ+/7u/J6rzGSALKDcjaQkIQvHUrtM73LK37ujUI9nr7TADK/wzxxzRqYZ/u096ekw5nyQ1vfxOTRuO6CynIeUdSkFYLg/MCt85tXa3zs9rupXl7XBpJQoBzfyaNplEH1SWNBMko6rfqwnRAy/o+I2qIih0OLM/MxMfnzuE3tzJIsQYDrgwLQwfnBeoObnfdbMnPx89ZWfg5Kwtb8/Oxp7AQe5wdSncNDMQD8fGYHBuL0Nq8D11RxMVpoxGoLKD7yisiu/Waa4BLL/XjKShVKiegkiQJSycuxa8nf0VkQCRahLZAXHBcmXqu9e3IETHU64GCAuCGG4D//Ad47jngIuiK4KLFgGtdy8kBkpNFcKd9e+CSS4CwsPo+K6JK6TQahGg0td5hr+wMaKl1eC3O20ndb+P09kNC/RHqfutp6VtQPeY5h8UOB06dPYuo2FjYIG77cf8BYfNl3G2b8nLk3H+Q0MVLDRIoQIW1TkuX5AjX6TxuUY903qJf+tb1SL0e4Tpdo/5R6w8aAC2NRrQNDsbQcmqcORQF+c5bx4tkGTF6PcJ0umoHksP1eoTr6ybTqinSaTRoZjCgmZ/qwEnOwJpap7ypci9X5H7hs3TJiKrMd7/wUmYI1Hh5uY8FFXxWajTItlrLzqdGJcDZGWKwVutxMS/GOYzS6113KOmlsncqueZ7m+ccXuyff+Qbh6LgkNmMbfn52Jafj+35+bhgs0HvfB65P6c6Bwbi1ubNcXlYWLnPr70FBfg4NRWL0tNdd2loAIyOjMT0+HhcGxlZ7t0/l4WF4bKwMDzfti0yrVasy87GnsJCDI+IwJDwcP9f4D58GPj+e+DkSc9WWAhERwN//y3ue/fm9Gng88/F+FNP+fW0tpzdgmPZxxCoD8T1na+v0rYhxhCM6zzOr+fjb2o3Rc89B6SnA++8A7z4IrBjB7B4sajtSk0PA64+qlZJAYcDWL8e2LoV2LlTtOPHy67XsSPQty/Qpw/Qvz9w2WXi0gfRRUgjSSLQVEc/kG02G1YdPYoxXbtC74fXnZpx6S04a3N2nOatlmOZjt3KWebLNpVliLqvWzrjBqXnlZpfZrkkuTLJvJ5TOfPdz0kd9zVD11u2rnv2L1CSietqbtNyBcvUaUkS5Szcs9lM7kO3ZSbnF3L3Wovu5+XK5PXyhdk9U8imKK7b2xtC9mhTp5UkBkmp0fMoV1TfJ+MDxe0939eAbZHNht9+/x2DLrsMkk7n14Cw++cBUP5ngi/TqMY2/t6HytsdJ2pHsurjV/8WasezanmpQue4zW1db5cHJXhmv8LtHBS341ndzksN6l+w23GqlupD6yQJgc5O/9wvSEbp9YgxGNDGZHK1+BqWwKHG5XhRERalp+OX7GzsKCjwuQzThpwczD93DglGI25t3hy3xcSgZ1AQzLKMpefP4+PUVPyVl+dav5XRiHvi4jA1NhYJJlOVzjHaYMAtMTGotYqja9eK+9jdasJ6yMwEJk0C/vhDZLuW9uqr4r74oUOByy8vs/h07mnMWDUDidGJeH7o81XKMlWzW8d3GY8gQ5DP2zUWaoZr167A7Nki/HPvvcCqVSIEtHIl0K1bvZ4i1YIqBVzbtm1brR+BDz/8MB566KEqb9eQVKmkgMMBfPUV8MILokhHaa1aiSDrkSPiKtGRI6J99ZVYHhEh3ggnTgSGD/drr39EVLvUwBpDOOQLjRosqe8TISKqA2rmsbYK73s2mw0psoy+ISF+uTBKVecq36MoJRcWffxNKCslHcaqpaTyHQ5kWK1IL9Uh4wWbzXUnks0ZrHW/O8lWatpbOR+7oohO7xwOnKjk3LQAEkwmV0d9vrYQ550nYWrnhc7paL2eAdwGJtdux7Lz5/FFejr+cLvFHxB3E/UJCUE/Z2tpNLoSJNTnX7EsY312NpZnZCDFYsFrKSl4LSUFnQMCcM5qRb4zaKuTJIyLisL0uDhcExnZMDv+XbECuOUWwGoFBg4Ehg0D2rQpaYAoEbBtG/D448DcuZ7bnz0LfPKJGPdSu3Xf+X0Y+d+ROJt/Fj8c/gHrTqzD1xO/RvvI9pWeml22Y+m+pQCqVk6gPlkswI03AjExwGefVbyuopQEXDt2FMM77xQB1htuENmvI0eKm6C9xbmp8apSwHXBggXVOkgb9QXc1HkLtEZGAqNGidIBl1wC9O4NOOvMARA9Ku/YIdr27cDvvwPnz4tU/c8/F+UGrr++JPhah/XUiIiIiIjo4laTi8kaSXKV9vCtC5yqcXgJwhY4HGU6l7xgs+GcxYKTxcU4WVyM0xYLbIrimvYHCUC0Xo9YZ61ztbV2y6ptbTQiuDZrcTZSDkXB/sJC/J2Xh72FhShw6xdCLX0iAegXEuK6Bb+5l6QkqyzjoNmMPYWF+PHCBazIzESxs661BGB4RARubtYMl4aGomtQkE+B0dtjYvB+x474MSsLS9LT8cOFCzjk7ByzvcmEaXFxuCs2FrENOVK2YAFwzz2ALIu4wn//6z2yt2CBiD288w4wZIhnr06vvy6CtZdfDlx1lcdmf6b8ibFLxiK7OBudojrhgvkCdqTuQJ+P+uCTcZ/gpm43VXh664+vx/nC84gOjMaIdiNq+mjrxOLFIjsVEH8a9xBPaRkZQF4eIEmiqqSqTx8RArr1VuAf/2CwtSmq0rv9VaVeWOSkKMCXXwLPP+8ZaH30UWDmTCCkgqqXzZqJyxkjR4pph0Ok8C9bBixfDqSliV4AFy4EAgNF0PW664BrrwViY2v/sRERERERETVAWklCgFaLqqakOBQFaVYrThYXI8du9+ggs6JW5OyIL8fZch0OV2eMCuDq+E/t9MibaL0eCUYjAr10ZFm6Pq06NGk0CNfpEOHMqlWHaqmEBplRWYFcux2bdTr8fvIkthUUYFt+vk+d5P6Wm4s3z5wBAHQICMDlYWFoZzLhgNmM3QUFOFRUVKYOdNfAQEyJjcUdMTFoUc2IlkmrxYRmzTChWTPk2Gz4OTsbMXo9rgwPb/h1g995B3j4YTF+zz3Ahx8C5ZVuu+464JFHgLffBqZOFQljrVuLmMSHH4p1nn5aRA6dfjz8I25adhOK7EW4tOWl+OHWH1BkL8Kty2/FH6f/wM3f3Iz7T96Pt0a+BZPOe4kFtZzApG6ToNc2/DsZZFkEWVX79wNXXFH++mp2a0ICULrKRHQ0sGaNx5+UmpAaX1575JFH0LNnT/Ts2RPdu3eHsYmG5cut4epwAP/3f8Cnn4ppXwOt5dFqxRWjq64Sb45//imCrytWAGfOiALX338v1h0wABgzRgz79gWaN6/ZgyQiIiIiImritJKEFkZjtQNwpTkUBRdsNqRZrR7tnMWC026Ztdl2OzJtNmRWpV+QSmgBxBuNSDAa0dKtuU/HlqpXKysKcu12VxZwrt3uqt/rXs9XAjzq16vjLY1GJAYFIawK2bopxcX4LjMT3124gF9zcmAPChK3qTsFa7XoHxKCviEhiNDpEKDRiKbVIkCjgdnhwF95efgjNxf7zGYcLSrCUWemqbswrRY9goPRLyQEtzVvjn4hIX6tjR+u12NSY/jdrSiih6bnnhPT//yniBJW9rd45RVg0yZgyxZRgmDjRuDNN4HiYlFyYPhw16pfJH+Be76/Bw7FgTEdx+DriV+76q9umLIBT294Gi//8TI+2PYBNp/ZjMU3LkZis0SPw5ltZqw4uAJA4ykn8MMPwMGDJdO+BlzVcgKlMdjadNU44Dp06FDs3r0bP/30E/bt2wdJktCtWzdXEHbcuIbdW5yvvNZwtViA224Dvv0W0GiAZ54RV4SqE2j1RqsVr9wrrhDB1127RLD1f/8TtVW2bBFN1aJFSedbPXuKWrEJCSKLlq9iIiK6GMgyglNSIG3YAOTmig4gLlwQw/x8oG1b8RnZq5f4nKzq56PFAmRni899nU58Vmu1YlyvLz9rxBcOh7jnLCxM7J+IiBoFrSShucGA5gYDelawXo7NhlMWC85YLLDIcplyCFZZhsVLmQSzw4Fcux3ZzsxadXjBZoMDQIrFgpQKOiLTQARljZKEbOf23jpDq6oWBgO6BQUhMSgIiYGBMGk0sDrroKrnn223Y3VWFnYUFHhu63BgdHw8BoWHY2BoKLoEBlaaqXtXXBwAINtmw+a8PGzKzcUZiwVdAwPRIzgYPYKCkGA0svNRQMQmXnhBjL/4IvDvf/v2ncdgEGUSL7kE+Osv4P77xd28APDUU659vPnnm3h07aMAgMm9JuOTcZ94ZKfqNDq8dPVLuLL1lbhzxZ1ITktGjw964I6ed+DpK5921Xb9/tD3KLAWoF1EO1za8lL/Pf5a9OqrYhgYKPof27+/4vWPHhXD8gKu1HTVOOB63XXX4brrrnNNFxcXY+/evdi9ezd++eWXJhNwLSM/X/Tgt369eFP68ktRNbm2SJKo/9q7t0jjP3dOXFr59VdR//XwYXGF8OzZkgxYlcEAtGwpgq8JCZ7j6nRUFIOyRETU+CiK+Ka7YQOwYQN0v/2Gqy9c8G3b0FARfO3WDQgKEgFTna4keOpwAKmp4g6TM2fEZ2xGRsX7DAsTd7u4t4AA0atv6WY2Azk5JS0/X+yjTRtRR42lnIiImpRwvR7hej16BQf7ZX92WUa6zYaU4mKccQZy1ZbiHJ6zWmFXFJzxEpAN1moRqdMhVKdDkEbjqrcbpNUiyHnhr9gZBFbLLpgdDpwoLsY5qxVnnW1Ndnal5yoBGBwaiuujozEmPBxHN2zAmA4dqtUZXoRejzFRURhTUeHMi9nevcBLL4nxd98Vd99WRdu2oj+ZG28suZO3b19g9GgAwO703a5g66ODHsWrI16FRvJ+oXhUh1FI/r9kzFg1A98d+g4Ldy3E4t2LcVfvu/DUlU+5ygnc1v22RhEo37RJ3IRsMABPPilaZQFXNcO1Q4faPz9qWPxWsdtms2Hx4sXIyMhAYmIi7rrrLmiacHaGdsIEEegMDgZWrgSuvrpuTyA+Hrj3XtEA8SMtObmk862DB4GUFCA9XRS3Pn5ctPJIkrhEExRUMgwKEpWb1R+f7i0gQPxQDQnxbAEB4t3HYBDbquPqD9CICHb8RURENWO3i4JXixeLC5/p6a5FEgC7yQRtmzaQmjUTFxSjokSRrKAgcYFy927gwAGRTfrHH6JVhSSJQK83ubminaisf+wKnDwJDB0KPPEE8Oyz4nO0NikKcOoUEBfHHhuIiBoRnUZTaWkEh6LgvNWKFIsFVllGlF6PSL0eETodDDX4vZ5js2G/2Yz9hYXYbzbjoNkMh6KUqUFr1GgwKDQUY6Oi0Mz5eWaz2XC02kemCimKqNnqcIiAaVWDraobbgAefFAEbAGP2q2f7hBB2Bu63IDXr3m9vD24tAhtgZW3rMTWs1vx9K9PY/XR1fh056dYuGshZEXU7r29Z+MoJ6Bmt06ZIkJATz4J7NtX8TaVlRSgpstvAddbbrkFcXFx6NKlC3744Qc8+eST+Prrr9GpUyd/HaJB0ezYIX7A/fQT0L9/fZ+OCHaq5QfcWa0iGzYlRWTnpKSUNHX6/HnxxlxYKFptM5lKgq/uQ3U8PLwkWKvXlx1WZZ56e2cjuFpGRESV2L8f+OILYNEikXmqCggALrsMGDoU9iuuwKrz5zH6uusqzpqxWsXFyd27RRDWYhFZp3a7aGp9vfh4UbKnZcuSYWSkWCbLYl2HQ7SiIlFuICvLsxUXe34uqS0gQHzuhYWJz77wcHFR81//EhklL78MrF0rAsu18X3KahV36LzxhsiGMZmAQYOAK68U2bWXXlq7F0nVvx9QEsBWhwYDyyoQEfmBVpIQZzQizs8X1ML1egwOC8PgsDC/7veilpNTcjG5bVugXz8Ra2jf3vffs99/L7Y3Gj17dqqO118XsYKAAMB557LFbsF/9/wXAHBv33urtLv+Lfrjp9t/wp8pf+LpDU9j/Yn1AIA+cX3QJbpLzc61DuzfL6o7SpIoieuscIHUVPH1LyKi7DaKwoDrxcxvAdfjx49j+fLlrunk5GRMmzYNGzdu9Nch6lXpTrOUuDjxRta1az2fWSUMBnFrYps25a9TXCze3M3mkqCrOm61lvzwdP8RajaLrNrSrbhYbKM2i0W03Fzxo1OWxTrnzolWV9TbREv/2K3uvOpsFxAgWmCgaOq0weB5C6taE1CWxQ94dVhek2XRNBrPWoJardifmrFc2xlSRES1obAQ+O9/gc8+86xbHh0N3HGHyMAYONCVmanYbFBWrap8vwaDKCfQs6Jqe5VQ33NVISH+6cDyk0/EbXvTp4ua7ZdcImq533OPfy4g5uaK3obfeafks1iSxOezszQDAPE36tdP/EJo3dqzxceLz57SZRIKCkpKMKgXd8+cEaUYzGbP5qWzExetFoiNLQl4x8eL1qqVOJ8OHSovh2Szie8iiuLZAPG/YkCXiIjqm6KIWqkffQQsXer9szE8XHweDx4s+owJD/e+L4tFRAIBYNYsoF27mp2b0Sjqubr57tB3yCrKQsvQlhjRbkS1djs4YTDWTV6H307+hiV7lmBan2k1O8868sYbYjh+PNC5sxhv2VJ8zTlwQPx7SktPF1+NNJqa/zuo8fFbwDUkJARHjx5FB2dhit69eyPbh1oujUXpTrPsP/7oCrZuPLURU1ZOwdA2QzFv9DwEG/xTk6fOmEziR01tk2URlFWzf0oP1fHsbPEDSf3xpo77Mk/9YVWauk5FP+6aOp2upFREaKgIVkRHi07V1GFIiPjxqjag4mn3QLJ7Cw0tKR/B7GIiqo6zZ4H33hOBQfX7hE4HXHstcNddwJgxTftC0oQJIpA8ZQrwyy8i+LpyJfD229VPkcjMFL0Pf/RRSb3YuDjgH/8QJYrS0oDffhM9Ev/2mwjG/vmnaHXN4SipTb91q/d1wsJE4LVDB/FL5sKFkpaZKX7hlEevF4/dPaDbooXneHx8+R2h2mzih616sdN9SEREVBmrFfj4Y/E9Z8+ekvndugG33io+k7duFWUDc3KAdetE++EHcfeLereNu3feAY4dE59vs2fXyml/tvMzAMBdve6CVlODjkIBXNXmKlzVpnHUqz9zRlz/B4DHHy+Z362bWLZ/v/eAq5rd2qoVqzZdjPwWcH3vvfdw/fXXY8yYMUhMTMSBAwfQunVrf+2+4WnVCgCw9/xeXPfldci15OLz5M+xKWUTlt20DD1japAx01RpNOLHUVhYxRm3NeVweO+cRM3Ora15Fa1jtYrMoaKikqweNcPHPXtYvbXS299OzaRyb+4/8NRMWPfbW9V9A2Ko1hasq+xivV4EXtVSEc2bi+B+TIwYqk2d9lMHAkRUh/LzRf3P1FTPC2fquMVSUs/bvfRLeLhnB47x8WLZtm0iqPj11yXvXx06AA88ANx+u38ySBuLli3Fj6q33hK9C//4o6hdO3Om6CnY271r3tjt4gfdU0+VBK+7dQMefVT8qFN/AUREiIvJ990nLl4eOyayik+eFP/jU6dKxouLPY+h3qURECDO270lJIj/W3BwyR0e6t0eBoP3i3p5eSV3w6jt7FlRG/foUfHrJjdX1K3fvr3qf1ubDTh9WrSKqFnLdrv47FZbeZ/XgHhMUVHiQqZ7i4oS+wsOFhc/g4NFMxpL7lZxb+5/19J19ENCxMXNoKCLI8jrcIgs6ZwcMczPL7mLyr3pdCV/Y7V/geDgkv+Hzm8/fYjoYnDwoPiMUT8vTp8Wn4FnzwKJieJz9Prrq/4+nJUl7tBR7wYOCBAdct97ryjn456wYrOJIqFbtgD/+Y/os2XEiLJB17Q04IUXxPgrr5R/wbAGTueexppjawAAd/W+y+/7b8jeeUf8K668UlwPVyUmAj//XH4d16POYsUsJ3Bx8tu3jp49e2LHjh1YuXIlDhw4gA4dOuDZZ5/11+4bpDN5ZzB68WjkWnLRN64v0grScPjCYQz4eADmjZ6H6X2mN4qe9pocNRhpMtX3mVSdopQETd2DqTV5HlmtJaUi1JabK7J/MjPFDxd1WFDgWUfP/fZLb+Nq1rB7M5vFD2U14Hv+vGi+CAryDMKGhZX8aPJlGBTErFoif1MU8cPi0CHRjh4tCbqdPCl+NPiDRiOy7d3fL666Stw6N3as5637FxONRvygu/ZaMVy1SgSkFy4Enn9e/DirKIj0+++i04tdu8R0r16i5+LRoyt+r5SkkuzR0hRFBLzcg4H+ft8NCRFZpuUpKhKdgR49KpoklXSQ5t6MxpIgrvp5qijiM+/s2ZJArjp0H3cvmVQVVqu4AOFeZ7i2SFLJ52BAgGcpI3+Oq88xWRZ/PzUoXNl46XmSJM5T7aRVbUDJd5FSTZeRgeuysiCpQeia/K2io8X3C7VptSJQW1zsOVTP1f15o9GIc1cD5er3DjUoHxdX0mJiGNwlasz27AGeeQZYsaL8df78U3RK1amTqL1+552+pTAeOybu0jl8WLx/vPgiMHly+WUC9Hqgd2/RnPXqsWMHMHy4yHhVg67//rf4LTdggCi55EW+JR//Wvsv7M/Yjzt73olbe9xapbtzv0j+AgoUDGkzBO0j2/u8XUPz1FOiO4BrrxV/+gEDKv4ak5MjrlsDwGOPeS5LTBTD/fu9b6tmuHr7OkVNn6Qo5XWzWzW33norPvzwQ4SGhuK7776D1WrFTTfd5I9dNyhqSYFjZ47h+u+vx97ze9Elugv+mPoHFCiYsnIKVh0RteNu6X4LPhz7IUKNofV81kR1TO2ETc10U9v58+Lqa3q6GLo3s9k/x5akkrq1PjSHyYT9p06ha//+0IWGlr9tQIBnfV4GdUmlZrF7q7EMlNRVVmsr63S+dwikKOLHf16e7y0/33tpFfW1Ufo5bjKVZOGrAYfiYvGaPXxYtMo6VIyIEMGxqKiSrHa1Q0SjsWwpGItF3Pbt3omj2kmVTgfccosItPbpU7X/BUTPx6tWrcKYMWMq7jSrsfr5Z1GXTf1mn5gofuTFxIigjzqUZdF17pIlYr2ICPGjrrIALZXIzxfB1/PnxWtWzcxVy+cYjSUXSt3rrRcXew8eZmWJ11JBgWezWEousLpfbFUUzztg1LtmiopKLmxebMLDRaaqewerRmNJs9vF31QNlKvjaj8CdUWSxHm6B2Hdg7HuwWb355U6VC8UEDVRDfaz+uBB4NlnxV026oWXyy8XxTdbtSppzZsDy5YB778vonGASBh5+GFRAsjb7f6ACNJef734jEhIEBdRu3ev2jnu2wcMGyY+my65RARdjx8XUUNFEccYNKjMZtvPbccty2/B0ayjrnkhhhDc3uN23NfvPvSK7VXhYWVFRod5HXAi5wQW3bAId/T0HtRtDGJiPK/vd+okAq933CHK1KvsdvERMneuuMbdvbvoa9X97fnPP0UcvGVL8ZW2tJtuAr75Rtys9MgjtfaQ6l2DfU1XQV5eHsLCwpCSkoKWLVv6ZZ9+C7j27NkTu3fvxv79+3HzzTfjqquugkajwbvvvuuP3TcYasB1cNJg/JnxJ+KC47D5ns1oHS5embIi480/38Ts9bPhUBzoENkBb13zFkZ3HA2dhj9wiMpVUFASfE1PFy0vr+THUmVDfwVsfeWtkzT1dmlvrbxl6nw1+OZ+W23pYUXLKlpHzcypqCyFe5Oksj/yS3ee597K+xFb3o9FNeCo/lhWG+B9/+U1NRvc13Xdb8l1b6Vv11Wz9Upnb5fO5FbHqxP0kKSSesrqba8hIeJcSwdQ1UBkfdLpxI+Nzp3Ft1K1M0a1A6XQGl5YlGXxzffsWfGNNSam2rtqCl/4KmW3i7pvTz0lAtcVkSTx42/OHJHdR02DoojArnqRJS9PTLuXN6psvKrrun+euGd9ljfubZ4si/dNtYNWtZNWRSmpJ1+q2cLDsX73blx9883Qq9mwVeVwiACH+v1C/a4BiM8ik8lzqGZCq03N0i0qKhswz80V+1KzmtPTax7cVTOBvQVkywvS+jL0No8Xkake1Piz2uEQgc6sLPH8bd++Zs/jY8dEoHXJkpLX7003iXlqCqM3+fni8/jtt8XFY0B8Zxo+XGw/fnxJ8HXZMnGB1GIRF5R/+KGkm/uqKh10NRiAv/8WEcNFizxWlRUZb29+G7PXz4ZNtiEhNAFTe0/Fl3u/xJGsI671BrYYiH8O+idu6uY9ae6XE7/g6oVXI9QYitR/piJQX83343qWm1uSTHzLLcD333v+jGzfXkzn5pb9efnFFyIw6y4np6TCU25u2a/EvXuLm4z+9z9xw1ZT1RS+fzfogGvfvn2xbds2PPbYY+jVqxfuuOMO9O3bF9urU1erAVMDrngCCAkNwcapG9E7tneZ9TanbMakbyYhJU9c5ogPicfU3lNx9yV3o10Eu6cj8jtZLvnhVroVFHifX1gIOT8f544eRXxoKDTqj8DSzWz2njFIVBH1Fnh/ZaEFB4tvcd5aWFjJeHCw99vvHY6SQId7Ky4uCTS4t+Bgcf9T584i2NpIvjw1hS98PsvJAT74QJR6SE8XP7zUoc0mMlzefRfo27e+z5So2hrda1oN7qoBWLWlpYlhRkZJsNn9Al5hYf1lLbtfRK7o4nFVLyrr9Z7Z26VbVZZ527dG43kxuPRFgtLz1O9ypS9O63SeGdPumdOlxw0Gz4sd7q2oqOQuEbVZLOIYpR+fTifuCnG/M6F5c3HhQb0jxv3vYLN5Pp/OnSt5Pnn7vivLnt8P1GFCgsiE7Nev/FvYvcnKKrnr5ehREVlSLzyr/VRYLKIee5cu4rtDly7ie4SXDi59fl3n5QG//iru7ti2Tby2srJKskpVLVsCo0aJcjnDh1ftQvCyZaIzTjW6Nn488NxzQM8q9MlitQJffilSIZOTS+brdMDVV4u/Q1KSmDdunFg3KMj3/Xuzf78oL6CmagYGiv+PWzme9IJ0TFk5BT8f+xkAcGPXG/HxuI8RGRAJRVGw4eQGfLj9Q3x74FvYZVGb/N3R72LmgJllDnfHt3dg8Z7F+L++/4f5Y+fX7Nzr0bZtQP/+4uWWni5eut9+Kyo1bdjg/SefySQ6xPrpJ+/9tbZoIV6Sf/3lWd9VUUQ+RWGhSJ7u3Ln2Hld9a3Sf1V7URsDVbymX999/P/r06YOcnBxX7dbCym5BbESSkpKQlJQEmzPTSKfRYcWkFV6DrQAwKGEQku9LxpyNc/DFri9wLv8c5vw+B3N+n4Or216NaX2mYXyX8TDpGmGdUaKGSKMpqalWBQ6bDdtXrULMmDHQlPfhoCglt0O73xpdUavqOqVr5JYeVrSssnXUzJzSt7t7m+dwiO28ZX2W16rSUYB6i6zVWtIsFjGUJN+P6X6bvq/n6C0jtqKsXfW2YV+a0Vg2c9j9Mbt3aqd2vqNmpak/1PLyxN/dW0C1vCAqXdzCw733QqyWdWFHhER1T6stqRHbu3fVtlVLRpQOxnob+rJORdu4Z+G6fzbRxaNzZxF8HTBARJ9ycjxbdrao1X7kSOV3U5RHqxUXbW++GXjooco7vpRl0UnVmjUiyLp5c8WdFIaEiO+QZ84An3wimk4n7vG+7jpxh0d5nUfJsshgVTuauuoq4M03q3eR0mAApkwR7dAhEcRdtkzcf/7zz6IB4m/w1lv++U6XmCgihGrQdfZsj2Drbyd/w83f3Izzhedh0pkwd+Rc3Nv3XlcfM5IkYVjbYRjWdhjSC9Ix5/c5eHfLu3jwpwcRFRCFW3vc6tpXTnEOlh9YDgC455J7an7u9UitqdqpkxiGhJT8686cEcnOpXMZvAVZ3SUmioDrvn2eAdfUVPF1TKMB2ratncdDDZvfAq7Tpk3DxIkTodPpEBQUhKNHj+LSSy/11+7r3YwZMzBjxgxXhuu7o97F1e2urnCbyIBIvDnyTbx09Uv4/tD3+GTnJ1h7bC3Wn1iP9SfWI8IUgTt73olpfaahR0yPOnokRFRlklSS3UBUVZJUEohVv7GFhtbotnlqPCx2C84XnodRZ4RRa4RRZ4RBa4BGqsWe5dWOlIiocVEzN2tapqUyasenZnPJxc/KLhJX9wKzetHX/eJvVed5y1ZV9126czf3Tt9Kz1NrI6t/A3Vot5dc/LVYKh83mcqWBAoJERmG6l0iAQFiaDCI9+TSj89qFdmaauey6t0JWVnlBxgjIkQGaVxcybBZM3Hs0n0PSFJJaaLc3JLh4cOit/vjx0s6wyx1C3q5WrYUXa137CiO6/441U77UlJEKt+hQ2KYny8iXHPmiGDm3XcD//ynyLR1t2ePuJ1/yRLg9GnPZR06ANdcIwKL8fHiFv3ISPH30OvFhYKNG0X64U8/icf422+ivfKK6Ezqvvs8O1TOzxf3hq9cKab/+U+xrj9qnHfuDPznP6IdPiwKeK5ZI+5fv+++Sje32C3YcnYLYoJj0D6iPbSaCoKziYmiA62//xaZuU5ZRVmYuGwiMs2Z6N68O76a8BW6Ne9W7m5igmPwzqh3oCgK3tv6HiavnIyIgAiM6jAKAPDV3q9QbC9G9+bd0S++n89/irqya5eo7PDii5UnbqsB144dyy5r2VK0qkpMFKV0S3ecddRZLrdNm8qDttQ0+bWoaLjbs7tDhw5YsGCBP3ffoNyU6HuHYEadETd1uwk3dbsJJ3NO4vOdn+Oz5M9wJu8M5m2Zh3lb5mFAiwGYdsk03NL9FoQYy7kKR0RERA2a1WHF1rNbseHkBmw4uQF/pvyJYntxmfX0Gj1ahrbENe2vwcj2I3F1u6vZySYR1Q1J8qyfTg1P6Q75NBr/XvjPzAS2bhXB1y1bRAAyIkJEq8LDxXhYmIg+deokgp5VvQVeUUSK36ZNwBtviOO8/z4wfz60N92E6O7dodm7F1i6VARcVSEhoizANdeI1q6ScnwBAcDIkaLNnSuCyatWAe+8IyJejzwigr3PPCPSGFNSRMdVe/eK18DHH5ctzOkvnTqJgO+//+3T6uuPr8cDqx7A4QuHxUPTBSCxWSJ6xvREj+Y9MKDFAAxOGOzKUgUgslpvvNFjP0/98hQyzZlIbJaILdO2IEAfUOmxJUnCO6PfwYWiC/hy75e4cemNWDd5HQYnDManOz8FANzd+27PYzcAiiJK4+7ZI7oV+Ne/Kl7/sPjTeg24Vpda5rd0wFUN7nbo4L9jUePitxqu8+fPx2effYawsDD06NHD1fr1a3hXQGpCzXDNzMxEVFRUtffjkB1Yc2wNPt35Kb479J2rZkqgPhATEydiSq8pGNJmSO1mwBBRk6g3Q0Ql6uM1LSsyluxZgkW7F+GP03/AbPPsZUEraeFQKq7NqJW0GJwwGCPbj8QNXW9AYrMKOukguojwc5qoCVCUkoxT9fZ6dwYDcO21wG23iWFA5QHCStlsopej554r6dCqQweRRZyVJTKEV6zwvAe8nqQVpOGfa/6JJXuWAADCTeGw2C0ospct8/HUlU/h+aHPl7uvnak70e/jfpAVGRumbMCQNkOqdC5WhxXjvxqPn47+hHBTOJLGJOH2b2+HXqPH2Vln0SyoWZX2V9t+/VUkPwMibv7FFxWvP3CgiP1/8w0wYYJ/zuH334ErrxQB35MnS+Y/8QTw6qvAjBnAe+/551gNVVP4rG7QNVxfffVV/PLLL1AUBXv37sWePXuwZs0afPnll/46RJOi1WgxuuNojO44GukF6Vi0exE+2fEJDl04hIW7FmLhroVoFdYKd/a8E1N6TUHHKD9egiEiIiKXQmshci25KLQWotBW6BpKkDA4YTCCDOVn9aw7vg7/WvsvJKclu+ZFB0ZjSJshGNpmKIa2GYou0V2gQIHNYYPFYYHFbkGxvRh7zu/B6qOr8fOxn3H4wmH8fvp3/H76d/xnw38wusNoPH7Z47iy9ZV+zSax2C0w6lgehYiI6pAkAUOGiLZzJ+RXXoHjhx+gHTAAmttvF5Evtat3f9HrgWnTgDvuAD78UJQ1UO/x7t9flBOIj/fvMavIITvw0faPMHv9bORacqGRNJjRfwZeGPoCgg3BOJ59HHvO78Ge9D3YkbYD3x/6Hi9sfAH94/tjXOdxZfYnKzJmrJoBWZFxS/dbqhxsBQCD1oBvbv4GIxaNwJ8pf+L2b28HAFzX+boGF2wFRCKzat++ytevqKRAdakZrqdOib7r1KpOtXEsalz8luE6fvx4LFmyBIGBgf7YXYPlrwxXbxRFweYzm/FF8hdYum8pci25rmX94/ujV0wvdIzqiI6RHdExqiPaR7T36fYAIipfU7gaR0QlfH1NK4qC3079htc2vYafjv5U7nqB+kCM6TgGNyXehDEdxyDYIL5F7z2/F4+tfcy1bZgxDI8OfhTXd74e3Zp3q/IdKieyT+DnYz/jxyM/YtWRVZAV0aHNgBYD8NjgxzC+y/iK67hVwuqw4j+//Adv//U2bu9xOz657hPoNH6tLEVUK/g5TdT01MvruqAA+OADUc/23/+uURatxW5BhjkDhdZCFFgLUGgTQ6vDiiFthiDcFF7pPk7lnMLN39yMLWe3AAD6xvXF/LHzK6yR+uCqB/He1vcQZgzD9nu3o31ke4/lC5IXYOp3UxFsCMbBGQfRIrRFOXuqXHZRNq5ccCX2nt8LAPjxth8xpuOYau+vNpw4AbRvX1KSOTBQVMcorz/fCxeA6GgxXlgo1veX2FhRinnLFhHPB4CePUWpgx9/BMY0rD+d3zWFz+oGneH65JNP4tprr8VDDz2EgQMHIr6erxY1RpIkMmkGJwzG3FFz8f2h7/HFri/w87GfsfXcVmw9t7XMNvEh8WgR0gLxIfHltqiAqAZXa4WIiKg+OGQHvj3wLV7/83WPz1WtpEWQIQhB+iDXMKc4B6dyT+Gb/d/gm/3fIEAXgNEdRyPEEIJFuxdBVmToNDrM6D8D/7nyP4gOjK72ebWNaIv7+t2H+/rdh6NZR/Hmn2/i8+TPseXsFkxcNhEdIzvikUsfweRekyvMuPXmYOZB3P7t7diRugMA8MWuL2C2mbH4xsXQaxvnl2IiIqIqCQ6uvMCnD/67+7948KcHkVOc43V5QmgCfp/6O1qHty53HxmFGRixaASOZB1BiCEEL139Eu7vd3+lF1bfHPkmtqdux+YzmzHh6wn4854/EagXUcOc4hw8tvYxAMDTVz5do2ArAEQERODnO37GyP+ORLAhGNe0v6ZG+6sNSUki2DpsmCgVbDaLW/rLK/ur1m9t2dK/wVZAZLmmp4s6rv37i/LLakI1M1wvXn4LuE6ePBnDhg3DunXr8Pbbb+PMmTNo1aoVfv31V38d4qISoA/ApO6TMKn7JJzLP4dfTvyCIxeO4EiWs104glxLLs7ln8O5/HMV7sugNSAuOA7xIfGIDowu+UGpD0KgPtA17eu4QWtgAJeIiBoVm8OGT3Z8gjc3v4lj2ccAACadCXf3vhuPDHoE7SPal/lsUxQFO9N2Ytm+ZVi2fxmOZR/Dtwe+dS2fmDgRL1/9MjpE+rc3hA6RHfDB2A/w7JBn8d6W95C0NQlHso7ggVUP4N+//BvTLpmGGQNmoE14mwr3oygKPt7xMR5e/TCK7EWIDIjEA/0ewKubXsWy/ctgl+34auJXMGj903HO32f+xrrj6/B//f6vRsHnhsRit+DQhUPILc71KDdRaC2EAgWXtrwUvWN7s+Y+EVETl1uciwdWPeCqs6rT6BBsCEawIRhB+iAEG4JxLv8cUvJScPXCq/H71N8RFxJXZj8F1gJcu+RaHMk6gtZhrbFx6ka0Cmvl0zkYtAYsu2kZ+nzUB7vSd+GBHx/A59d/DkmS8PSGp5FhzkCX6C74x6X/8Mtjjg+Jx+77djfI3/4FBcAnn4jxWbNEP3C7d4uyAuUFXGvzFv/ERGDDhpKOs1JTgaIiQKsF2rTx//GocfBbwDU8PBxJSUke886oxambgKSkJCQlJcFms9X5seND4nFHzzs85imKgkxzJk7knEBqfqor8Hou/xzOFZSMZ5ozYXVYcSr3FE7lnvLL+ahZQIH6QFcmkDoebAhGkCEIwXrnsNSHUOl5Oo3O1bQarWvcpDMhUB8Io9bYIN/giYio8TiTdwaTvpmEP1P+BABEBkRiZv+ZmDlgZoX1yCRJQp+4PugT1wcvXf0SdqXvwrJ9y3A2/yzu7XsvBicMrtXzjgmOwQvDXsDjlz+OT3d8ine3vItj2cfwxuY38NZfb+H6ztfjoYEP4YpWV5TJisk0Z2L6/6Zj5cGVAICr216NhTcsRHxIPC5teSlu/PpGrDi4AhO+noBvbvqmxnVdfz35K0YvHo1iezHm/j0Xc0fOxW09bvPrZ/iWs1twOvc0DFpDmVZewDNIH4RwUzgiAiJg0pkqPUZqfio2n9mMP1P+xOYzm7H93HZYHJYKt4kOjMbwdsNxTbtrMKL9CLQM9e02tPOF57EjdQdSclMAAArEPZFqta+Y4Bhc3fZqhBhDfNofERHVDrWW6cmck9BKWjxz1TOYfcXsMqV5zuadxRWfX4Fj2ccwfNFw/HbXbx4XIK0OKyZ8PQFbz21FVEAUfr7jZ5+DraoWoS3w1YSvMHzRcHyx6wsMajkIgxIGIWmriMW8O/pdv11IBdBgf4svXAjk5org6ejRwOLFJQHXcWXL2wKo3YBrt25iqAZc1WO1aSPKCdPFyW8B14EDB2LBggW46667XPP8VfegIZgxYwZmzJjhquFa3yRJQrOgZpUWrrY6rEgrSHMFYC+YL6DQVgizzezK0jDbzN7nlRq3ySLY7FAcyLPkIc+SV/uPE5Ir+GrSmWDUGaHX6Mv80NJry84zaLzP12v00Gl00Gv1Po0DgF22wyE7YJftYlxx4PJWl1f5A5KIiOrW+uPrcevyW5FhzkCYMQwvDH0Bd19yd5Vvy5ckCb1je6N3bG+vyzMyxJfrQYNE3yD+FGwIxj8u/QdmDpiJn47+hHl/z8Pa42ux4uAKrDi4AgCgkTQwaA0wao0waA2uz3a9Ro+Xr34Zjwx6xBWUvLbTtfj+lu8xful4/HD4B4xfOh7f3vxttevCbzq9CWOXjEWxvRhB+iBkmjNxx4o7sGj3IswfO7/STNzKbDy1Ec/8+gx+PflrjfZj1BoRbgpHuCkcWo0WsiJDURTIigxZkWG2mZFakFpmuwhThOcdQs5hkb0If5z+A5nmTHy19yt8tfcrACJDuU14G4+STy1CWkCSJCSnJWNH6g7sSN2Bs/lnKz1ng9aAYW2H4frO12Ncp3E1vkWUiIh8Z5ftmLNxDp7f+DxkRUbb8LZYfONiDEoY5HX9FqEtsH7yelzx+RXYn7EfI/87Er9M/gVhpjDIioy7v7sba46tQaA+EKtuX4XO0Z2rdV5D2w7Fy1e/jMfXPY6HVj+EjpEdISsyJiZOxPB2w2vykBsFWQbmzRPjDz4oaraqAc+KOs5SSwp06uT/c1I7ziodcGU5gYub3wKuJ06cwPfff4/nn38e/fv3R8+ePdGzZ0+MK+/yAtUJg9aAVmGt/BIYtDlsrh9whVbvgdrShcPLjFs957sCmM5gpkNxwOawwaE4AIhsjyJ7EYrsRTU+f38LNgRj9e2rcVmry6q8rc0G5OSI2wwSEmr241xWZNgctlrpdbqoCFi0CEhJEQXJO3QQLSbG/wEFIiJ/khUZczbOwdO/Pg1ZkdErpheW37y8TAcT/nDsGHDllcC5c8DllwNz5wJ9+/r9MNBqtBjbaSzGdhqL/Rn78e7f72Lh7oUw28yQFRnF9mIU24td63eJ7oIlNy7BJXGXlNnXyA4j8eNtP2Lcl+Ow+uhqXPfVdfjulu9cteB89feZvzF68WgU2gpxTftr8M1N3+Cdv9/BCxtfwM/Hfka397vhxaEv4qGBD1W5068/Tv+BZ359Br+c+AUAoNfoMaDFADgUB6wOq6tZ7BZXdqg7RVFQYC1AriUXsiLD4rAgvTAd6YXp5R5TgoQeMT0wuOVgDEoYhMEJg72Wm1DZHDb8deYvrD2+FmuOrcHWc1txNOsojmYdrfTxSZDQObozOkR2gFYSfxv34+xJ34Nj2cew+uhqrD66Gvf/eD/6xvXFwBYDEWoMRYgxBMGGYIQYxLDIXoS0gjRXSy9MR3pBOhyKwyMYb9QZYdQa0TGyIwa0GIABLQagQ2SHBpvFRERUU2abGadzT+N07mk0C2zm9XOxtOPZxzF5xWRsStkEALij5x1IGpOEUGNohdu1jWiLdZPX4crPr8SO1B0Ys2QM1tyxBk9teAqL9yyGTqP7f/bOOyyKqwvj7+7SQUFAQFTE3nvvYo3YNcaSWFNMokmMxkRNwy9GTTNNE40maowtxpLEbhR7RcWG2AvSREV6WXbv98dxGMrusrvs0jy/59lnhp07d+4uOzN33nvue7DphU1oU7lNoT7TjA4zcOL+CWwJ34LLcZfhZOuEhb0XFqrO0sLevcDVq0C5csC4cfSeMYKrtS0FAErklZrKgitDKIQ0b6mQZGZmYs2aNYiIiIBKpYKPjw8uXryI7777zhLVlxikCNeHDx/Cw8OjuJtTZlFr1CS0qklsTVWnIk2dBrVWneshS63J/XemJjNfGV3lskQW1Bo11Fo1srS0nqXNglqrzrcOIJf1gY3SBlFJUbj66Cpc7Fyw+6XdBqeVhoUBs2YBt24B8fEktKakyNtbtQIWLQLatjX8nQghcPHBRVx+cBlXH11F+MNwXH10FdceXUN6VjqG1BuCGR1moG2VAioygrQ0YOlS4IsvgJiY/NudXLJgM3o4/HyccWr2Cjjal955EmUhoyLDMDJqtRob/tmAdenrsOPGDgDAxGYTsShwkdkRnIa4dw/o3JmWEgoFPQB8/jlg7RyimZpMJGUkkfCoycgWIDVCg4YVGxaYFOvQ3UMIXBOIFHUKGnk1wvph69HQq6FRxz4bfRbdV3VHQkYCAvwDsG30tmzB9urDq3ht22s4dPcQAKCpd1OMbzYeQ+sPNTgInKpOxeG7h/HN8W+w99ZeACS0vtz8ZczuPBtVXU2fZaQVWiRlJOFJ+hPEp8fjSfoTCCGgUCigVCihVCihgAI2ShvUr1i/wIdpQ8SnxWdHr0YlRSEyMRJRybTM0GSgqXdTtKjUAi0rtURTn6ZwsXPRW5cQAuEPw/HP1X/w99W/ceL+CZ3CsiVwc3BDa9/WaOXbCuXty+eL/tUIDdKz0rP7ZVIfLUOTARulTb4ZRo62jvAt54sq5atkv3zL+eq0dZCOIw28a7QaaAQNxAshoNVose+/fQjsEwgHeweoFCqolKoy55srhEBSZhKik6IRnRyN6KRoxCTH4HHa43z92kxtJlQKFdllOFRABccK2UtnW+dcNl0qBa0nZybjQcoDxKXG4UHKg+z1LG1W9gwwW6Vt9uywaq7V0Ni7MRp7NS5wRh3DmIo1+98ZWRlYEboCe2/txd0nd3Ev4R7iUuNylRneYDgW9lmo0wJGCIHlZ5fj3d3vIkWdgnJ25fBzv5/xYpMXTWpHaEwoAlYF4En6E1R3q47bT24DAFYPWZ3PLtBcEtIT0HpZa1x/fB3zus/DrM6zLFJvSScwENi5E3jnHRrkBkjgrFMHcHAgf1dVnjFeIYDy5WlbWBhQv77l21WxInnJnjkDfPYZsHUrReK+9Zblj1XSKAvP1ImJiXB1dUVERITFZutbTHAdNmwYfH19UbduXVy+fBnHjx/Hhg0bULeueWHyJRUWXBmAHggHrBuA/bf3o5xdOex+abfOqSW//w688QaNculCqaQpEQAwcSIwfz7g5aX7eGO3jMWmK5sKbFtnv86Y0WEG+tXpZ/LDSGoqsGQJ8OWXlGURAPz8gN69KePj9eskKgj//cC4HgAA/+h3ceGrhShXSi3mysLNgWEYICkjCUcjjmL/rf1YGbISceo4ONg4YHHgYkxsPtEqx4yKosjWmzepk79mDfD998Aff9B2Z2dg5kxg+nTA0fJar8U4HnEcQzYMQWxKLBxtHPH9c9/jlRavGIx4vBB7AQGrAvA47TE6Vu2IXS/tyiceaoUWv579FTP2zkBCRkL2+619W2NY/WEY1mAYKrlUwrGIYzh49yAO3DmAU5Gnsi2MbJQ2mNhsImZ3nm0w2/OzQmxyLHZc34Fb8beQnJmMpMyk7GVSRhIcbR3h4+IDH2cfeLt4w8fFB97O3rBV2SIjKwMZmgxkZJEon6JOwaUHl3Aq8hTORp8t0KfWUkhibk5RVSu0Ztcnia+SqCit513q2yaJ7QqFwuSlufsKiFwzw6RlQkYCUtV6OozFjI+LDxp7kfhao0KNbBG9cvnK8HL2KnPiN2N9rNH/Ts9Kx/Kzy7HgyAKdli0udi6oWr4qrj66Cq3QwtnWGZ92/RRT203NHpyMSY7BK/+8gu3XtwMAulTrglWDV5ltjXPi/gn0/L0nUtQUbfN1r68xvcN08z6gHmKSY3D47mEMazDsmTgXr14F6tWjwe3r12kWJgBoNICLC5CeTu/XypPPNDqaBsGVSnrmtbf85FB07QocOkQzRBcsoGjbnTuB556z/LFKGmXhmbpEC67NmzfHuXPnsv8ODQ3F22+/jUOHDlmi+hIDC66MRKo6Ff3X9kfwnWCUsyuHPWP2oF2VdgAoQvStt4Bff6WyPXoA778PuLsDFSoAbm6Aqyvw6BE9jK9cSeVcXYH//Q94803A5qnhR0xyDAauG4jTUaezp1PW86yHuh51UdezLup51kN6Vjq+PfEt1lxYk/2gWs+zHmZ2nImxTccaNU1w9WrgvfeABw/o72rVgA8/pCgtuxy+6xkZwGub38Xv177Lfs/v1DocXToS1rZtfpz2GH+F/YVBdQfB28XbInWWhZsDw5RlMjWZOBV5CvFp8RAQ2QmFBATUGjVCokJw4O4BnIk6k21HAwA13Gpg04hNuTxXs7Lka2thefCAOtbh4UD16tTBlq6BJ08CU6cCJ07Q3/XrA6dO0YNASSU2ORZjt47Fnpt7AFD0zy8DfoGbg1uucvFp8Qi+E4zXt72OuNQ4tK3cFnvG7DEYFfog5QHWXVyHTVc24ci9I7miNFUKVa7/GwBUKV8F/Wv3xwedPii0/ytTMGqNGhcfXMSpyFM4H3MemZrMXIKiUqGEQqGAo40jHG0d4WjjCCdbJzjaOsJeZY8sbVa+CMwUdQqikqJwP/E+7ifeR0RiRC67C0Y/5ezKoVK5SqjkUgmVylWCh6NHth1EzleWNgvx6fGIT4vHk4wniE+LR3x6PFLVqbmsuiT7LidbJ3g5e9HLyYtyQThVhJ3KLnt2mDT7Kz0rHTce38CF2Au4GX/TYHttlbaoVK4SytuXz+VxLCXR9XDygKeTJyo6VYSnkyetO1dElfJVLJrYhyldWLL/naZOwy9nfsEXR7/I9uGuUr4KJreejAYVG6CaazX4ufrBzcENCoUC52PO480db2Yn0mxQsQEWBy7Go9RHmLRtEh6lPYKdyg7zus/L5X9uLgfuHMCb29/EqEaj8HHXjwtVV1nj/n3g5ZdpRmVGRu6XEGTTNGwYMHAgPUMD9Iy9aBElxvrnn9z1NW8OhIZSZOmgQbm3HTpE/bbq1WnmqTV4803g55+BDz6gAfj0dODGDVkULsuUhWfqEi24dunSBb/99htq5RhKaNy4MS5evGiJ6ksMLLiWHJKTyb/lzh2gTx/ZN6UoSclMQf91/XHgzgGUty+PPS/tQYXUthg+nLIkKhTAp58CH32Uf1pDTo4fB6ZMAc6epb8bNwb+/BPQuF9Gv7X9cDfhLtwd3fH3yL/Rya+T3noiEyPx/cnvsfTM0uykZl/2/BIzOs4w+DlOnAA6dqRoW39/ElrHjs0ttEoIIVD7x9q4GX8Tjdza4dKTE0CmEyr+fRK7VzdC84ItkcwiJTMFXVd2xZnoM/B388eel/agtoduUxwhgLt3gXPnyMJh6FASs3VRFm4ODFOWEELg6qOr2HNzD/bc3IMDdw5kR4YURHW36ujs1xmu8a4IeiEI7i7uAOjaNncuTe+qXx8YMoReTZua50f9+DEQEEDX+SpVgMOH6dqZ+3MAGzbQtf3RI1p/4QXTj1WUaIUW3xz7BrP3z0aWNgvVXKvht0G/IU2dhuA7wQi+E4xz0eeyBdMWlVpg39h9+URZQ8Qkx2Br+FZsurIJwbeDoREa+Ln6oZt/N3St1hXd/Luhult19hMtYwghEJ8ej4epDwuMPM35nkKhQHpGOrbv3I5evXtBaaPM9v7PaT9gaJnXqiDnUiu02QM55i5NrUMBBZxsnUiQtHPJFifL25eHt7O3yQn9rE1yZjIuP7iMC7EXcOnBJdxLvIfIxEjcT7yPmOQYs20uFFDAt5wv/N38s19+rn7wcvZCRaeKtHSuCFd7V74elDEepT7Cruu7sO7IOrh4uSAhMwFP0p9kvzI1mehQtQP61e6HwNqBOm1onqQ/weG7h3Hw7kH8ceGPbG/uquWrYnbn2ZjQbILB/BZaocXv53/H+3vfz2c30MynGVYPWY1GXo0s+8GZfMydC3xshAZtYwP07AkMHkwBQpIO0DNPfrCXXqLZRp9/DsyenXvbr78Cr7xCMzd377bYR8jFokUkCLdoQc/1NjYUiGWpwf6STFl4pi7RguuFCxcwatQoBAYGokGDBrhy5QrCwsKwbds2S1RfYmDBtXi5fx/Yto1Gs/bvp9EviaZNgdGjgZEjaRp8UZGSmYJ+a/vh4N2DcFSWB37fi7QbbeDlBaxdS9GtxqDRAMuX083h8WOgdp//ENttGBIzElHLvRZ2jN6hV2DMS2JGIhYcWYD5R+bDRmmDIxOO6PV2TU+n0cDwcPrufv8dMHSNvBJ3BQ1+agA7lR0evPcA/X4fhqPR+4BHteH0x2n8udoV/foZ95mNRaPVYOifQ/HPVXkY08vZC7te3IXmlZojPR3YsgUICSGRNTSU/HIl3N0pknjKlPzTesvCzYFhygLXHl3DDyd/wL/X/sW9hHu5tnk5e6G6W3UAyJ6WCwBKhRL1POuha7Wu6OrfFX6ufvnO6aQkitTfsiX/Mf39qfP+/PM06GQMCQl0XT9zBvDxoYgJQwkR3nsP+OYbYMIE4LffDNctBN0Dzp6lRBDlypHfmLTs04fuddbmVOQpjNo0CrfidYeA1PWoiz41++CTrp/Aw8n8vlB8WjxS1Ck6PfQYRoLv0yUXtUaN6ORoRCVFISkjKZ9NQnJmMh6lPcLD1Ie5XrEpsUZHPNsqbeHh5CFHzeYQqZ1tnXOtu9i55IqwzRdxm2Nfe5U9C7kGSM5Mxo3HNxCXEpdLDE3ISEBCegLcHd1R26M2arnXQm332nB3dNf7fWZps3Aq8hR239iN3Td341TkKZOE+kZejRBYKxDNfJrhVOQpHLx7EKExobnqqOZaDbM7z8b4ZuNNipyOT4vHR/s/ws8hP0OhUGBmx5n4tNunHH1dRPTrB+zYQbOChgyhaf7SKzWVnvv/+gu4dCn3fg0bAhcv5h80nz+f+lGjR5PwmpOZMyk/yZQpwI8/Wufz7N+f+9m/dm3g2jXrHKukURbu1SVacAWAjIwMbN26FVeuXEGlSpUwZswYODmZlu22pMOCa/Fw4AB54EkRoBI1apA/S3AwoFbL73fuTJ6o48aZF71kKimZKej4cyDOPzkERLVE16shWLcOqFTJ9LqiooCaw5cjvccbgCoLnfw6YeuIrSY/1AohMHLTSPx5+U/4u/nj3KRzOqOQ3n8f+OorEg4uXyZx0hBfHv0SH/z3AfrU7INdL+1CXEocmi9picjkCCB8IBR/bsGyX5R4+WWTmmuQqbum4vuT38NeZY/1z6/H/w7+D+dizqGcXTlsGPwPPn+1G44ezb2PrS3djFNS5CyRvr7AJ5/Qb0O6D5SFmwPDlGaORRzDV8e+wt/hf2c/PNmr7NG5Wmf0rtEbvWv2RmPvxkZP6ct5TkdE2GLQIOqo29lR4gJHRxJfd++mqAOJxYtpKlhBvPACsHEj4OkJHDxY8OyKvXspmsLXlwYNDd2Tzp4FWrbUv93Hh+owNGPCUiSkJ+DNHW9i/aX1qO5WHQH+AQioHoBu/t3gW87KmcAYJgd8ny57CCEQlxqHO0/u4M6TO7j75C7uPLmDiMSIXEm9kjOTrdYGpUKZS3yVIqtzJhrT956jraMs8OYQerVCmy/5bs4EvWqt/LdGq8mV3MxGYQNbFSUuc7J1yo6Cltpoo7RBQnruSNAnGU+QmJGIlMwUpKpTs4XuVHUqtEILFzsXo17Ots54kv4E1x9fx/XH13Hj8Q3EJOvImmsANwc31HKvBRulDdKz0rNfaeo0JGYkIi0rLVf5Bp4NUFNRE12adoGnsyfcHNyyk8CptWrsvbkX269vx/H7x/X6PNfxqINu1bqhR40eGFxvcKFE0qsPyde1fkUrZFJidCIE9aUePybbpdat9ZcNDwc2bSLx9cIFmgk6bFj+cv/8Q1YCTZtSAE5Ohg6l/t/33wNvv23Rj5JNTEzu5//AQGD7duscq6RRFu7VJVpwvX//PipVqgRVUTwFFCMsuBY9ISHkt5KaSg+q7duTj8uAATQ1VKGgC/WmTRRRevAgXcABYN48YFYRJWvsPPAWjrSsCaXWDqkfpsLezrxzYd3FdRi9eTQAwP7qi7j7w6/w9jTP1TshPQHNljbDnSd3MLzBcGx4fkOu0efjxymqSwi6QQ0YUHCdnVd0xpF7R7Co7yJMbjMZAHA68jQ6reiETE0msG8uHEM+RGSk7LVTGH48+SPe3kV3xT+f/xPDGw5HQnoCBq0fhIN3D0KptYf2z/VwjR6MMWMoWrd5cxJB7O3Js3H1aiAoSM4iXqsW/TaGDy/em0NyMk1vWb2avquAAHq1amU4yphhSjtaocU/V//BV8e+yvZQA4CBdQdiUstJ6ObfLTvbvalI57S9fT+8+KINHj+mzu/mzUC7dnK51FRgzx6K6t+yhcrcvm04icK1a5SoQYiCHw4k0tNpICstjR4SGjfWX/aTT8j2oGNHis5ITASSkui1ciUtT5wA2uqesGAV1Bp1djIRhikOysJDHGMeaeo0xKXG4VHqo3zRszmjaHO9l2M9OTM53z6Zmszi/lilBg9HD1QuXxmu9q7ZgqibgxvK2ZVDXGpctjh7P/F+gXVVcKiAXjV7oU/NPuhdsze8Hb2NOq8fpz3Gnpt7sP36dlx9eBUtK7VEV/+u6FqtKyqVMyOyhSkxSMmvHBxo9pAuKztdaLWU+EoXN25QVKm9PQXd5JSmGjemAfgdO4C+fQvffl0IAXh4yDMt33kH+O476xyrpFEW7tXWEFwt5iYRGBiIw4cPw/WpUWJ8fDyuX7+ONm3aWOoQFiU1NRX169fH8OHD8fXXXxd3cxg93LpFUw1SU4FevSjzs5dX/nLu7sCrr9Lr/n2KVFqwgLxTW7Wifa3J6dPAkW3VgKZ20NpkIjo1Av52/ibXI4TA/CPzAQAVrr6N+HXf4QsfBRYuNK9drg6uWD9sPTqt6ISNYRvR82xPvNbyNQD08D9+PN0YxowxTmx9lPooWxzpX6d/9vutK7fG4sDFePXfV4HuHyMtqhVWruyDd981r90S/179F1N3TwUALOixAMMbDs/+XNtG7kKd2SMR7fo38MIwvNHoF8wfnj+s1saGpvKOHg0sWUKePjduUJTaunW6R0etTXQ0TWX5+WfymJX47z9auriQSXz37hSNy2M7TFnhXsI9/H7+d6wMXZmdiMVOZYcxTcZgevvpFoksoQGkGli5UgWtFmjThgRV3zxBmU5OZCcQGEgJFKKi6Jowfrz+uhcupPoHDDBObAXoQSIggDr4u3YZFly3bqXlpEl0Xc5JTAxF1u7cWbSCK4utDMMUF462jvBz9dPp4WkuWdqsfEJtpiYzV5KxvEnHcv6t1qiRlpWmU9RVKpSwVdrCVmULG6VN9rqt8unfT9dtVbbZCQOlpGZSZGymJlOOVs0hFGdps+Dm4JZP/MyZrMzJ1il7XQFFdvuMeTnbOaO2e216PbUKMNafO1Wdilvxt3Ar/haEEHCwcYCDjQMcbR1paeOIGhVqQKWU1S91zqmJBnB3dMfIRiMxstFIc/7dTBEjBEWh1qtnuL8DUOAPQM/qxoqtgH6xFaD+nIMDDXbfvAnUqUPva7X0/AfI71kDhYJmWB45Qn8bspxinhGEhWjevHmuvzUajWjRooWlqrc4s2fPFi+88IKYPn26SftFREQIAOLhw4dWahkjERcnRJ06QgBCNGsmREKCafu//DLt6+EhxN271mmjxJAhdCzX2fUFgiB239htVj0H7xwUCIJw+txJbNz2WABC2NgIERZWuPZ9eeRLgSAIh7kO4mLsRSGEENOnU5srVRLi8WPj6vk99HeBIIgmPzfRuf2Vv18RCILAbGdRqcsOodGY3+aQyBDh9LmTQBDEq/+8KrRabfY2jUaIceOEgFItlIMn0jGDIH49+2uB9SYlCTFpkvzZHz7MFFu3bhWZmZnmN9ZIwsKEmDBBCDs7Oj4gRO3aQixaJMSPPwoxdKgQ7u7yNkCI+vWFePTI6k1jGKuRmpkq1lxYI3r+3lMoghTZ56vbAjcx679ZIioxyqLHW79enX3+jBsnRFpawft88QWVb9hQiByXmlzExgrh4EDlDh40rU3ff0/7de+uv8yNG1RGpdJ9zv/6K21v08a0YzNMaSczs+ju0wzDFA18XpdNtm2jvkqNGvr7UxKvvUZlZ8ywbBuaN6d6N2+W37t7V2Q/V6vVlj1eXqTPBQix2zxJoFRSFs7phIQEAUBERERYrE7jDNGMoEqVKjh8+HD230qlEpmZJXPKxvXr1xEeHo6+1oolZwpNWhrZBly7Rgmwtm+nhCGmsGgReeE9ekRRjOnGefObzOXLFD2lUAAt/WnI7Pqj62bV9eMpcvB+qfFLeL5fBQwYQFPip06VbRLMYXqH6Xiu1nNIz0rHiL9GYN+h1Oyo2WXLjJ/6v+06JcHrX7u/zu0/Bv6IHtV6A3YpiO42ADPWrDSrveEPw9F/XX+kqlPRu2ZvLA5cnG2FIAR9H6tWASqFDf4auxzT2k0DALyx/Q2cuH/CYN0uLjS1o1YtijSdO9dil0GDnD4NNGsGrFgBZGbSlOEtW4ArV4DJk8nAfdMmIC6OEn8tXAhUrkzbBw+23u+XYQpDfFo89t7ci88PfY5X/nkFY7eMxci/RmLohqHov7Y/eq3uhUrfVMKLm1/Ef7f+g4BAN/9uWDV4Fe6/ex/zesyz+JTAlSvpnH7jDQ1WrKBIh4J47TW6Nly+rD9z7U8/0XnYujX5hJvCc8/R8sgRshLRhRTd2rWrbi9tqY7Tp+k6YYisLIqi37KF1hmGYRiGYazN4sW0vHWLfFcNIUW4tm9v2TY0bEjLsDD5PSmfR40aNPvRmuT09q9Vy7rHYko+JisN8TlTf+dg0aJFeOONN/Dqq69i2bJlePvtt+FnRqr4Q4cOYcCAAfD19YVCocBW6QkkB4sXL4a/vz8cHBzQtm1bnDp1yqRjvPfee5g/f77JbWOKBo0GePFFugi7udEUzLxTQY3BwYGmNLi7kw+stcyxFyyg5ZAhQItqNG/g2iPT0xHeT7yPLVcojfaUNlMAkOhmZ0c+g//+a34blQolVg1eBR8XH4TFhWHosncgBCUV69fPuDoyNZnYdWMXAGBAXd3+Aw42Dtgx5l/Uy3gJUGqw8NYEfH7ocwgj1WIhBH458wtaLG2BmOQYNPZqjI3DN+aa0vrJJ3JmyRUrgCFDFPiq91cYUm8IMjWZGPbnsAKN/h0c5Dp+/FGJu3fLGdU+c8nMBF5+mZadOwPHjpHwMnhw/uQ3SiUJs+++S799V1fg8GGaXqzVnTOAYYqM64+u48eTP2LMljGou6gu3L90R+8/euOj4I/w67lfsfrCamy4vAFbwrdg+/Xt+O/Wf0jISEA112r4tOunuPn2TQSPC8bYpmPhbOds8fY9eADs3UuDM5Mna41OmujmRpY0AKDLZSg1VX6IeO8905Mx1q5N09wyMykJpC6k7s7gwbq3+/pSEggh6J5giOXL6Z43dChQrRrw6adARIRpbWYYhmEYhjGW27fp2UVC3wA2QB71ly7RurUE18uX5fckwdWadgISkuBqa0uBY8yzjcn6fp06dfDZZ59h0qRJuZLv+Pn54dy5c9iyZQsuXryIOnXqYN68eSY3KCUlBU2bNsXEiRMxdOjQfNs3bNiAadOmYcmSJWjbti2+++479OnTB1evXoXXU3PPZs2aIUtHSMeePXtw+vRp1KlTB3Xq1MGxY8fylclLRkYGMjIysv9OSkoCQL4zxnrPMMYjBPDuu0ps2aKCvb3A5s0a1KolYO5XXbkysHq1Av37q7BsmQKtWmVhwgSL5IkDQKN369bZAFDg/ffVOKeoCYAEV1N/Hz+d+gkaoUEXvy6o514ParUa1aoB77yjxFdfqTBtmkD37lkGE7oYooJdBawcsBLPre2LxFrL4dn4dXz1VROjv9vg28FIzEiEl5MXmns11/v5FFBg4+hf0fjdykCnL/BR8EeISIjAd72/y+XdlJeHqQ/x+o7X8c+1fwAAPfx7YMXAFXBUOmYfa/16BebOpcvWDz9oMHKkNrv9y/stR/jDcFx5eAVDNwzF3hf3GsxW2qMHMGiQCn//rcQvvzTByy/r/jzJmcl4YdMLCIsLQye/Tuju3x3dqnVDjQo1CvzOJBYsUOLiRRU8PQU2bMiCpyeM+t7r1gU2bqTf719/KTB1qgZff228iMQwluBBygNsDNuItZfX4nTU6Xzba1aoiVaVWqGeZz042jjCTmWX6+VX3g8dqnaAUkFjvNa8d65bp4RGo0KtWvGoXt3epHvHm28CP/xgg337FDh1So3mzeVtK1Yo8fChCv7+AgMGZJl1T+rdW4mlS1XYsUODPn1yj548eAAcPUr3kn791Hrr791bifPnVdi+XYsXXtDoPdZvv6kAKGFnJxAVpcD//gfMnSvQt6/Aa69p0aePMOiBxjAlCemawf1uhik78Hld9vjpJyWEUMHGRiArS4Fdu7SYPFl3X+XYMQWEsIG/v4CHh3n9Kn3UrasAYINLlwTUatKEwsOVAFSoWVMDtdq6ESwtWwK1a9ugTRsBITQW/WwlmbJwTluj7QphbOjZU+bPn4/58+ejRo0a+PHHH9HZ1Hl1JqBQKLBlyxYMzhHu0bZtW7Ru3RqLFi0CAGi1WlStWhVvvfUWZs6cWWCds2bNwh9//AGVSoXk5GSo1WpMnz4dn3zyic7yQUFBmDNnTr73165dCycn8zIoM/rZv78qfvihBRQKgRkzTqNDh2iL1LtxYx2sWVMftrYazJ9/GLVqJVik3p9+aoo9e/zRvHksPv30BC4mXcTHNz+Gj50PljRYYnQ9aq0ar4S9goSsBLzv/z46uHXI3paWZoM33+yB+HgHjBkThmHDzLMrkHh+8y/IqrED3TOn4u023Yzeb/n95dj2cBt6uPfAW35vFVj+00/b47z9BqDvO4BCoK1rW0z1mwpHlWO+sueTzuO7u98hPiseNgobvFTpJQysODBboMlV53kvDB58HePHh+WrJyojCu9dfQ+p2lT08eiDN6q+YbCNDx44YsqU7sjMtMG7755B1665s6yqtWp8duszXEi+kG9fLzsvNHFpgh7uPVDfRX+in/v3XTB1ajdkZanw7rsh6No10mCbdHH4cGV8800rAMD48ZcwePBNk+tgGFPI0GbgRMIJHHx8EKFJodCCOqdKKNHYpTEauDRAbafaqOVUC+VtTPR7sSIffNAZV6+6Y+LEixg48JbJ+3/zTUscPlwFXbpEYNq0swBo1sVbb/VAVJQLXnnlIvr3N71eADh50gfz57eFj08ylizZl2vb3r1+WLy4OWrWfIJvvjmot47Llz3w4YedUL58Blau3KVTNL1/3wVTpvSAUqnFL7/sRXi4O3bv9sfFixWzy+i7hjIMwzAMw5iKWq3Eyy/3RmKiPUaPvoK1a+vDzi4Lf/yxE3Z2+QXODRvqYN26+ujc+T6mTz9j0bbExDjh9dd7wcZGgw0btkOlEpg7ty1CQnwwadJ59O17x6LH04UQps+GYoqf1NRUjB49GhEREahSpYpF6jRZcAWA6OhozJ49G6tXr8bw4cPx9ddfo3LlyhZpUK7G5RFcMzMz4eTkhL/++iuXCDtu3Dg8efIEf//9t0n1r1y5EpcuXcLXuuYPPiVvhGtkZCQaNGiA6OhoeHDqcIvTo4cKhw8rMXu2BkFBlht90mqBYcNU2L5diWrVBM6fz0Jh9fLISKBuXRtkZiqwf38WOnUSiEyKRPUfq0OlUCHx/USjszuvvbQW4/8Zj8rlKuPam9fy7bdmjQITJtjA2Vng0qUsmHu6xcUBlUd8A/SchYG1huKvF9YbtZ8QAvV/ro9bT27hz2F/YnDdwQXu888/Cjz/vA1c2myEesAYZGjoPKroVBFVylehV7kqSMtKw8rzKwEAdT3qYvXg1Wjm3SxffUlJQKVK9H1fuKBGvXq6j7vzxk4M/nMwBAR+7vszXm7+ssF2zpsnEBRkB29v+m5dXen9LG0WRm0ehb+v/Q0XOxf80OcH3Iy/iQN3DuBk1ElkaWnEVKVQ4dTLp9DYK38qTq2WftNHjyrx3HNa/P23xuyb78KFSsycSRHCq1dnYcQIy0VqG8P+/QpcvapAx45aNG7MnYiyyuO0x1hyZgkWhyxGXKpsFNratzVGNxyN4Q2Gw8vZqxhbqJ+bN4H69W2hVAosX74bI0Z0ga2tcddgiXPngLZtbaFSCVy9mgU/P+DvvxUYPtwGbm4Ct25lwcXFvPYlJQHe3jbIylIgLEydy9dr8GAVduxQIihIg9mz9d/71Gq6DiYmKnDsWBZatcp/HfjwQ5oV0a+fFlu2yJEl4eHA0qVKLF6sgq2tQFhYFqpVM++zMExRolarsXfvXvTq1cvkc5phmJIJn9dli3XrFBg3zgZVqghcu5aFWrVsEBWlwI4dWejZM39fZeBAFXbtUuLbbzWYPNmyEadaLeDuboPUVAUuXlSjbl2gUSMbXLumwK5dWejevWifoZ4VysI5nZiYCE9PT4sKrihMxq2QkBDRqVMn4ezsLD777DORnp5e6CxeOQEgtmzZkv13ZGSkACCOHTuWq9yMGTNEGzPS9q5YsUJMnz7dpH0iIiIEAPHw4UOTj8cY5tEjys4MCHH7tuXrj4+nrPSAEDt2FL6+d9+lujp3lt/TarXC6XMngSCIqw+vGl1Xu+XtBIIgPjv4mc7tWq0Q7drR8ebONb/N+/YJgWoHBIIgfL/xFdqC0kc+5fKDywJBEHaf2YmkjCSj9snKEsLPj9o8a8lBUfmbytnZyXW93tj2hkjJTNFb3+bNVFfNmgVnvfz80OcCQRC2/7MVx+4dM1g2KSlT+PomCUCIqVPpPY1WI8ZvHS8QBGH/mb3Yd2tf7n0yksSOaztE1xVdBYIgOv3WSed3+dNP1GZnZ8qOWRi0WiHefpvqs7MT4ujRwtVnLLduCTFggJxtExDCx0eIMWOEWL1aiOjoomkHY13uPrkr3tn5jnD+3Dn7nPT71k98sv8Tk65lxcn//ke/zx49NIXKktq9O9UzbRr93bHj0+vYrMK3sVs3qmvRIvm9pCQh7O3p/YsXC65j6FAqO2dO/m1ZWUJUrkzb//pL9/49etD2SZPM+wylndu3hWjZku7hScbdzphipixkPmYYJjd8XpctOnXK3TeZMIH+1iW1aDRCVKhA20+ftk57Wrak+jdtEkKtFsLWlv4u7PMYo5+ycE4nJCQIACIiIsJidRbKwatly5Y4fPgwfv31V/z666+oX78+tmzZYgkduEgYP368wejWnCxevBgNGjRAQECAlVv17LJzJ03dbNQI8Pe3fP1ubkDv3rRuhH2vQR4+BJYupfXZs+X3FQoFarubljgrJCoEJ+6fgJ3KDq+2eFVnGYUCCAyk9du3zW42mZNHtYZCqBCVFIWIROOyqPx7lTJ2da/eHS52xoV3qVTA66/T+n+/dkHEuxF4OOMhzk06h39H/YufAn/C7E6z8VqL17Bj9A781O8nONnqDzvevp2W/foVHF05q9MsDKs/DGqtGsP+HIbLDy7rLWtvD7z6KlkG/PgjcP68wLTd07AydCVUChU2PL8B3at3z7WPi50L+tbui9VDVsPZ1hlH7h3B7+d/z1Xm/n3ggw9off78wpumKxSURG3oUEq8Y+28fxkZwOefk/H7v/9SRs8uXQBHRyAmBli9mhJ5VaoE9O1LSYWY0oEQArHJsThy7whWhq7ES5tfQo3va+D7k98jRZ2Cpt5NsXboWtx8+ybmBMxBHY8iyDBQSIQA1qyh9VGjChcp8d57tPzlF0r+cPQoJT54q2AnlQJ57jla5kwqsWsXnW81a8qJHgzRty8td+7Mv23fPpp9UaEC0L+/7v2Dgmj522/A3btGN73MsG0bcOYM8O231N8wlNSDYRiGYRjDXLxIyYBVKkoSDAB9+tBS1z322jUgPp6eKZo2tU6bcibOunuXZgg5OACWClpkGGOxSMqEESNGIDw8HC+//DLGjRuHXr16WaLafHh6ekKlUiE2NjbX+7GxsfDx8bHKMSUmT56MsLAwBAcHW/U4zzL/kqaHAQOsd4yOHWl59Gjh6vn+exKYWrSQbygSkjhx/ZFxXquLTpEf8QsNX4C3i7feclWr0rIwmaYvXgSgdoIPKBvM8YjjRu337zX65wyoY9o/55VXADs74PRpICREAQ8nDzTzaYb+dfrjjdZv4PMen2PpgKXoW7uvwXq0Wllw1Sci5EShUGDl4JVoWLEhopOj0eKXFph3eF62DUBemjePw5AhWmg0wMBv/ofvT34PAPht0G8YVG+Q3uNUda2KT7qS//OMvTMQnxYPgMSfN9+kKcTt2tG6JVCpAMluOjiYRBprsHcv0KQJ8NFHQHo60K0bcP48cPAgdZD27QNmzqTfP0CC0bRp1mkLU3juJdzDkpAlGPHXCDRf2hzlF5SHzzc+6LyiMyb8PQFrLq6BRmjQo3oP7H5pN85NOodRjUfBRmlyXs1i49w54OpV6kwPHly4qWLPPUcDDcnJwAsv0HsvvUSDC4VFul/s3y+fv1u30nLIEOOsOiTR9uRJ4NGj3NtWraLlqFHQm2CxUyegZ096+Pj8c5OaXybIOWh59y59n2PH0kAqwzAMwzCmseRp2pJBg5Bte9ezJ/VpLl2iIJScHH/6+NmqFQ1oW4Ocguv1p4/ktWqBE4YyRU6hfnKZmZm4cOEC1q5dizlz5uDkyZNwdHTE/v37LdW+XNjZ2aFly5bYt09ONqHVarFv3z60b9/eKseU4AhX66JWyxE/1hRcOzzNRXXyJJClW3srkJQUioQEKLo17wOyKRGucSlxWH+JfFSntJ5isKw0Ipf3pmUKFy/Ssqk7nS/HIgoO9X2Y+hDH79OdsX8dI9TOHFSsKAsWixebtGsuzp2jqEpnZ4qyNAYXOxf8N/Y/9KvdD5maTHy4/0O0Xd4WF2LzJ8ASQuDl2eeg6vcu7tUMAgD88NwPGNt0bIHHmdpuKhpUbIC41Dh8uP9DAMDGjTSAYGsLLF9OQqmlaNIE8PGh3+GRI5arV+LDDykS/No1wNubogb37ycBCiARp3t3irA9cwbYs4fOgaVLgc2bLd8exnSytFk4fPcwZv43E01+boJq31XDG9vfwJ+X/0RoTCiSM5OhgALVXKuhZ42emNx6Mk6/ehr/jf0PvWv2hqIUGvRK0a0DBgDlC5nDS6GQo1yTkmg5fXrh6pRo2pTO39RUGvhTqyniEgByWNMbpEoVoHFjGtjZs0d+PzERkCYZjRtnuA4pynXFCuDOHf3lhABu3aJlWeHW05xnCxYA775L/+/Vq+kat25d2fqsDMMwDGNNkpPpHgoAb+TIVezhAbRuTes5+yqALLhaU77RJbjWrm294zGMPkwWXOfMmYPhw4ejfv36cHFxQbNmzTB58mQcPnwYVatWxZw5c3D48GGzG5ScnIzQ0FCEhoYCAG7fvo3Q0FDcu3cPADBt2jQsW7YMq1atwpUrV/DGG28gJSUFEyZMMPuYxsARrtbl8GEgIYEEujZtrHec+vUBV1d62L2QX3czigsXqK0+PhSRlJfaHnQ1v/644AjXX8/9igxNBlr5tkKbyoY/uCS4mhvhqtU+tRQA0KMOKc+SkGqIHdd3QCu0aOrdFH6ups+Ln/JUR16/3vwIIkmQ6N1bf9SWLnxcfPDvqH/x++DfUcGhAs5Gn0WrX1rhs4OfISMrA0fvHcUH+z7A61deR//traBp/R0A4IWK/8NbbY2bP2ynssNPgT8BAJaELMGRWyHZU49nzzZuirAu7j65i9GbRmPbtW253lco5Ag3XVOKC0NEBPDFF7T+1lsUMTh6tOGou169gBkzaP2VVwoXgc0UjvuJ9/HB3g/g/bU3uqzsgi+OfoGLDy5CqVCiQ9UO+CzgM/wz8h9cmXwFaR+m4c7UO9g7Zi8WBS5CK99Wxd18s9FoSCgDgBdftEydo0fLEa2Bgeafx3lRKOQo1127KGo8IQHw8qJoeGPRZSuwcSOQlkb3OekhRx8dO9K5m5WlP8o1I4NE4Jo1KcI3M9P49pVkpAjXRo3IpuX4cVqPi6P/+8KFxds+hmEYhikJCEHBOt98QzNodAUrrVlDg9O1a1NARk702QoUpeB69SoQFkbrLLgyxYKppq8NGzYUI0eOFPPmzRP//vuvuGth5+Hg4GABIN9r3Lhx2WV+/PFH4efnJ+zs7ESbNm3EiRMnLNoGQ3DSLOswdSoZWY8fb/1jPfccHevHH83b/48/aP+uXXVvP3rvaHbCGUOoNWrh962fQBDEynMrCzxuUpKctCghwfR237wpJ1y6HndHIAjC5n82IjUz1eB+z//5vEAQxEf7PjL9oIKSPUnG5QsWmFWFaN2a9l++3Lz9hRAiKjFKDFo3KDshkP1n9rmSdjnMdRCVpw8UaPCn+Oor45KJ5eSlzS8JBEHU/7aVgCJLeHgIYW4ewcysTNFmWZvsdp6NOptr+4YN9H00bGhe/fqYNo3qDQgwbb+MDCFatZLPi6wsy7aLMczZqLPixU0vCpv/2WT/nt2/cBejN40Wf5z/QzxMKdv3q//+o99ehQr0W7SUaf/69UI0aiREaKiFGvqUdeuovY0bCzF5Mq2/8oppdezfT/t5eVHyCSEogaMp19mjR6m8jQ0lx8tJeroQ/frlTpbXo4d5956ShFYrRPny9HkuX5bfz8iQr3/GJGZkipaykIiDYZjc8HldMklNpeTSb74pJz+WXo0aCREcLJfVaoVo2pS2ffNN/rqOHJH7Z9KzwZMnQigU9H5MjPU+h0ZDSYsBIapVK/xzJFMwZeGcLhFJsy5duoR169Zh1qxZ6N+/P/wKmwkmD926dYMQIt9r5cqV2WWmTJmCu3fvIiMjAydPnkTbtm0t2gZdsKWA9RBC9m8dOND6x5NsBcxNnCVNR6xRQ/d2yVLgXsI9pKnT9Naz8/pO3Eu4Bw9HD4xoNKLA47q4UOIvwDxbAclOoH59oKaHH3zL+SJLm4WQqBC9+2RkZWD3DRqWHFDXPK8HhQKYPJnWpSknphATQx6wgJw4zBwqlauELSO2YO3QtfBw9ECGJgOu9q4Y1XAU3vd/H9FTo/GSzd9A2HBERJg+pfqrXl/B1d4VVxJCgJa/oF4906JxczLn4BycijwFAMjQZGDYn8Oy/WEB8kVSKmmajKUiSp88oSRBgByxaix2dhRh6OJCEXvWTujFkA3Gzus70eP3HmjxSwusubgGWdosdK3WFX+P/Bux78VizdA1eLHJi/Bw8iju5loVyU5g+HD6LVqKESPoumnphA69etF18eJFOTLXWDsBiY4d6Xx78IAsV27epJkiSiVFoxpDhw40ayBvlGt6Os3e2L6dPHE//5zsXPbtI0uX6GjT2lqSiI8n6wUgd3JOOztgzhz6vDdvmj8DhmEYhmFKK++9R1YAgYHATz8B9+7RffG55+j9S5eAgADqH0VEACdOUI4HBwdg/Pj89bVtSzNL4+OBkKePm6dO0bN/9epkXWYtlErZDk1KEMoRrkxxwLbBRsKWAtYjPJwecOzs6EHU2lhbcPV08oSbgxsA4Gb8Tb317LlJhjYjG42Eg42DUccuTOIsyU6gcWNKKtW+SsE+rrtu7EJSZhIql6tcqCnHki/v5cs0bdMUpCmzLVsWPmmNQqHAqMajcOPtGzj96mk8mPEAqwatQge3DnC2c4Y0fmRO5m4fFx/M7T6X/ugxG761H5jVxkN3D2He4XkAgKX9l6K6W3XcfnIb47aOg1ZQ9nV3d3n6cc5s54Vh6VLyYWrUSLYsMIVatWSf3qAgeboQY3nC4sLQc3VPBK4NxP7b+6FSqDCq0SicfvU0Dow/gIF1B5aqhFeFIT0d2LSJ1kePLt62GEtOX7PHj0k47dHDtDrs7GjgBaBr5O+/03rPnnLCCmOQvFxXraJ7W3o6ib87d1L24O3byRrl4EGyPTh/nqYAhoeb1l5LIAQlDZQ81M1BshPw9gacnHJvc3GRr33Sb4phGIZhngVu3iTrgLQ0et58/XWydHv0iPoE165REmClEvjzT6BePeDVV2nfESPo2SQvNjZy/0ayFSgKOwGJvHZQLLgyxUGpF1wXL14Mf39/ODg4oG3btjh16pTessuWLUPnzp1RoUIFVKhQAT179jRYnikapOjW7t3pgcfatGlDN4u7d4HISNP3L0hwVSgURiXOOhJBGY+6Vutq9LELkzhLinBt3JiWHaqS8nzsvn7Bdf1lSug1ouEIKBXmXy48PUnIA4BDh0zbd/t2WvY3LV+XQdwc3NDKtxXsVLnD4STB9alltMm80eoNuGc0Bxyf4Kr/uxAmZl+JT4vHS5tfgoDAhGYT8FrL1/DXC3/BXmWPf6/9iy+OfJFd1pI+rhkZwPff0/p77xmXKV0XY8aQ6KXR0DIhofBt08WVKyQKvfoq8PPPNMKemmqdY5UkEjMSMX33dDRd0hT7b++Hg40DprWbhlvv3MLaYWtLtQ+ruWzbRhGLVasCnTsXd2uMJ+egRt++FB1iKpKP644dsuBaULKsvLRvTx5rWVnAxx9ThuHdu0mM3LFD9mNr2ZIekmrXpntnx47mD1qay/HjwGefAW+/DURFmVeHJLjqu38PG0ZLFlwZhmGYZ4n19MiHHj3oPv/zz0C/fvLgpLs7BVacPQt06kT97suXaVvOZFl5kfo7eQVXU3zrzSWn4OriQvlXGKaoKdWC64YNGzBt2jR8+umnOHv2LJo2bYo+ffrgwQPdkWUHDhzAqFGjEBwcjOPHj6Nq1aro3bs3Io1Q3dhSwHpIgusA82asm0y5cpTpHTAvCq8gwRUA6njUAQBcf6Q7cVZCegIuxNKcxU5+nYw+dmEiXCXBVRI+pQjX4xHHdQqDKZkp+OfqPwAoCrewdH2qKx88aPw+mZlyZst+/QrdhAKpVo2W5gquKqUKVS/8DAgFLmAtPtz/odGiqxACr29/HRGJEahZoSa+f44U0BaVWmBR4CIAwEfBH2H/7f0AZLHlv/8o03lhWLuWpglXrgyMGmV+PQoFddCqV6fM55KVhCUJC6PpTH//DSxfTqPt7dvTed2oETBxovn/v5KKEAKrz69G3UV1sfDEQmRpszC43mCEvRmGb/p8Y1Yyu7LC2rW0HDWKBtJKCzkFV1PtBCSka8Dx43S+lStnXl1SlOvatXS9dXamgZxu3XKXq1EDOHqUBi0fP6aHshD9jjQWZ/Nmef3vv82rQ7p/V6+ue3v//oCtLV1niiOKl2EYhmEsxaZNwKRJQEpKwWUlwbWgZLlNm1LwzJo1QN26wAsvGE54LSXOOnmSrAVOnKC/iyLCVbIUAGjA2NyAEoYpDKXo8SQ/CxcuxKuvvooJEyagQYMGWLJkCZycnPDbb7/pLL9mzRq8+eabaNasGerVq4fly5dDq9Vi3759BR6LLQWsw8OHcpSMJSMYC8JcW4GMDDkq1pDgKkW4Xn+sW3A9fv84tEKLmhVqolI54+fJmxvhmpFBWRoBOcK1RaUWsFPZIS41Tqf1wbZr25CqTkWNCjUsEjlnjuB6+DBlvvT2pggrayNFuD56ZFznRBdx59oC22lu/fwj8zH30Fyj9lt1fhX+vPwnbJQ2WDtsLcrZl8ve9nLzlzGh2QRohRYj/xqJyMRItGhBkcNJSYWLNNNqga+/pvV33im8B2b58rIItmaNZaNcw8Io4i42ljp8H35IopOXF32Oy5eBFSuAoUN1Z1ItbQghsPfmXnRa0Qljt45FTHIMarvXxs4Xd2LLiC2oXkGPavSM8PixHAH/4ovF2xZTad0aqFOHrm3melNXrZo7emPEiPzT5I2hXTtZAHZxIZuSLl10l61YEdi/n7xf09OBmTNNP545CAFs2SL/nXPdFKQIV32Cq5ubPP0xp8DLMAzDPLsIQVGfW7bQbJB+/eiZbORImoJfEFot9dXNfbYwh/v3aebZL79QMIQhLl2il50d9aELQqEgYTY8HNiwwbCQ6edH9gMaDfnCPnlClkWW9sfXRc4+EtsJMMWFRQTXa9euIauIn24zMzNx5swZ9JRMzAAolUr07NkTx40MW0xNTYVarYa7LtORp2RkZCAxMTH7lZSUBIAuFkzh2bGDbkJNm8piV1FgruB69y7ddJ2d6cFTH7U9DFsKHLlHdgKmRLcC5ke4hofTjc7VVRZt7W3s0bISqZjHI/KfM5KdwMiGI6GwwJCg9AB/8SIJJcawbRstAwOLJnrN1ZUEQ8C8KMm0tKdTXUPewGcdFwIAPjnwCb46+pXB/W48voEpO6YAAOZ0m4M2lXMPFSsUCiwOXIym3k0RlxqHEX+NgEaos0eNC+PjunMnCZnlygGvvWZ+PTlp1w7w9aX1sDDL1JlXbN23D5g7l64hMTE0EPL33ySYnDkj+8mWRrRCi81XNqPN8jbo/UdvHIs4BmdbZyzosQAX37iI52qZYbJbxkhIoCSLmZk0iCTNWigt2NhQMsCwMDkZojnkjJQ11U4gJ4sW0YPZvn00VdAQzs70AGdrS+UPHzb/uMZy4QJFp9o8tSYODqZIGVMpSHAF2FaAYRiGIbKyaCZVxYqUaHHoULnvGRlJYmO/fpQDQR+pqTQg2rEj3V8NlbUks2fLYvBPP9FzoD6kBJ59+xauT6IP6Xnlm29o2aoV9SGsjZ+fbFfIgitTXFhEwqhfvz5uSfO0ioiHDx9Co9HAO096O29vb8TExBhVxwcffABfX99com1e5s+fD1dX1+xXg6ex6Xv3cky6JShqOwEJSXA9e9a4kUmJnHYChjTIbEsBPRGu5gqu5ka45k6YJb+f7eOaJ3FWQnoCdlzfAcAydgIARXLVq0eCtbEP6Nbwby2IwtgKSL+P8uWBD3u8i8+7U+rv9/97Hz+e1J3pJSwuDKM3jUaKOgVdq3XFBx0/0FnO0dYRf73wF1ztXXE04ijmHZ6XPaW4MILrV0+14EmTSHC2FNKosuTvVBh0ia0eHvJ2hYIE3oEDgS+e2tx+9JF51hvFiVqjxsrQlWj4U0MM+3MYQqJC4GjjiLfbvI2rU67ig04fwN7GvribWew8fkwJFo8epd/s8uXF3SLzKF9ed5IJU5AsBOrWpYc5c6lZk3xgDU0LzEm1amTfAQCffmr+cY1Fijbt14+uLVlZ8oCcKRgjuA4aRAN8Z8/K5Y3l5k3gu++ADz4o2kim0kJICPmFf/ut/PruO3qtX194exyGYRhLsmIFRYc+ekQDfs2a0b1v0SLgjz8oWCE4mPokugYBo6Npht9ff9HfoaE0uKnVWrfdp04Bq1fTurMz3cv05XwQQrYTGGmZR758SIKr9B0VhZ0AQM8H0szOevWK5pgMkxeLpDE2NTFMSWDBggVYv349Dhw4AAcD2SpmzZqFadOmYdmyZVi2bBnUajVu3bqFHTuUeP31ImxwGSQzUzbQLmrB1d+fjLNjYigarqCIHomC/N8kJEuBmOQYJGYkorx9+extmZpMnIw8CaDoBNe8CbMksn1c7+eOcN0avhWZmkw0qNgAjbwamXYwA3TpQtG2Bw/SQ60hrl0Drl+nEdBevSzWhALx86PvyxzB9eZTZ4aaNekmP7vzbKSp0zD38Fy8vettONg44JUWr+B87Hn8FfYXNl3ZhPCHZBTo5uCG1UNWQ6VU6a2/lnst/NzvZ4zePBrfn/weZ8ZMh0LhgtBQ6tRVMt6dAgBF2B08SJ3Id94x/fMaolEjYO9eWew3l4LE1ry88goJR0ePAlOmAFu3WtazSQjgwQO6FuR8eXgA8+aZZ8mgFVqsu7gOH+7/EHcT7gIAXO1dMaXNFLzT9h1UdDYQTl+GEALYuBGwtydhzUZHDyUujq4H58/Td75nD9CiRdG3taTQqRN9B7VqFb032ezZwG+/0cPmwYOybYw1kATXoUPpvnD5Mk3tHDPG+Dq0WvK6BQzfwytWpM8SHEzHnT5df1mNhjx0//2XXleuyNtcXGjq6bNOUhLZzPzyC4nYhmjYEPjxR/LpZhiGKU5SUoBPPqH1uXPpXpBXMqhTh8TEEyfI93zPHgowAYBz5ygQ4P59Glz99FNgxgzql370EfUZ9ZGZScEpCQm0nvNVvjzw/PP6k20KAbz7Lq2PHUu2W19/TddWXQEsp05RP9bJyXrP4127Ut8uI4P+LirBFQC+/JIE7yFDiu6YDJMLYQEUCoW4evWqJaoymoyMDKFSqcSWLVtyvT927FgxcOBAg/t+9dVXwtXVVZw+fdrk40ZERAgAwsnpiUhNNXl3Jgd79ggBCOHjI4RGU/THHzqUjv/FF8bvM3067TN1asFlvb7yEgiCOBN1Jtf7xyOOCwRBeH7pKbRarUltTkqi4wNCJCQYv19gIO2zeHHu9yMTIwWCIJRzlCIxPTH7/b5/9BUIgvjfgf+Z1L6CWLOG2tGiRcFlFy6ksj17WrQJ+cjMzBRbt24VmZmZQggh3niDjvvhh6bXJbX5+efl97RarXhv93sCQRCKIIXw/85fIAjZL9v/2YrANYHieMRxo46RpckStX+oLRAE8f2J70Xr1nTMFStMb+/w4bTvmDGm71sQy5cX/v8XFSWEtzfV07SpEA8fGrffpUtC2NjQfps3m3/8vAQFCeHsLJ+DeV8vvmj6tSz4drBoubRl9u/B+ytv8cWRL0RCugkneBkgMZHOG+m7rFZNiG++EeLJE7lMVJQQ9evTdm9vIS5e1F1X3nOasR7S9bJrV+sd49o1OoaNjRCPHglx9iz97egoREqK8fVERNB+KpUQarXhsj/+SGU7dNBfZtMmITw9c18DbGyEaNCA1mvUKJ6+TUnhzBkhXntNCBcX+fuxsxNiwAAhRo/O/Ro1SggPD7nciBH0/5Lgc5phyh4l/byeM0e+lmdk6C934YLcV61TR4h794TYulUIJyd6r149IW7coLKrV8vXud9/113f+fNCNGmiv68JCNGrlxBpabr3X7+eyjg5CXH/vhC3bgmhUNB74eH5y0+dSttGjTLt+zGVnj3l9sfEWPdYTPFQ0s9pY0hISBAARETOTkghKbVJs+zs7NCyZctcCa+kBFjtDQybfPnll/jss8+wa9cutGplfiKg1FQFjMi1xRhAshPo1694skub4+Oa01KgILITZz3KbStw+C7Np+/k18lkb1QXF9lbx5QoV30Rrr7lfFHNtRq0QotTkacAAA9TH2Lvrb0AgBGNRpjUvoKQIqBCQwtOpiRNF+3Xz6JNKJDCWApIEa61asnvKRQKfNnrS0xpPQUCAnee3IGjjSOG1BuCNUPXIG5GHLaP3o52VdoZdQyVUoVp7acBAL498S16P0f+2fqmCunj1i3Zo/C990zb1xgaPQ2MLoylwMaNFNlar17Bka05adgQeP99Wn/rLSAx0fw2SOzeTdncU1IoktDPj6IZJk6kiAUbG0oS9oFuR4h8XIm7goHrBiJgVQDORJ9BObty+Lz757j1zi283/H9XFHxZZ2wMEoi9ddfFNHu7k5+2dOnU1T/229TRtwuXSiCsHJliqhsZLnge8ZMZs+mqO6DB4EDB6xzDClBVkAA/TaaNaPrdFoaRRMZi2QP4OenO3o6J1IkzLFjT3258xAeTtG1Dx8CFSpQ8pB16ygC+/RpmmZ66xb9bp81Hj0Chg+nRJe//EJ+hXXqkHdfZCTwzz90rcz5WruWZrVMnkz9wQ0byCZjwQI5IophGMYShIQUbMUVG0uRkQAwf77h2UuNG1M0qp8fXcdatqR7SGoqzcg5fpxmvgHASy/RfROgGVk5085kZdGxWrUi3/IKFWgWS/fu5Nc+cCB5jDs70wyy4cMp4jUn6elyP/SDD6i/VL26HNmaN7+BRkPXWwAYNcrwd1JYJFuB6tXlKGCGeSawhGpbHBGuQgixfv16YW9vL1auXCnCwsLEa6+9Jtzc3ETM02GTMWPGiJkzZ2aXX7BggbCzsxN//fWXiI6Ozn4lJSUZfUwpwhVIEC+/bPGP9Myg1Qrh70+jXFu3Fk8bjh2j41esSO0xhqZNaZ9t2wouO37reJ1RogPXDRQIgvj66NemN1oI0bgxtWHXLuPKx8fLI4qPH+ffPuqvUbnauTRkqUAQRIulRoShmkHNmgV/hwkJcoTitWtWaUY2eUfj1q41P2KrTx/ad9my/Ns0Wo34PfR38dflv0RyRnKh2pyamSo8v/Sk/9vm9QIQokKFgqO2cvL229TWPn0K1RS9JCbKv7tHj8yrY8QI2n/uXNP3TU2Vf2tvvWXe8SWSk+Xr1ZQpQqSn5y+zapX8eRcuNNCuzFTx1o63hGqOSiAIQjVHJSZvnyxik2ML18hSyvr1ctRw5cpCHD9O/7tly4Ro2DB/VIe/vxA3bxqusyyMsJcmJk+m/02XLsbfS02hbVuq/6ef5PekiJyxY42vRzpHu3c3rnz79lR+0aLc76elydFH3bsLoetn9uqrprevLPDff0L4+srRviNHChEcbNrvIjRUiE6d5HO+fn0h7t/nc5qxPFot/d6++44i1p/liPTioDju1T//LIRSSdeWb7/VX06avdGmjfHXr7t3hahdW752vf667vuDRiPEkCFUxstLiDt36FmnXTt534ED9UeBBgcL4eBA5YYOzd33nzeP3q9SJfcMkN276f1y5ah/LrF/P73v5qa7b2tJ4uIoMve336x7HKb4KAv9b2tEuJZqwVUIIX788Ufh5+cn7OzsRJs2bcSJEyeyt3Xt2lWMGzcu++9q1ao9FUtzvz799FOjj5dTcK1YUYisLAt+mGeIc+foAm9vT2JGcZCeTtPbACGuXy+4vFZLNypAiLCwgsvPOzRPIAhizGZ5vrZGqxEeX3gIBEGciDhhYG/99O1LbVi+3Ljyhw/LN19d/HDiB4EgiL5/9BVCCBGwMkAgCOLLI1+a1b6CmDiR2jNjhv4yGzbIU3OsTd6bw5EjsrBjKrVq0b7791u4kToICg4SCIJoubSlcKugFYAQR48av3/16tTWf/6xXhv9/OgYhw6Zt3+VKoX7Pvfupf0VCiFOnjSvDiGEmDZNnuZuaHxuwQK5s7xuXf7tNx7dEE1/bpptHzBo3SARHqdjftczQGamLJpJwlVsHs1ZqyXrGckSRZqqV3Ddpb/DV5qIiJDvpfv2Wbbu+/flczgqSn7/4EH5IdHYf3NQEO1j7GD5119T+YCA3O9PmSIP1kZG6t73+HHZ9sAU+5/SSnq6EO+9J5/PdesKERJifn1aLU29labpdu+uEZs2mXdOq9VC7NxJ4sqMGWT90r07TfN1c6OBnddeI0H++nXrDBowJYfYWCH++IOslKTfl/Rq3FiIv//m30BRUZT3ao1GiJkz8w/i/vFH/rLh4WQ9A9C9xhRiYug5Z/lyw7+j5GQhmjWjY1SvTvcKQIjy5YVYubLg3+Du3fJ9d9Qo0iOio2ULl7yfS6Oh63LeQURpcJCDyBhLUBb63yy4FiOLFi0S9evXF7Vq1RIAhJvbk0IJCc8qp08LMWGCPDIXGFi87enQgdqxalXBZePi5Bu0Mf69Gy9vFAiCaLusbfZ7YQ/CBIIgHOc6isws8y5Gr71GbTB2nODnn6l83766t4dEhggEQbgtcBP3E+4LRZBCIAjiTvwds9pXEFKUUZs2+st07EhlZs2yShNykffmIPn82diYNqCiVstRuXfvWqmxOYhLiRMOcx0EgiACJgQLQIiPPzZu34gIIaBUC4VrhFXFAEkoyxmZZiz37tG+KlXhBmVeeonqadrUtAhgiVOn5GiIHTsMl9VqKZoWEMLWNrf49Hf438J1vqtAEETFLyuK3Td2m96YMoJGQ1EO0vV05syC/zdRUcZdd4UoGx2+0oYkQnbqZFmxYtEiqjevl2pWFgmeAA2sGMO4caZFzN+6ReWVSiEePKD3tmyRf7eGrgdaLQl6gBC//GLc8UorYWGycCBFdVlqIP3SJdkHcfjwcJPO6bAwEljzimoFvby9Kfps97N7iS6T3L+f20NSejk5CdG7N4nv0ntt2lC0NmNdiupenZ5OXtHS/3fOHCHeeUfu6+e9lg8eLEeZWpN79yiPidSuHj1Me3745x/5uWP8eHq+ln6/uqK1f/iBtterR/eojAyaHQfw752xDGWh/80ersXI5MmTERYWhuDgYABA794CAGUaNBUhyNvr0SNaL+ukpQGrVgFt25JP34oV5DHTrBl5ehUnHTvS0hgfV8m/1dcXcHQsuHwdjzoAgOuPZQ/Xw/fIv7VdlXawVdma1FaJKlVoaayHq+Tfqs/vsIl3EzjaOOJJ+hPMPTQXAgLtq7RHNbdqZrWvICQf1zNnKHtxXg4fpgzz9vbkv1nUVKoEqFTkpRQTY/x+ERG0j50deSZZG08nT0xoNgEA8Lje1wCM93E9fBjAsNEQ71ZFjw2tseLcCqSqUy3exoYNaWmOj6t0TjZrRn5V5vLNN+T7eP488Mcfpu2rVgOvvkrZzUeNAvr2NVxeoQC+/ZZ8tdRqYPBgIORsFmbvm41B6wchISMB7au0x7lJ59C7Zm+zP1NpZ/du8h9zciJ/zvnzC/bUrFTJuOsuUzzMmkXX7CNHYFF/+82baTl0aO73VSpg0CBalzxeC0LycK1e3bjy1asDzZvT+f/PP3SNnziRtk2fbvh6oFDIZVesMO54pZH//gNatCBfdk9P4O+/gZ9/Ltw1OycNGwLLltH6xo11sXOnYd/7xEQq36ED0KAB8NVX5MVYsSLwwguUufurr+hesH8/cOkS9eNnzKB97Oyo/JYt5Df4+uvkQcuUbg4epN/pf//R382akb/l/v3A48d0T7p1i7w1nZwoa3vPnuSdKfWhmdLJkyfkf7p2LfUzVqwAPvkEWLgQePFF6rcPGyZ7qR45QtcElYo8pK1J1aqUr6JnT2DRIvIk9/Mzfv8BA4D166mtK1fK95pvv9WdG2XcOMoFEh5Ov/29e4H4eMDHh3ISMAxjJSyh2s6cOVM8NDZ9dClHshRYseJR9jQAY6M5tFoh/v1XiJYt5dEsR0cK8e/Zk0amPviAfGNeeIGmPDVtStNq/fzIc7S0cfmyHIUC0PSHl16iz1ISpuxI0SqNGxdcVsr62KmTcXWnZKZkTx1+mELnx5jNYwSCID7eb2Qoog5WrKB29O5tXPnOnam8vmyYQgjRdUVXgSAI5RylQBDE9ye+N7t9xlCtGrVJlw+tZJkwaZJVm5CNrtE4qX2mTNGXpq/XrWv5Nurj+qPr2RHJqHhZAPmnZesi8J1t2b9N6eW2wE28u+tdcfWh5WYrSNHM3bqZvq8UKVpY/9WcdeWw9DYKySLA3d2471UiLY08gOEcK+xf6579Hb+z8x2RkWUg1a0Ozp4lq4rmzSlaa88e4yM9Syr9+tH3+s471qm/LIywl0YkX+gOHSxzf4+Lk6d16vLt3b6dtvn6Gue9KFmUHD9ufBvmzqV9evWSfUVbtTKcsVoiOlpuvzE2RKUNrVaIFi3o8/XsSZ/XWrzxRpYAhKhQQStu39bdlt9/p2u11N9UqShCbetW420n0tLovi/5EgOUofzIEYt+HKaI0GrJn1U6D5s0EaKgCZkxMXRvkqZrV6yoO/8BU3isfa++e1f2gi9XLn/Uemam/MxRoQJF1Es+qkX1DGIJ1qwh2x2AfLMNIV3bBg0iexWA7t0MYwnKQv+7xFoKPEtIguvduw+zp8WfP294H62WEgS1aiV34KQpAKZOc7Lg/75ImDOH2l6pkhDz55smWhQFMTHUPoWCkksZQjIiNyUJRuVvKgsEQRyPoCe86t9VFwhCoaYTS8JegwYFl9Vq5WlS587pLzdz78xsUUgRpBBRiVH6C1uAsWN1WwaEhspTOG/csGoTstF1c+jShdqhy4dTH0uW0D79+lmhkQYYumGoQBCE+4SJAiDfO0OkqdOE3Xs1BYIg+vzwhvjiyBfZv0vpFbgmUCRlGJ9MUB8hIfSdeHqavq80MGXK/0AfH39MdU2ebPw+16/L1icrV5p+zNA7t4Rimh9ZiHzmLNZfXG9yHefP5xYQpJe9PQ3IzZ9f+h4Eb92SHwzCrWRfWxY6fKWRyEj5nNm+3bh99u8X4tdfdVtK/PabbAeii/R02Vf9RAGW6Onp8u9OXyISXVy5kvvcK1fOtHvTgAG0nyHP8tLK0aP02RwchLB2zEVSUqaoXfuxAOjekJYmb7t3T7avAcjr+csvCy8A798v+5ArFEK8/771k8owliM5Ofc08tGjTbO6uHtXtgV5803rtfNZxpr36vh4OXGqry89X+giOVkWWaX7ibOzdQeQrMGmTWSbU1C7w8Lk5yzJN7Y0BnQxJZOy0P9mS4EShLMz0PvpjFB9tgJCADt20FT6/v2BkBDa74MPgKgomlZ/4wYQHAz8/jswdy7wzjs01eH772nK065dwIkTQJMmNM1pyBCaom8phADmzaMpndYgJISWH3wAzJwJeHlZ5zjm4u0N1KxJ38PJk4bLSpYCNWoYX3+2rcCj64hMjMTtJ7ehVCjRrko7M1tsmqVAZCRNp1GpgPr19ZfrULVD9no3/26oVK6S2e0zBslW4ODB3O9L03deeIH+L8WFNKXn3j3j97lxg5ZF3e4ZHWYAABL8/gBcorF9u+Hyn+37BpkuN4FEX/w05Au83/F9XH/rOraP3o7+dfpDAQV2XN+BPTf3FLpt9evT1NqHD4EHD4zfLyWFpqgCsu1HYShXjpaJicaVFwJ47TW6RvfsCYwda9rx7j65i0FbAiDK3wMe1caMCqcwotEIk+oIC6NjP34MtGlD94jx48muIiODpoPNmkXTmrVa09pXnCxZQt9vz55A3brF3RrGkvj6ApMn0/r06WSrYYjwcJq2/fLLdE+4ezf3dskqIK+dgIS9PRAYSOuS9YA+7t2j352Tk2n9kHr1aGq6xNKlpl3jJVuB338v+PsobfzwAy1ffBHw8LDuseztgfffPw0PD4EzZ4CpU+m6t3Qp2Q7s2EF2APPmkU3AjBk0TbYwBAQAFy7QdVcI4MsvyRZryxbL9sMZy3PzJllErF1L/d/vvqNnKlOsLvz8yB4DoKX0PMOUfLRa6rfdvAlUq0bP0U2b6i7r7Axs307Xecnm7L33Cn/9KGqGDiVbgYLaXb8+9b+0WrqO+fsD7cx/JGUYxggKcE3LT3x8PCpUqGCNtpQ6hgwhX68tW0gkzYkQwPvvA1+TtSKcnIApU+giXrGiXK5mTeM671u3UkcvJIQ8BVevJiGjsJw6BXz4Ia3360fCrqUQAjh9mtZbt7ZcvZamQwe6KR87Rg9/+pAEV2P93wCgtnttBN8JxrVH12CnsgMANPNphvL25c1uryS4JibSq7yBqiTvqTp16IFFHzkF4JGNRprdNmORBNfTp4HUVDo/btwA/vyT3p850+pNMIgkuOYVAAxx8yYti1pwbVelHTpW7YijEUeBtj9i5855UKsBWx0WwfcS7uHrk58DAHwvfY0aVUiJVClVCKwdiMDagZjw9wSsDF2Ji7EXMbS+HqXDSJycaIDi5k16CO7e3bj9Tp8GNBr6rVetWqgmAJAFV12ewbpYuZIGwhwdSSA05VobkRCBgFUBuJtwFxW0tRG/8gCiR/qa1N5r14AePYC4OPKd270bcHMDxoyh6+rVq+S99eGHNFC0ejV5c5V00tOBX3+ldUmYY8oWH31E4mJ4OPDTTzSIrAshgDfflEXIY8fIV/HXX+nBMSmJ/OwA/YIrQP2wDRuoH7Zggf5zVfJv9fc3ve80dizdk15+mbycTaFfPxJ4Y2NpAH3AANP2L6lERgKbNtF6UXmtV6yYhlWrNBgwwAZLl9JvRurjtG9Pvx1DA8vm4OpKvoiDBtEg3MWL9Ht0ciKxf+hQ+h/n7IdptTTAGBEBREfTbzk5mQYSk5PplZ5O92hbWxKKpaWjIz0jeHnRy9ubxGyVyrKfqyxz/TrQqRP9D7y8gI0bgS5dzKurWzfgpZdIrH3jDRLu+H9R8lmwAPj3X3ru2bSp4H6kuzv1swIC6O/p063fxuJkyhTZz3jkSMvoCQzD6MdkwbVOnTr47LPPMGnSJCieoTN08eLFWLx4MdQ5QhT69ydT6tBQ4M4d6sgD9CDxwQey2Pruu4WP7qxenToNvXoBa9bQg8l775lfn8TixfL6mjWWFVwjIynpkEpF7S2pdOhAgkVBibMKFeH6+DqepD8BAHSq2smMVsq4uJD48uQJRbnmjL7Jy6VLtNSXMEuionNF9K/TH5cfXMbwBsML1T5jqFGDIvUiI8movkcPSmSh1dJDjL6R6KKiWjVamhLhWlyCK0BRrkc3HIWi9c9IODwbhw+76BQ3p+2ehkyRBtzpgsBquoX1xl6NAQAXH1gmU0TDhvTdXL5svOB69CgtO3QwXM5YpIdhYyNcV66k5Ycfmvb/jEyMRMCqANx+chs1K9TE+17BmJTka1JkzM2b9D3FxND1eM8eOt8lFAqKuqtXjx7a33+f7i9Dh8rCckllwwZKFlm1Kt0/mbKHmxvw+eckTgUFUfSjp2f+cmvX0qCGgwNFF82eTYMHw4ZRoqJ27SiSu3ZtOfmeLvr2JaHq+nWKCtdX1tSEWTmZPp3uUS1amL6vrS0JNgsXAr/9VnYE1yVLKNlM585Fe7/u3Vvgk0+AOXNI/HRyot/bW29ZVwgbPJjuR198Afz1F/UN/vqLXnZ2JOhlZJDIGhlp2WhmhYKE1xo15CCNmjWBWrXo/LB2dHFpIiaGAicePKDf5fbthU9i+tVXJN6FhAC//ELCK1Ny+e8/4OOPaX3RIqBlS+P2q1IFuHKFzreyLqr3708zjG7dooF8hmGsi8mWAtOmTcP777+P5s2b4/Dhw9ZoU4lk8uTJCAsLQ3BwcPZ7np7U2QQoMytAYuvMmXSDBijCY+FCy0ylDwigaTEACbq7dhWuvrg4egCWWLvWslNTpejWhg2pU1xSkUSdEycoqk4XarUsvpkiuNb2qA0AuPboGo5EHAEAdPIrnOAKyKO1ERGGy0nRH40bF1znv6P+xc23b6KCo/Uj2BWK3LYC0dGyyFXc0a2A6ZYCQhSv4Dqg7gDU8agD4fAEGDIG6//NP39/78292HRlE6BVATsWoWsX3QNmTbxp1MVSgqsk9l++bPw+0uCHJewEANMjXB8/pmXbtsYfIzopGgGrAnAz/iaqu1VH8Lhg9GlPT3qXLpE4WhB375LYGhlJAyl79xp+mH77bXrojokh0aGkIw3wvf46ZQtmyiYTJ5LY8eRJ/tk/AGVFnjaN1j/+mH7zhw9TvwYgMU+aij90qOHom/LlaSAakC0IdFEYwdXGBmjVSnfWZ2OYMIGW27aZZq1SUsnIoKn8AF2DipqPP6YI+RdeoD7O1KlFI5B4eQHffEMBFiEhNEhQty6QmUkiz+HDtE2tpt+sry/N7urZkwTbl14CJk0iAf/DD+n3/u67FG322mtkXTB8OEVVNmhAzxgKBfUvYmLovrh6NQ1kjBlDUb2enhQR27kz1bFwIbBzJ/3eS5PVjCVITKQBmNu3qR+2e3fhxVaApmhL99dZsyhanSkerl0Dvv2W+ki6iIigWQhaLd1DXnnFtPptbMq+2ArQZzx0CDh/3nDQDsMwFsIc49eoqCgxfvx4oVKpxMiRI8X9+/ctZipb0pGSZj18miHgu+/IcLprV0pQNHOmbNC+aJHlj6/VCvHyy1S/q2vB2TYNISWBatZMTqwUHGypllJCJIDaW5LJyhKifHlq6+nTusvcuCEnhzAmG7LElbgrlDRnrqNQzlEKBMEiCamkrJrLlxsu16wZlduypdCHtDhLl1LbunShhCKAEB07Fn07dBl8X75M7XFzM66OnMnXiiupxuawzUIZpBIIglDO9BB/nF8jtE9ThWdkZYh6i+pRUrS+7whAiDt3dNcTkxRDdcxRitTM1EK3a80a0/63Gg1lizV0PprKgQNUX716xpWvVo3KnzxpXPmYpJjs77fat9XEnXj6crVaITw8qK5TpwzXkZwsJ3ioU8f4hA3//kv72NlRoq+SyqlTcjutnTyxLJj2l3akc06pzJ9Y9I035PMxIyP3tj17KEGo1I8y5hxcvpzKtmihv8zw4VRm4ULTP4slaNOGjv/NN8VzfEuyahV9lipVdCc7swYl9ZzWaqm/8NNPlODxyBFKtmSpZqrV1L8ICRFiwwbqt0+cSM8cVaoYTrTr4EAJ50aMECIoSIj16+meGhlp/P9Nq6VEhxs2CPHee0J060aJHL29hahfX4gOHYTo358SoU6bJsQ//xRPHyg9XYiAAPrcXl6WT7qalUXXF1MT5zKGMeW83rlTTmplZyfEpEn025RIT5evs82bC5Fa+O4rwzAmUlLv1aZgjaRZZgmuEiEhIaJTp07C2dlZfPbZZyL9GUjfmVdwvX1bfqiYPFnu6Pz4o/XakJ5OnRzpgSUx0fQ6srLk7KsrVwrx6quWF0d79aI6lyyxXJ3WYuBAauuCBbq379lD2+vXN63ejKyMbKEVQRA1v69Z+MYKIV57jdrz6af6y6jVlM0csHzn0xKEh8sZ111caH3btqJvh66bQ1KSfC4nJBRch5StuWpVKzbUCI7cPCMUbzTN/r31X9tf3E+4L7488qVAEITb514CDvEFtrPilxUFgiBCIkMK3abQUFm8fqr/GkQSu52cLPfQeuaMnKnWGCTB98qVgssmpieKxj81FgiCqLqwqrj5+Gau7X36UF0//WS4ni1bqFylSkKYMoap1crHGDjQ+P2KmnHjqI0vvmj9Y5WFDl9Z4Pnn6X8eECCf+ydP0sCUoQHemBgSicaMMW6AMzZWrlPfudOqVfEOPv78Mx2/YUPjroOGiIkR4sEDy7TLVLRa+bucN6/ojsvntG6Sk4U4e5YGNj/+mM65Ro1IkDIkxiqVdK9p2VKIAQNoQGLIEFp/7jkhevYUonNnecDQlJerK13vt2/PP6BiDbKy5GuNiwvd761BzmvXgQO5t6nVQuzfL8RnnwmxdWvRfO6ygLHn9eLF9JuVBHXpt6ZS0W8tPFyIN9+k9ypUyC3EMgxTdJSFe7U1BFczJ0gRLVu2xOHDh/Hrr7/i119/Rf369bHF0JyuMoi/P9C8OU1fkKZL/vADTRGyFpIJeOXKlJhC8oo1hW3baLq0hwcwYgRNdQLIJ9aYqa8FIYSc0bMkJ8yS6NGDlvv26d5ujn8rANip7FDNtVr235awEwDkxFn37+svc/06Tf1zcjJvGqW1qVOHfMkyMiiJROPGcsbp4sbFhUz0AeNsBSQ7gVq1rNcmY+hYowX63DsN7JsLFeyw7do2NPipAeYcnAMA6JLxBZDuVmACicbe5EFxIfZCodtUty5NX3ryBIiKKri8ZCfQpo3uxF/mYIqlgBCy16uhhHQAoBVajNkyBhcfXISPiw/2j9uPGhVyXyRataLlmTOG69q/n5ZDhpg2DVKhoCl2NjaUxFFKNFSSePQIWL+e1jlZ1rPDV19RfyU4mBJ/ZmWRnYQQNCW6Wzfd+3l70+/l99+Nm8bv5SXbf2zfrrtMYSwFLMHIkeRXe/my/jYaw4MHZNPUrBndO4uakyepb2dvTwlcmeLF2ZmeQUaPBv73P+rDX7xICbquXyfv0S+/pOnV7dvTvUWlomeW6Gi6L/37L+23ZQut79olWyQ8ekT34VatyLv011+Bc+doKnJwMD2LLFtG3rZvvEE2CgkJwKpVlEzM25ssNTZvNt5D3RSEoMR8f/1F7dy61TyvZWNo04ZsGwBK+Pf4MX1vY8bQNah7d7K7GDwYqFSJyhw7Rm1kzEOjIcuQyZPpNzt+PNkGHDwI9O5N21etomR5P/1E+/zxR8l85mEY5tmlUIKrxIgRIxAeHo6XX34Z48aNQy/JUOsZYfBgef2774omY6uPDz1kA+TZ9PChaftL4vDLL9NDQKdO5AuamEhibGG5eZN82uzsCk7YVBKQBNcjR3QLztLDmqmCKyAnzgIsJ7hKHq6GBFcpYVbDhuZ7z1kThSJ35tiZM0tWpkxTfFxv3KBlcfi35mVQf1vg8IdoePgc2lZui8SMRKSoU9CuSjskHh4LQPae1oclE2c5OMhCtDE+rpLgaqmEWYAsnCYnF+xrl5YmezkXJLh+Gvwp/r76N+xV9tg6YitquedX3KWEDQUlzpLswaUsuaZQv748yDd1qmUTtliC334jcah5c0qGxDwb+PvLyT2nT6c+y7lzlFjLnIFiQ0jJqHT1X5KSSDgCiu9B3M1NFmtefJGSs5jDF1/QZ4mKAs6etVjzjOaHH2g5erTuZGhMycDGhu67/fsDM2aQUHrsGPUZMzJyi61Ll1JyoSVLgOXLyU//jz9o0OP0aTp/Tp8mQWviRBL7mzShAZOhQ8kn8/33aXtEBAm1b71FzylPnlB9w4ZRgEfXrpRBPjS0cEJkYiK1ffx4ep5RKKjNUl/eWsybR7/7sDBavvACHTc+nv4eNoxE58ePgZ9/Jh/6WrWATz+lMozxJCUBgwYB339Pf8+fT30JKUnd7t00ADRggPxb+vjjkhO4wTAMk01hwmMzMjLE+fPnxZo1a8SsWbPEgAEDhJeXl1AqlZaKwC1x5LUUEIK89vr0EeKXX4q2LRoN+dQAQkyfbvx+0lRuhSL3tAvJf3bQoMK3be1aqqtNm8LXVRRotUL4+FCb9+/Pv13yf/v2W9PrnrJ9SvYU7ytxRsxRNoK9e6k9DRroL/Pxx1Rm4kSLHNIq/PQTtbFGjaLzgsuLvukPgwYZNxVcCJomDQgxf7512mgKERHy+R0ZlSW+Pf6teO6P58T5qCvC0ZG2hYUZrmP5meUCQRA9f+9pkTYNG2a8f2KdOrK9xN0nd0WLpS3E6E2j803VN4WUFHkKWkEWLNHR8vdnaOrv+ovrs8/rVaGr9Ja7d0+e+qbPUyw2Vm5fXJwRH0gH8fFCeHpSHd9/b14d1iArS4jq1Y3znLYUZWFKU1khKYmsPHJOObaGzdD581S3o2P+80za5u5u+eOaQkYGTdMGyK85RzfSKCIjyZdT+h6//NI67TR0fBsbOvbZs0V7bD6nSx9ZWTT1/u235ft6zpeXlxDdu5On87ffkgXB9et0nqSlkZ3TgwdkE3LrFlmQfPSREO3b0/00Z10//FB0n0vyMAaEqF2bvG0PHaLPK33uvXvJ69XZWS7brBndpxkZXed1crIQu3YJ0aSJ7EW8caPhes6fF2LzZtNybDAMY3nKwr3aGpYCJucJnjNnDi5duoRLly7h5s2byMrKgqurKxo1aoQmTZogMDAQTZo0sbwyXILx8aEpOEWNUkmZMwMDaYT33XeNm4oqTbvo1y93tMdLL9HI844dNDorTak2h9JkJwDQ6HiPHsCaNWQrkDfKzFxLAUCOcPV08kRdj7qFbClhjKXAxafBiY0bW+SQVmHCBPoMQ4eWvKzlpkS4SpYCJSHCtUoViqo8cwbYtVOFqROnYmq7qTh+nKI3PT2BevUM1yFZClyMLXyEK0BR1ps2yVHX+nj4kLLQAjT98avTP+Ns9FmcjT6LjZc3YnLryfioy0fwcPIw6fiOjnS91GopakKyGNBFTjsBfRHXZ6PPYsLflHr8vfbvYWzTsXrrq1KFphs+eABcuCBPfc7JgQO0bNLE/KgxNze6H0yaRNE0JSUCbdcumiFQoQJlD2aeLVxcqF8x9ukp0q6ddaaiN25MMz8iIsieo18/eVtx2wlI2NnRdbBNG7pnPP88WYAYa50ybx7NwFGpKAr/6FGKXiwqli4lW4hOnShanWEMoVJRRGvXrvT3rVsUlbhzJ52jDx7QUrLTMZWaNanfPnQo0KeP5dpdEGPH0rOAhwf1pfL2E1QqoGdPev30E/D338C0aRTV278/nfNOTkXX3pJAaipZHikU1C+RXuXLA2q1AkePKnDwID1/nTghz9Lx9qb92rQxXH+TJvRiGIYpiZg80Xjjxo2wsbHB2LFjsXnzZty5cwfx8fE4fPgwFi9ejNdffx0dLDkXlDHIc8/RlJX0dGDu3ILLJyfT9B4gv8+s5AumVpMvUWE4fZqWpUVwBQz7uBZGcO1dszfsVfYY22QsFBaaMy8JromJun2xtFqyRwBkD8mSiIMDiUTStOuSRGkVXAF5eu2//8rvHT5My06dCrZuaFixIRRQIDYlFg9SHhS6PQ0b0rIgS4Hjx2lZvz5QoYLAn2F/AgDqedaDWqvGdye/Q80fauLLo18iPct4s2mFQrYHKMjHtSD/1tjkWAxaPwhpWWl4rtZzWNBzQYHHLshWQHrg7N7dcNsK4uWX6Rr+5AnwySeFq8tSSPY1EyY8ew+ZDPHii/TbdnGhacvWsLhRKEjMAPLbChTm/m1pKlak67KLCw20vPWWcVOr794FfvmF1j/7jJZHjxadP2RGBgmuQNHYZjFljxo1yOf1n3/IFuPYMXoemT2bBh8aN6Y+YV5sbOjeUbkyDST++itw5w5ZOS1dWrRiq0SnTtRPKagv5exMbd69mwZFjx4lgTgzs0iaWewIQYNM9evTgOvIkSREN2tGzzHly9tixIj+CAiwQVAQ9VPVaup/v/IKcOpUwWIrwzBMScfkbu+lS5ewbt06zJo1C/3794efpEqUcRYvXowGDRogwByDPSuiUFDUA0DeS9KDhT7WrCFBoVYtQJfVrpQ8648/zG+TRiN7i5VksS8vkuB6+jSZ/kvEx8veS+ZEyNT1rIvk2cn4qvdXhW/kU1xcqPMG6I5yDQ2laMFy5XRH1DEFU+1prrO7dw2XS0oC4uJovaQIrgMH0nLPHopqBYBDh2hZUMIsAHC2c0ZNd/owlohylXycL182LBAcPUrLDh0oivRW/C042jgi5NUQ7H5pN5p4N0FCRgI++O8D1F1UF0fvHTW6DVJUa0GJOwwJrhlZGRj651DcT7yPuh51sW7YOqiUqgKPXVDirML4t+ZEpSIfcYDEmfDwwtVXWLRaSr4CkBjMPJsolRTpHBUFNG1qvePk9HHNeZ0pKRGuEo0aAevWUf9t6VJ5UMIQc+eSEBEQQH64Dg65ZwRYmy1bgNhYEr2GDCmaYzJlF3t7msUybhwNum/cSDNAUlLod52QQIEkGg397lNSqK+7Zg35yEr9s9JC06aULM/JicTXF1+UveLLKleuUGKr55+nwIUqVSjauWFDilxVPe06abVKeHoKvPACXQ9v3CBBfdkyOfCBYRimNFMCU+mUTCZPnoywsDAES0/GJYguXWiENysLCArSX04IuWP/5pu6o0xGjaKHgCNH5IcUU7lyhTpHzs4FT10uSfj5AbVrUyfo4EH5fel78PKiz2QONkobKBWWPd2kxFkREfm3SZnKAwIsl+n9WcPYCFcputXDA3B1tW6bjEWKHkhNpehJrVYWMwtKmCVhycRZtWvT7zA5LQOBq4Zh1n+zdJaTEmZ17Aj8eZmiW/vX6Q9nO2f0rtkbZ187i5WDVqJK+Sq4l3AP3xz/xug2WCLC9a2db+FYxDG42rvin1H/wM3BzahjG4pwjYwk0USpNE4ML4iuXUlw12iADz4ofH2F4eFDelhWKOg3wDy72NoatvKwBAEBJGjcv09Z1CVKmuAKUDTuF1/Q+tSpwN69+svevAmsWEHrn31G1gRS1Jc0k8Uc0tMLTiIoIR1nxAjuUzDWQ6mkvlT58iTKlsSEr+bSoQOwdSudv3/9RfY/RRWhXhiEoKCTS5dILF6xggaAJk+mPsZPP5GYfOmSPOvuvfdoiv9//9H/8eOPgatXKar/0iUgJob6Bg8eqLFs2W7cv5+FDRsosWDNmiUrgS7DMExhMelWVr16ddSoUcPk1w9SWlPGakh2An/8oX/a7uHD5Ovp6EiZPXXh6ytHeq5da15bJDuBli3lEczSgi5bgZI0HTEnhnxcJcG1d++ia09ZQxJcIyNpMEMfN27Qslb+BPXFhkKR21bg0iWaZu7iQmKsMWQLrhaIcLW1BerWBVA9GLvubsaCowtw+UHuC1VmpnztaN9ethN4oeEL2WVUShXGNRuH75+jtLXRydFGt8HUCNe84tCWK1uw7OwyKKDAhuc3ZHszG4MU4Xr5MongOZHG8Fq0kKPWC8sXX9C1959/5Mjm4iD66b/H05NFGsb6ODjQdFUgt61ASRRcARIlxo2jwZHnn6fzVRf/+x+VkSykAJrSDMgDaaZy5Qr59BtrD3DlCi3ZJ5FhzKdXL4puVyrJGuG990qu6HrvHvDRRxTV7u5Olg/PPUcRxh9/TELrl1+S8Nq/P213dSXB/JtvqN88cCAQFkbXsLyWQgoF9XkqVkwvU8I6wzBMXkxKU7NSMv80EX9/f7P2Y4ynVSvyBdq8mW6Emzfn3h4dLYuyL75ICUz08dJLNCr5xx/krWTqSGNpS5iVkx49yGOuNAiuUoRrXsE1JUV+CNNlG8EYh7c3RSJkZpLoqm8KW0nzb5UYMAD4+WcSXCUP1Q4djE9Olp04ywIRrgC14dIjeYbA9ye/xy8Dfsn++9w5irjy8AASXE7jzpM7cLJ1QmDtwHx1+bj4AABikmOMPr4koJoT4RqTHIPXtr0GAHi/4/voU8s00zhfX0quGBNDkXft28vbLOXfmpN69ShS5Oef6YHuxAn9kUJpaXS96M1yJOkAAEqZSURBVNiRBuMsiSS4Vqpk2XoZRh8DBpBwuW0biQVClFzBVbIUuHOHZtUMGkTe+l99JXtZhofLFk//+5+8ryS8mhvhunkznftbtxpnaRAWRsv69c07HsMwxNChZAE3cSKwcCFFh/brR69OnajfaU0yMui6qMsvV6ulKNaff6Z25YyAd3cn8dXXl5aVKlF/6u5det27RwmXs7JoRsv33wN9+1r3szAMw5QGTBJcu0qpJpkSyWefkc/Wli0UKdasGd0wf/2VsoJqNNTBnzzZcD1DhgCvv04d/XPnKPLKFEpjwiyJgAD6ji5fJnHEx6fkCq5ShGteS4FDh0gkrFaNp/EWBqWSRO2bN6kjWdoE14AAssCIigIWLaL3jLUTAOQI18txl6EV2kJbYjRsCCBCFlxXX1iNeT3mwdPJE4BsJ9ChA7DxaXTrgDoD4GSbP9OSt7M3AEpgJYQwKhmduZYCQgi8/M/LeJj6EE29m2JOtzkFHisvUuKs7dtpQEqX4Gppe/BPPwVWr6br8Z9/UrKKvDx5QlHwp09TMp+33ya7GXd3y7SBBVemqAl8Oj5z6hR5jioUFFWuUJRM30d7exI4Zs8m8WXRIrqHr1sHNGhANlFaLYmxOftU7dvTZ7p+nbK9e3mZdlxJqI2KIpHE0DkfH0/9IYAFV4axBBMm0IDH1Kk01f7qVTr/y5ene/Jzz9H5X7Mm3ZsL6uIIQffz6Gg6V/UtY2LofAdoxpOXF9Xv5UWD3QcP5raT696dEp0FBhqX9DIpia5H1aoZP7jPMAxT1uEg/jJEgwZy0qsXXyRBbsgQivTQaCgiYsuWgqcUly9PnXvA9ORZmZmyd1ppSpgl4eEBNG9O65IQInU+Sprgqi/CNaedAPsgFQ5jfFxLquDq4CBn75USq5jiEVrLvRYcbByQqk7FrfgCsvEZQfX6CUAlyhpVo0INpGel45czcoSrJLi2by+y/VtHNByhsy5vFxJc07LSkJRZgIL6FHOTZv1y5hfsuL4D9ip7/DH0D9jb2Bt1vLxI18OcPq63b1NkiI2NPEXYUnh7yx6us2ZRVEtO4uMpAl4aIIuLo9kRfn70EFhQsjhjYMGVKWp8fWlwQwhgxw55wLRyZRI3SyL29jQFd+dOEj4uXKDrxUcfARs2UJmc0a0AzVKSZi6Yaiug0cjXW0C/DZWEZCdQtar1fXgZ5lnhzTfpvrtxI9m8eXlR/+Ovv4BXXqHBZ29v6os0bUrPc2+8Qc95/frRM13DhnRtc3SkQZOGDWmm3ksv0eyWb76hRGP791OUuiS2AkByMl0fT56kmVArV1KfxM2N+gDh4TTb7/nnjRNbAbo+1KzJYivDMExOWHAtYwQF0Y1Oinrw9gZmzKAO85EjspBaEJJwu2GDaf5CFy6Q6OruXvIESmORfFyl7NqlLcJVSr7BdgKFR4qIMiQ+lVTBFZB9XIHciVaMQaVUoWFFeqK/EHuh0G1J9TgCKLVQPK6FT7tQlOji04uRqcmEELJo4NboJCISI+Bi54Lnaj2nsy4XOxc421IGu9jkWKOOb6ylgLS9fHng+qPrmLZnGgBgfo/5aOTVyKhj6UISXM+ckd+TBnXatKFoE0szbRoJUHfuyFHOAD109exJ4q+nJ7VpzRoajEtJoamANWuSv2RysvnHZ8GVKQ7696fltm0l105AF889RwPWvXpR9Nvnn9P7w4fr9k4118dVSm6T829DSHYCDRqYdhyGYQzj6kqC5ooVdL88eRL45BOgWzca/FQo6B584QLZfyxZQvfqHTto0CQsjKLUpQFVNzeKQg8IoCTI06aRz+rq1fRscPEiJbOMj6eB+CNHKBBn6VKaJfn772Sh9e23T333GYZhmELDY1BljBo16MYZHEw38cBA85KV9OhBU6qjomTh1hikaKlWrUpvdGWPHuShtm8fRYLcuUPvl1TBNWeEa2QkRasoFLJwzJhPQRGumZmy4F0SBdd+/ei3IASJero8uwzR2LsxzkSfwcXYixhaf2ih2hKeSXYC4nYAWju9AB+XGYhKisLGyxvhE/cioqNpsOiKkqJbB9YdCEdb/aaiPi4+uBl/EzHJMajtUbB3hqmWAs7lsjBmyxikqlPRvXp3vNPunYI/pAFatqTllSskajo7ywmzLOnfmhMnJ/LunjiRlhMm0G+hVy+yi6lYkUTfRo3IOmbUKBpo+vJLWv7+Owmy33xj3vFZcGWKgwEDgDlzaLZHY3JGKRWCK0A2Rrt20Tk3ezb1w4KCdJft2JEEGFN9XPOWN1ZwZTsBhrEeSiX103IOjGdk0DPIrVs0uB8XRyKtm5v8qlCBXj4+pvXx3NzYdoxhGKYo4AjXMsjEiTSaOWiQ+ZmhHR3lrOsFdcZzUpoTZkl06kTf2717wIEDZABvZ0eRYiUJSXBNTJRFIim6tVUry/kwPssUJLjeuUP+ek5O1NktaVSsKPuFmuLfKiH5uFoicdbBu0/VxTvdcO2KHSa3JjPp9zZ9h569KIy+dx8tNl8lwfWFBi8YrE+yFYhNMS3C1VhLgaPKeTgZeRKu9q5YOWhloT1sK1Wia4hWC4SGkvBpLf/WnIwdS6LTkyc026FnTxJbvbxI8G2UI2hXoSAxdu9eYNMmem/x4vy2JcbCgitTHDRvTr+55GQaNABKj+AKkPAyYwZFoIWG6o8slRJnnT1LPrXGIgmuderQkiNcGaZkYm9PkaZ9+1JCvTlzKGp14kRKvtW9O13v/P1NH1BnGIZhigYWXBm9SA/ipgiuOSNcSyvOzrJItWwZLf39AZWq2JqkExcXGqEGZEFEElx79y6WJpU5JEsBfYLrjRu0rFmz5EZ0f/UVRbu/9Zbp+1pKcI1Pi8e56HP0x+0AXL4MdHSYBIXGATHKEKDKMYwfD7z91XFEJkWinF059KnVx2CdPi6kcMckxxjVBmMtBRITAfiexrYkMk38qd9PqOpa1ahjFEROH9dr10iQtLfPnUTL0qhU9BsAgN9+IwHH25vEVskDUhdDhgBdu1KETV7/SGNhwZUpDpRKiu4HSpelQF6qVzccVervT4M4arXc9yoIIYDDh2n99ddpeemSYesoycOVBVeGYRiGYRjTeCYFV39/fzRp0gTNmjVDgDVDi0o5pgquKSly8oXSHOEKUBQYAGzeTMuSZicgISXOioigyDkWXC2LFOF6967uB9KS7N8q0aEDJWUwR/Rq7E2C643HN5CmTjO7DYfuHoKAQEVFXSC5EpYtA/p0qggRSmbR7ad9hxUrgB13KLp1UL1BcLAxHK7h40yCq7EerpKlQEERrgmJWmDAJGihwYiGIzCq0Sij6jcGyVYgJESObm3fnmYUWJM+fWRPZx8fitwvSDxRKIB582j9t9/kxGvGIgQLrkzxkdO/GiidgmtBKBRylKuxPq737pH1kI0NeTQrleTpHKNn3CopSR5wZEsBhmEYhmEY03gmBVcAOHbsGEJDQxEsmegx+TBVcD13jkS/SpUoa2ZpRvI/VatpWVIf1nL6uJ4/T/5Ozs5Au3bF266ygiRoJyfTlOy8lAbBtTB4O3vD08kTWqFFWFyY2fUE36HrbEsPGuC6c4fOrQAn8kU9mbgZt+NvY2PYRgDAiIYjCm7bU0sBS0e4RrtuASqdg5OqHBYFLoLCgqHLORNnWdu/NS+rV9N0xCNHgHr1jNunQwdKQKTRUCIPU0hIANLTaZ0FV6ao6dGDosclSuqgaWGREmcZ6+MqlWvRgmyHJA9Hff288HBaenuzTRHDMAzDMIypPLOCK1MwOQVXQ9PNJMqCf6tE69a5s4aX1Ic1SRC8f1+Obg0IIM9ZpvA4OpLXJaDbVqCsC64KhSLbVuBC7AWz6zlw5wAAYEizANjZkfi5YgWwb10j9KzRE1qhxZgtYxCdHA1Xe1f0qtGrwDqzLQVSjBNcjUmapRVaxDX6FAAwpvZUeDp5GlW3sUgRruHhlJQKsK5/a068vUk0NfW3+vnnFEm3YQMNqhmLFN3q6mr9CF6GyYuzszyYURI92C2FFOF67BgNeBeEJLhKQm1BA+vs38owDMMwDGM+JU5wPXToEAYMGABfX18oFAps3bo1X5nFixfD398fDg4OaNu2LU6dOmXSMRQKBbp27YrWrVtjzZo1Fmp52aNWLUoelZys38MyJ5KHWFkQXG1tyb9QoqQKrlKEa0QEZWQG2E7A0uS0FchLWRdcAaCJdxMA5vu4Pkp9hPOx5wEAg5t1Q3g4ZdwdP56EvKltpwIAjkbQnNjB9QbD3sZeT20y3s5Pk2YZaSlgTNKsPy//CY3HZSDNDW82n2ZUvabg7U3nrBBAfDwlW8uZkbgk0qQJMOqpq8KHHxq/H9sJMMVN//60rFaNps6XRZo2JXE5IUG2dDKEqYIr+7cyDMMwDMOYj01xNyAvKSkpaNq0KSZOnIihQ4fm275hwwZMmzYNS5YsQdu2bfHdd9+hT58+uHr1KryehqI1a9YMWVlZ+fbds2cPfH19ceTIEVSuXBnR0dHo2bMnGjdujCZNmuhsT0ZGBjIyMrL/TnoaHqVWq6GW5puXYerWtcGlSwqEhmbB19dwmOupUzYAFGjWLAtqtREhsSWcgAAltm+nTFlVq6pREv/dvr4KADa4elWLU6cUABTo1q1ktrWkIp3H+s7nKlVUCAlR4vZtDdRqOYTowQPg1i36zfv5ld3vvIEHPWlfiLlg1jVv3819VI9nA1Swq4AKVaTvm7b39O+J2u61cf3xdQDA0LpDjTqOpwNFn8YkxxhVnjL42iIpSUCtzn9/yNJm4ZNgim7F8enwmeZslWt8ixYq3L9P6k/HjlooFJoS/9v56CPgzz9tsHOnAsHBWejUqeDre0QEXZt8fLRQqzXWb2QOCjqnmWeDESOAfftU6N9fWyb6JPpo21aF/fuVOHRIg3r19Ie5xscDly7ZAgDatKF7Vr16dJ5evKj7PL10SQVAiTp1ct//iho+pxmm7MHnNcOULcrCOW2Ntpc4wbVv377o27ev3u0LFy7Eq6++igkTJgAAlixZgu3bt+O3337DzJkzAQChoaEGj1H5qcFopUqVEBgYiLNnz+oVXOfPn485c+bkez84OBhOTk7GfKRSTYUKLQFUwebNVwHc0FsuOdkGN25QWuD4+L3YsSOzaBpoRWxsygGgOYnXr+9BVFR+kaa4iYysCKADjh5VQAgFPD1TcfPmXty6VdwtK33slTwZ8qDVNgRQCwcO3IK/P82vjI11QlBQe2Rk2KJixVSEhf2Hq1fL5gN9QkoCAODM/TPYsWOHyfuvur8KAOAPf737BzgF4Prj63BWOSMzPBM7rhV8nNgMimyNTorG9u3bC/RaffzYHsBzSEoCtm/fgbzFgx8H4/rja0CqO3DiHRw5shMqleX/p+XK1QFA2WcqVbqCHTv0X1dLEj16NMHu3dUxeXIC5s07ku/7y8uBAzUBNIIQkdix42yRtDEv+s5p5tlh7FhamnHpKjVUrFgXQD1s3BiFKlX0n2shId4A2sHXNxkhITQQ9uiRC4AeuHhRi23bduSLBD5zpgcAFyQmnsCOHQ+t9RGMhs9phil78HnNMGWL0nxOp6amWrzOEie4GiIzMxNnzpzBrFmzst9TKpXo2bMnjh8/blQdKSkp0Gq1KFeuHJKTk7F//3688MILesvPmjUL06bJU0sjIyPRoEEDBAQEwMPDw/wPU0q4cEGJw4eBrKz6CAyso7fcvn309F29usDIkT2LqnlWRQggMlIDFxdg+PCSOU+/Rg3g008BIej7HzDAAf36BRZzq0oXarUae/fuRa9evWBra5tv+40bSvzzD2BjUxOBgf4IDQVef90GMTEKVKsmsH27LerU0T9IVNrpmtkVH3z9AZ5kPUGrrq3g5exl0v6zf5kNABjbZSwC6+n+bfbI6gGnA07oULUDBtUdZFS9aeo0TLoyCVkiCx26d0AFxwoGyycnAxMnAlqtAt26BcLZWd6m1qgxbenT6/zR9+GocsGAAdb5n6pUCkhONpMm1UXr1vqvqyWJpk2B+vUFrlzxgFLZD337Ghajg4NJuWnZ0heBgT5F0cRsCjqnGaYsYWenwIYNwN27VQyea0eP0jnZu7cTAgPpWpyVBUybJpCeboOGDQNzJQhNSwMePKDHhPHj28Db23qfoSD4nGaYsgef1wxTtigL53SiIe85MylVguvDhw+h0WjgnafX5+3tjXAplWoBxMbGYsiQIQAAjUaDV199Fa0NmI7a29vD3t4eixcvxuLFi7PDjG1tbUvtD8kUmjalZViYEra2+k3QJL27fXtFmfpevvxSWlMVZzP0kvPhCAD69DH8f2L0o++clr7j+/eVOHpUiUGDyAe0SRNg504FfH3Lzu9dF262bqhRoQZuxt9E+ONwVHarbPS+D1IeIOwhRQX3qNlD77XB1tYW3/b91qR22draorx9eSRmJOJx5mN4lTcsBLu5kWesEEB6ui3c3ORtv1/8Hbee3IK7vRcen5qC8u7Wu4517EhtcXUF2rSxgU0puQv7+wNvvQV89RXwySc26N/fsC9m7FNr3cqVVbC1LZ7r57Nyn2aebTp1onPxzh0FHjywRWU9l2ipn9ali9xPsLUF6tcHzp8Hrl61RZ0c4z9hYZSIy90dqFzZtsCo9qKAz2mGKXvwec0wZYvSfE5bo93PnDJTo0YNnD9/HufPn8elS5fwzjvvGLXf5MmTERYWhuDgYCu3sGQhJVS4coUiIfRx+DAtu3SxfpsYGRcXZAtHCgXQo0exNqdMUq0aLc+fB/r0IbG1Sxfg4MGym/k6L429GwMwPXHWwTsHAVDiLU8nT4u3y8eForlikmMKLKtQ6E6clanJxGeHPgMAjPabCaidUb68xZuaTYUKQGgoiR+lRWyV+OADoHx5OhcOHjRclpNmMUzRUK6cPDh+9KjuMunpgJRfVkqYJaEvcVYYjZWhfn2UCLGVYRiGYRimtFGqBFdPT0+oVCrExubOSh0bGwsfn6Kdsvis4O9PmbQzMuSM7HnJzMwZOVFkTWOeUrUqLVu2BDwtr2k98/j50TItjX7rQ4cCu3cjV4RkWaeJF3lcX4w1TXANvkMDVAH+ARZvEyALrrHJsQWUJCTB9WnuQwDAr2d/xd2Eu/At54vu5V8HAKsKrgCJ+KVRiPTwALqTrXWBGdFZcGWYoqNjR1oeOaJ7+5kzdP/y8gJq1cq9rSDBtUEDy7WTYRiGYRjmWaJUCa52dnZo2bIl9u3bl/2eVqvFvn370L59e6see/Hixdnerc8SSiXQsCGt5+2MS4SEUPSEpydQr17RtY0hJMG1V6/ibUdZxdOTohIBYNIk4M8/pYz3zw7mRrhaW3D1diZ7GWMiXAFZSJUE1/SsdHx++HMAwOxOs5GR7JirHJMfSay5UUCuLxZcGabokKJWDxwg25S8SEJsp075o1X1Ca5XrtCSBVeGYRiGYRjzKHGCa3JyMkJDQxEaGgoAuH37NkJDQ3Hv3j0AwLRp07Bs2TKsWrUKV65cwRtvvIGUlBRMmDDBqu16Vi0FAP2dcYlDh2jZpQtPOysOpk8HBg0if0XG8igUwNatwPr1wM8/A6qSaedrVRp7keB6Oe4ytEJr1D7RSdEIfxgOBRToUs06oe/ZEa4ppkW4SpYCv5z5BZFJkahavipeafFK9vssuOrHGME1JUUWtVlwZRjr07Ur+bFevAj873/5t+cUXPMi9fHCw4GnaQoAcIQrwzAMwzBMYSlxDnIhISG5okinTaPM0ePGjcPKlSsxYsQIxMXF4ZNPPkFMTAyaNWuGXbt25UukxVgOUwRXpujp3l2e5stYh2f9t13LvRYcbByQqk7Fzcc3UdujdoH7HLhzAADQzKcZKjhWsEq7TI1wzWkpIITAolOLAACzO8+GvY09C65GYIzgKkW3OjnJ3znDMNbDxwdYvBh47TUgKAioWRN46SXaptXK3q66BFc/P/KDT06m87p+fRJer1+n7fXrF8lHYBiGYRiGKXOUuAjXbt26QQiR77Vy5crsMlOmTMHdu3eRkZGBkydPom3btlZv17NqKQAYFlw1Gjly4lkXpRimrKJSqtDEm3xcz0SfMWofSXC1lp0AYFrSLCC3pcDx+8dx/fF1ONs646UmpEyw4FowkuB66xZd/3WR006AZz0wTNHw6qvA++/T+ssvy4PhV64A8fE0ANKsWf79dFlH3bhBiVJdXIAqVazedIZhGIZhmDJJiRNcSypsKUDRDunpubedP0/iRfnyQJMmRd82hmGKhta+rQEApyNPG1U+27+1uvUEV28XinA1x1JgxbkVAIDnGzwPFzuX7PcBFlwNUaUKTV1Wq4H793WXYf9Whike5s8Hhg2jBFlDhgDXrsmD4u3a0bmri7wD6zntBHjQhGEYhmEYxjxYcGUKpFIlShqk0QBXr+beJkVQdOr0bHpbMsyzQivfVgCAkOiQAstGJ0Xj+uPrUCqU6OzX2WptMjXCVRJcHyelYsPlDQCACc1k/28WXAtGpQJq1KB1fbYCLLgyTPGgVAK//w60aQM8fgz06wf88w9t02UnIKFPcGU7AYZhGIZhGPNhwdVInmVLAYVCv62AJLh2tp6mwjBMCUCKcD0TdQYarZ655E85FnEMANDEuwlcHVyt1ibJw/VBygOjknlJQuq59C1IykxCdbfq6FxNvnix4GocBfm4suDKMMWHkxOJrNWq0Tm6Ywe9b47gygmzGIZhGIZhzIcFVyN5li0FAN2CqxDA4cO0zv6tDFO2qedZD862zkhRpyD8YbjBspLg2qFKB6u2ycvZCwCQpc3C47THBZaXIlwvqshOYFzTcVAq5NsgC67GwYIrw5RsvL1JaHV9Ot6lVJKlgD6kPt6NG0BaGvm+Aiy4MgzDMAzDFAYWXBmj0CW4hocDDx8CDg5Aq1bF0y6GYYoGlVKFFpVaAABCogzbChy7/1RwrWpdwdXexh7uju4AgNjkgn1cy5cH4HoX0Q77AQBjm47NtZ0FV+NgwZVhSj4NGgCbNlEfrXdvecBJF97egIcHoNUCly9T/06qg2EYhmEYhjEPFlwZo5Ay2F6+LL8n2Qm0bw/Y2RV9mxiGKVqyE2dF6U+claZOw5moMwCsL7gCsq2AMT6u5coBaLoaUAgE+AegeoXqubaz4GocLLgyTOmgRw8gKgr491/D5XJaR/37L5CRQUJttWrWbyPDMAzDMExZhQVXI3mWPVwBWXC9fRtITqZ1SXBlOwGGeTZoXblgwfVM9BmotWr4uPjA383f6m2SEmfFphQc4eriIoBmKwEA45uNz7ddElwNRYIxsuB68yZFxOWFBVeGKTlUqADY2BRcThJc//yTlvXqcTJUhmEYhmGYwsCCq5E86x6unp6AD+kaCAsj/9aDB+lvFlwZ5tmglS95h5yPOY9MTabOMtn+rVU7QKFQWL1N3i7GR7jewxHA/SaUahcMqz8s3/akJFpyhKthqlUjISYtTRZXJTIzgUePaJ0FV4YpPUiCK9sJMAzDMAzDWAYWXBmjyenjeucOEBlJUROGEjEwDFN2qFmhJtwc3JChycClB5d0limqhFkSPs5PI1yN8HDdH78SAOB4ezic7ZxzbdNqWXA1FltbwN+f1vPaCsTEyGU8PIq0WQzDFAKpjyfBgivDMAzDMEzhYMGVMZqcgqtkJ9C6NeDkVHxtYhim6FAoFNlRrqcj89sKCCGyBdeOfh2LpE3ZEa4phiNcUzJTsC+G5soqzk/Ivz2FIvcBFlyNQZ+PqxTx6uNDvpAMw5QOJOsoifr1i6cdDMMwDMMwZQUWXBmj0SW4sp0AwzxbSImzQqJC8m27GX8TcalxsFfZo7lP8yJpj+ThWpClwKYrm5CalQw8roHU8E7Z4qqE5N9qY0PJYhjDFCS4sp0Aw5QuKlQAKleW/+YIV4ZhGIZhmMLBgquRPOtJswAWXBmGkQVXXYmzpOjWVr6tYG9jXyTt8XamCNeCLAVWhq6kldDx0GoUSE/PvV0SXMuX58hMY8iZOCsnLLgyTOlF6ufZ2gI1axZvWxiGYRiGYUo7LLgaybOeNAuQox2ioymqSaEAOhSNTSPDMCUEyVLg0oNLSFWn5tqWM2FWUWFMhOudJ3cQfCcYCiiA8+MAyH6tEjkFV6ZgJDGGI1wZpuwgCa61a5PoyjAMwzAMw5gPC66M0ZQrJydKAYCmTQE3t+JqDcMwxUGV8lXg7ewNjdDgfMz5XNuKU3CNS42DRqvRWeaPC38AALpX7w4XjR8AWWCVYMHVNHJaCuS0Z2DBlWFKL9Igevv2xdsOhmEYhmGYsgALroxJ5Mxiy3YCDPPsoVAo0LpyfluBhPQEXHpwCQDQvkrRPa1XdK4IBRTQCi0epj7UWebAnQMAgOcbPI9y5eg9jnAtHNWr0yyHpCQgLk5+nwVXhim9DB4MHD4MfPttcbeEYRiGYRim9MOCK2MSLLgyDNOqEtkK5BRcT0aehIBAzQo14e3iXWRtsVHawNPJEwAQm5Lfx1UrtNntbFelXbagyoJr4XBwAKpWpfWctgIsuDJM6UWpBDp1QvbAFMMwDMMwDGM+LLgyJpFTcO3cufjawTBM8SFFuIZEhWS/d/TeUQBFaycgIQm8unxcrz68isSMRDjaOKKRV6NsIYEtBQpPTlsBCRZcGYZhGIZhGIZhWHA1msWLF6NBgwYICAgo7qYUKx06APb2QMeOgJdXcbeGYZjiQEqcJYmZAHDsftH7t0pIPq6xyfkjXE9GngQAtPRtCRulDVsKWJC8gqtGA8Q+/Rew4MowDMMwDMMwzLMMC65GMnnyZISFhSE4OLi4m1KsVK8OXLkC/PtvcbeEYZjiwsvZC36ufhAQOBN1BhqtBifunwBQTBGuzvojXE/eJ8G1beW2AGRBlSNcC09ewTUuDtBqyduVB+QYhmEYhmEYhnmWsSnuBjClj+rVi7sFDMMUN619W+Newj2ERIXA3dEdyZnJKGdXDg0rNizytmRHuOrwcD0VdQqALLjqi3CV/mbB1XjyCq6SnYCXF2DDvQuGYRiGYRiGYZ5hOMKVYRiGMZnWvuTjejrqNI5FkJ1AuyrtoFKqirwt+iJc09RpuBB7AQDQpnIbAPoFV45wNZ28gmtUFC3ZToBhGIZhGIZhmGcdjkFhGIZhTEbycT0ddRr2NvYAgI5VOxZLW/RFuJ6NPossbRa8nb3h5+oHoGBLAc7ObTw1atAyPh54/JgTZjEMwzAMwzAMw0iw4MowDMOYTEvflgCAO0/uICUzBUDx+LcCsuCaN8JVSpjVtkpbKBQKABzhakmcnQFfX4psvXlTFlx9fYu3XQzDMAzDMAzDMMUNWwowDMMwJuPm4IY6HnUAAHGpcVBAgbZV2hZLW7xddFsKnIrM7d8KyIIqC66WIaetAEe4MgzDMAzDMAzDECy4MgzDMGYh2QoAQGPvxihvXzxqpRTh+ij1EdQadfb7UoSr5N8KyBGu+iwFWHA1jZo1acmCK8MwDMMwDMMwjAwLrgzDMIxZSImzAKBDleKxEwAAD0cPKBVKCAjEpcYBAB6kPMCdJ3eggCJXO9lSwLJwhCvDMAzDMAzDMEx+WHA1ksWLF6NBgwYICAgo7qYwDMOUCHIJrsXk3woAKqUKXs5eAIDYZEqcJdkJ1POsB1cH1+yyupJmCcGCq7mw4MowDMMwDMMwDJMfFlyNZPLkyQgLC0NwcHBxN4VhGKZE0MynGexUdgCAjn4di7Ut3s65fVxP3s9vJwDojnDNyADUT50IWHA1DUlwvX4diHlqocuCK8MwDMMwDMMwzzo2xd0AhmEYpnTibOeMP5//E0mZSahRoUaxtsXHxQfnY88jNoUiXCX/1pwJswDdgmvOaFcXF6s2s8whebjGxcnv+fgUT1sYhmEYhmEYhmFKCiy4MgzDMGYzqN6g4m4CAMDbRY5w1QotTkedBgC0rZJbcNVlKSCtlysHKHneh0m4ugIVK8qCq7s7YG9fvG1iGIZhGIZhGIYpbvjRkmEYhin1+DhTWGVsciyuP7qOJ+lP4GDjgMZejXOVkyJc1WqyEgDYv7WwSLYCANsJMAzDMAzDMAzDACy4MgzDMGUAHxcSXGNSYrLtBFpUagFblW2ucpLgCsi2Aiy4Fg4WXBmGYRiGYRiGYXLDgivDMAxT6pEsBWKTY3Eq8hSA/P6tAKBSAU5OtC4JrZLwyoKrebDgyjAMwzAMwzAMkxv2cGUYhmFKPdkRrskxSMokBVWX4ApQlGtqKke4WgoWXBmGYRiGYRiGYXLDgivDMAxT6vF2pgjXiMQIZGSROWubym10li1fHoiNlYVWFlwLBwuuDMMwDMMwDMMwuXkmLQVu376NgIAANGjQAI0bN0ZKSkpxN4lhGIYpBFKEa3JmMtRaNSo6VYS/m7/OspKPa94I15z+rozxsODKMAzDMAzDMAyTm2cywnX8+PGYO3cuOnfujMePH8Pe3r64m8QwDMMUggqOFWCjtEGWNgsA0LZKWygUCp1l9QmuHOFqHu7uQIUKQHw8C64MwzAMwzAMwzDAMxjhevnyZdja2qJz584AAHd3d9jYPJO6M8MwTJlBqVBm2woA+v1bAVlYZUsBy/Hhh8DAgUBb/V87wzAMwzAMwzDMM0OJE1wPHTqEAQMGwNfXFwqFAlu3bs1XZvHixfD394eDgwPatm2LU6dOGV3/9evX4eLiggEDBqBFixaYN2+eBVvPMAzDFBfeLrLgqs+/FeAIV2swfTrw998ATxhhGIZhGIZhGIYpgZYCKSkpaNq0KSZOnIihQ4fm275hwwZMmzYNS5YsQdu2bfHdd9+hT58+uHr1Kry8vAAAzZo1Q1ZWVr599+zZg6ysLBw+fBihoaHw8vLCc889h9atW6NXr15W/2wMwzCM9ZB8XAEWXBmGYRiGYRiGYZjio8QJrn379kXfvn31bl+4cCFeffVVTJgwAQCwZMkSbN++Hb/99htmzpwJAAgNDdW7f+XKldGqVStUrVoVABAYGIjQ0FC9gmtGRgYyMjKy/056+oSuVquhVqtN+mwMw5Q8pPOYz+fSj5cTDbrVca8DZ5Wz3v+ps7MSgApPnmigVmuRkKACoISTUxbUalF0DWasAp/TDFO24HOaYcoefF4zTNmiLJzT1mh7iRNcDZGZmYkzZ85g1qxZ2e8plUr07NkTx48fN6qO1q1b48GDB4iPj4erqysOHTqESZMm6S0/f/58zJkzJ9/7wcHBcHJyMv1DMAxTItm7d29xN4EpJKkxqQCAyqIyduzYobdcdHQdAPVx+XIEduw4j4iILgAq4Nq1EOzYEVs0jWWsDp/TDFO24HOaYcoefF4zTNmiNJ/TqampFq+zVAmuDx8+hEajgbe3d673vb29ER4eblQdNjY2mDdvHrp06QIhBHr37o3+/fvrLT9r1ixMmzYt++/IyEg0aNAAAQEB8PDwMO+DMAxTYlCr1di7dy969eoFW1vb4m4OUwiaJDaB53FPvNnqTdTxqKO33M2bSqxbB7i5+SEwsDJmzKBbYffurdCpE0e4lnb4nGaYsgWf0wxT9uDzmmHKFmXhnE6UfOYsSKkSXC1FQbYFObG3t4e9vT0WL16MxYsXZ4cZ29raltofEsMw+eFzuvRT3aM6FvdfXGA5NzdapqQoYWurzPZydXe3Af8Eyg58TjNM2YLPaYYpe/B5zTBli9J8Tluj3UqL12hFPD09oVKpEBube8pnbGwsfHx89OxlGSZPnoywsDAEBwdb9TgMwzCMdcmbNEtactIshmEYhmEYhmEYxhKUKsHVzs4OLVu2xL59+7Lf02q12LdvH9q3b1+MLWMYhmFKC5KwmpgIaDRASkru9xmGYRiGYRiGYRimMJQ4S4Hk5GTcuHEj++/bt28jNDQU7u7u8PPzw7Rp0zBu3Di0atUKbdq0wXfffYeUlBRMmDDBqu3KaynAMAzDlE5yRrhK0a0532cYhmEYhmEYhmGYwlDiBNeQkBAEBARk/y0lrBo3bhxWrlyJESNGIC4uDp988gliYmLQrFkz7Nq1K18iLUszefJkTJ48Gffv30fVqlWteiyGYRjGeuQUXCVvdAcHwM6u+NrEMAzDMAzDMAzDlB1KnODarVs3CGE4S/SUKVMwZcqUImoRwzAMU5bIaSkgCa5sJ8AwDMMwDMMwDMNYilLl4VqcLF68GA0aNMgVfcswDMOUPqQI14wM4OHD3O8xDMMwDMMwDMMwTGFhwdVIJk+ejLCwMAQHBxd3UxiGYZhCkFNcjYykJUe4MgzDMAzDMAzDMJaCBVeGYRjmmcLWljxbARZcGYZhGIZhGIZhGMvDgquRsKUAwzBM2UGKcmXBlWEYhmEYhmEYhrE0LLgaCVsKMAzDlB0kgfX+/dx/MwzDMAzDMAzDMExhYcGVYRiGeebgCFeGYRiGYRiGYRjGWrDgyjAMwzxzsODKMAzDMAzDMAzDWAsWXI2EPVwZhmHKDpLAGh2d+2+GYRiGYRiGYRiGKSwsuBoJe7gyDMOUHaQIV42Gliy4MgzDMAzDMAzDMJaCBVeGYRjmmSOvwMqCK8MwDMMwDMMwDGMpWHBlGIZhnjmkCFcJFlwZhmEYhmEYhmEYS8GCK8MwDPPMwYIrwzAMwzAMwzAMYy1YcDUSTprFMAxTdmBLAYZhGIZhGIZhGMZasOBqJJw0i2EYpuzAEa4MwzAMwzAMwzCMtWDBlWEYhnnmYMGVYRiGYRiGYRiGsRYsuDIMwzDPHHkF1rwCLMMwDMMwDMMwDMOYCwuuDMMwzDNHToFVqQScnIqvLQzDMAzDMAzD/L+9ew+Lqlr/AP4dEAaUixDBACIXQUBBUFECz4lQDNRIzJ7QOoqKlIJ44ZCm/VBLEbPkMZLkqCTZEVBL7aLZUQRNQ80LR02kIMlSwLygAsp1/f7wYR8nBgUcHIXv53nmib322mu/e+xlz7ysWUPUsbDgSkREnc69BVcjI0Am01wsRERERERE1LGw4NpCycnJ6NOnD/z9/TUdChERPaR7lxTg+q1ERERERESkTiy4tlBUVBTOnj2L7OxsTYdCREQP6a8zXImIiIiIiIjUhQVXIiLqdFhwJSIiIiIiovbCgisREXU6cjmgq3v3ZxZciYiIiIiISJ1YcCUiok6pcZYrC65ERERERESkTiy4EhFRp9RYaGXBlYiIiIiIiNSJBVciIuqUOMOViIiIiIiI2gMLrkRE1Cmx4EpERERERETtgQVXIiLqlLikABEREREREbUHFlxbKDk5GX369IG/v7+mQyEiIjXo3//ufz08NBsHERERERERdSwsuLZQVFQUzp49i+zsbE2HQkREarB0KVBWBgwdqulIiIiIiIiIqCNhwZWIiDolmQwwN9d0FERERERERNTRsOBKREREREREREREpCYsuBIRERERERERERGpCQuuRERERERERERERGrCgisRERERERERERGRmrDgSkRERERERERERKQmLLgSERERERERERERqQkLrkRERERERERERERq8sQXXJOTk2FnZwc9PT14e3vj6NGj9+2/detWuLi4QE9PD+7u7ti1a9cjipSIiIiIiIiIiIg6uie64Lp582bExMRg0aJFOHHiBDw8PBAYGIjLly+r7P/DDz9g/PjxCA8Px8mTJxESEoKQkBCcOXPmEUdOREREREREREREHdETXXBNTExEREQEJk+ejD59+iAlJQVdu3bFJ598orL/hx9+iKCgILz55ptwdXXFkiVLMGDAAKxevfoRR05EREREREREREQdURdNB9BWNTU1OH78OObPny+1aWlpISAgALm5uSqPyc3NRUxMjFJbYGAgduzY0ex5qqurUV1dLW3fuHEDAHDr1i3o6Og8xBUQ0eOgtrYWVVVVuHnzJnOaqANgThN1LMxpoo6HeU3UsXSEnL558yYAoKGhQW1jPrEF1ytXrqC+vh4WFhZK7RYWFjh37pzKY0pLS1X2Ly0tbfY8CQkJeOedd5q029vbtyFqIiIiIiIiIiIietz8/vvv6Nmzp1rGemILro/K/PnzlWbF/vbbb+jXrx8OHz4Ma2trDUZGROpw69Yt9OnTB2fPnoWhoeFD9yMizVJ3rjL3iTRLUznI3CdqP49zfj3OsRE9rjpC3ty4cQNubm5wdXVV25hPbMHVzMwM2traKCsrU2ovKyuDQqFQeYxCoWhVfwCQy+WQy+XStomJCQDA2toaPXr0aGv4RPSYaPzogLW1NYyMjB66HxFplrpzlblPpFmaykHmPlH7eZzz63GOjehx1RHypjHuLl3UVyZ9Yr80S1dXFwMHDkRWVpbU1tDQgKysLPj4+Kg8xsfHR6k/AOzZs6fZ/kRERERERERERESt8cTOcAWAmJgYhIWFwcvLC4MHD8aqVatQWVmJyZMnAwAmTpwIa2trJCQkAABmzZoFPz8/rFy5EqNGjUJmZiaOHTuGtWvXavIyiIiIiIiIiIiIqIN4oguuoaGh+PPPP7Fw4UKUlpbC09MTu3fvlr4Y68KFC9DS+t8kXl9fX6Snp+P//u//sGDBAjg5OWHHjh1wc3Nr8TmNjIzg5+f3xE6TJiJlcrkcixYtUlo65GH6EZFmqTtXmftEmqWpHGTuE7Wfxzm/HufYiB5XHSFv2uMaZEIIobbRiIiIiIiIiIiIiDqxJ3YNVyIiIiIiIiIiIqLHDQuuRERERERERERERGrCgisRERERERERERGRmrDgSkRERERERERERKQmLLiqkJycDDs7O+jp6cHb2xtHjx69b/vWrVvh4uICPT09uLu7Y9euXZoMn4haYPHixZDJZEoPhUKhMsdVWbduHf7+97/DxMQEJiYmCAgIuG9/IlKvAwcOIDg4GFZWVpDJZNixY4fSfiEEFi5cCEtLS+jr6yMgIAC//PKLyrGau78/SGZmJmQyGUJCQh7yaojoQTk9adKkJvftoKCghzpna3O/vLwcUVFRsLS0hFwuR+/evfm6n6gZCQkJGDRoEAwNDWFubo6QkBAUFBQo9blz5w6ioqLw1FNPwcDAAGPHjkVZWVm7x9ba3F+1ahWcnZ2hr68PGxsbzJkzB3fu3Gn3OIkeJy3J6eeee67JvXratGkaivh/HvQaQ5WcnBwMGDAAcrkcjo6OSEtLa/V5WXD9i82bNyMmJgaLFi3CiRMn4OHhgcDAQKxdu1Zl+86dOzF+/HiEh4fj5MmTCAkJQUhICM6cOaPpSyGiB+jbty9KSkpQUlKClJQUXLt2rUmOX758WeWxOTk5GD9+PLKzs5GbmwsbGxs8//zzuHjx4iO+CqLOqbKyEh4eHkhOTla5f8WKFUhKSkJKSgqOHDmCbt26ITAwsMkbpObu+83lfqPi4mLExsbi73//u9quiagze1BOA0BQUJB03y4pKUFGRkabz9fa3K+pqcHw4cNRXFyMzz//HAUFBVi3bh2sra3bHANRR7Z//35ERUXh8OHD2LNnD2pra/H888+jsrJS6jNnzhx8/fXX2Lp1K/bv349Lly7hpZdeate4Wpv76enpeOutt7Bo0SLk5+cjNTUVmzdvxoIFC9o1TqLHTUtyGgAiIiKU7tUrVqzQUMT/05LXGPc6f/48Ro0aBX9/f+Tl5WH27NmYOnUqvvvuu9adWJCSwYMHi6ioKGm7vr5eWFlZiR49eqhsd3d3F6NGjVIaw9vbW7zxxhuPLGYiar1FixYJDw8Pabu53E9ISGjReHV1dcLQ0FB8+umn6g6ViB4AgNi+fbu03dDQIBQKhXj//feltvLyciGXy0VGRobSsW3J/bq6OuHr6yvWr18vwsLCxOjRo9V2LUTUNKeFEGrPtdbm/po1a4SDg4OoqalRWwxEncnly5cFALF//34hxN37so6Ojti6davUJz8/XwAQubm57RZHa3M/KipKDB06VKktJiZGDBkypN1iJHoS/DWnhRDCz89PzJo1S3NBtYCq1xh/NXfuXNG3b1+lttDQUBEYGNiqc3GG6z1qampw/PhxBAQESG1aWlrw9/fHxYsXm7Q3fjzx3nYACAwMRG5u7iOLm4ja5pdffoGVlRXs7e3x448/wsPDQ9rXmOMtzeWqqirU1tbC1NS0vcIlohY6f/48SktLle7PxsbG8Pb2Vsrp5u77D8r9d999F+bm5ggPD2+fCyAilXJycmBubg5nZ2dMnz4dV69ebdM4bcn9r776Cj4+PoiKioKFhQXc3NywbNky1NfXtykGos7mxo0bACC9Vj5+/Dhqa2uV8tDFxQU9e/Zst/fSbcl9X19fHD9+XFp24Ndff8WuXbswcuTIdomR6Enx15xutGnTJpiZmcHNzQ3z589HVVWVJsJ7KLm5uWqp83VRZ1BPuitXrqC+vh4WFhZK7UZGRhBCNGm3sLBAdXW1yvbS0tJ2j5eI2s7b2xtpaWlwdnbGmTNn8NprryEuLg7jxo2DoaEhgLu5fO7cuRaNN2/ePFhZWTX5xUxEj17jPfhB9+fm7vv3y/2DBw8iNTUVeXl56g2aiO4rKCgIL730Euzt7VFUVIQFCxZgxIgRyM3Nhba2dqvGakvu//rrr9i3bx9ee+017Nq1C4WFhYiMjERtbS0WLVrU5usi6gwaGhowe/ZsDBkyBG5ubgDu3qt1dXXRvXt3pb7t+V66Lbn/6quv4sqVK/jb3/4GIQTq6uowbdo0LilAnZqqnAbu5outrS2srKxw6tQpzJs3DwUFBdi2bZsGo2290tJSlb8nbt68idu3b0NfX79F47DgSkSd0ogRI6SfzczMAAAVFRXYsmVLq2etLV++HJmZmcjJyYGenp5a4ySix8etW7cwYcIErFu3Tvq9QUSPxrhx46Sf3d3d0a9fP/Tq1Qs5OTkYNmxYu5+/oaEB5ubmWLt2LbS1tTFw4EBcvHgR77//PguuRA8QFRWFM2fO4ODBg5oOpdVycnKwbNkyfPzxx/D29kZhYSFmzZqFJUuWIC4uTtPhEWlEczn9+uuvSz+7u7vD0tISw4YNQ1FREXr16vWow9Q4FlzvYWZmBm1t7SbfjHjz5k3IZLIm7WVlZZDL5SrbFQpFu8dLROrRmPuWlpYoLCyU2luSyx988AGWL1+OvXv3ol+/fu0dKhG1QGPelpWVwdLSUmovKyuDp6entN3cfb+53C8qKkJxcTGCg4OltoaGBgBAly5dUFBQ0ClfTBJpgoODA8zMzFBYWNjqgmtrcx8ALC0toaOjozSb1tXVFaWlpaipqYGurm7rL4KoE5gxYwa++eYbHDhwAD169JDaFQoFampqUF5erjTLtT3fS7cl9+Pi4jBhwgRMnToVwN0iUmVlJV5//XW8/fbb0NLiKo3UuTSX06p4e3sDAAoLC5+o18gKhULl7wkjI6MWz24FAP52uIeuri4GDhyIrKwsqa2hoQE5OTmwtrZu0p6VlQUnJyeldgDYs2cPfHx8HlncRPRwdHV14enpid9//10qzjTm+P1yecWKFViyZAl2794NLy+vRxUuET2Avb09FAqF0v355s2bOHLkiFJON3ffby73XVxccPr0aeTl5UmPF198UfoGUxsbm/a9MCKS/PHHH7h69arSH1VaqrW5DwBDhgxBYWGh9EcWAPj5559haWnJYiuRCkIIzJgxA9u3b8e+fftgb2+vtH/gwIHQ0dFRysOCggJcuHCh3d5LtyX3q6qqmhRVG//wcvf7d4g6hwfltCqNS3C15V6tST4+Pmqp83GG61/ExMQgLCwMXl5eGDx4MFatWoXKykosX74cs2bNwokTJ9CnTx9oaWmhsrISKSkpGDNmDFauXIlRo0YhMzMTx44dw9q1azV9KUR0H7GxsQgODoatrS0uXbqEmpoaVFdXQyaTIT8/X8r9yZMnAwAmTpwIa2trJCQkAADee+89LFy4EOnp6bCzs5PWmjIwMICBgYHGrouos6ioqFCakX7+/Hnk5eXB1NQUPXv2xOzZs7F06VI4OTnB3t4ecXFxsLKyQkhIiNI4zd33VeW+np6e0jpVAKRZOX9tJ6LWuV9Om5qa4p133sHYsWOhUChQVFSEuXPnwtHREYGBgW06X2tyHwCmT5+O1atXY9asWYiOjsYvv/yCZcuWYebMmQ9/8UQdUFRUFNLT0/Hll1/C0NBQeq1sbGwMfX19GBsbIzw8HDExMTA1NYWRkRGio6Ph4+ODZ555pt3iam3uBwcHIzExEf3795eWFIiLi0NwcHCr148mepI9KKeLioqQnp6OkSNH4qmnnsKpU6cwZ84cPPvssxr/JOiD3jfMnz8fFy9exMaNGwEA06ZNw+rVqzF37lxMmTIF+/btw5YtW7Bz587WnVhQEx999JHo2bOn0NXVFYMHDxaHDx+W2uVyudDS0lJq37Jli+jdu7fQ1dUVffv2FTt37tRk+ETUAqGhocLS0lLo6uoKa2trERoaKhYuXKgy94UQws/PT4SFhUnbtra2AkCTx6JFix79xRB1QtnZ2SpzsDFPGxoaRFxcnLCwsBByuVwMGzZMFBQUqByrufu+EE1z/6/CwsLE6NGj1XhlRJ3T/XK6qqpKPP/88+Lpp58WOjo6wtbWVkRERIjS0tKHOmdrc/+HH34Q3t7eQi6XCwcHBxEfHy/q6uoeKgaijkpVPgMQGzZskPrcvn1bREZGChMTE9G1a1cxZswYUVJS0u6xtSb3a2trxeLFi0WvXr2Enp6esLGxEZGRkeL69evtHifR4+RBOX3hwgXx7LPPClNTUyGXy4Wjo6N48803xY0bNzQbuHjw+4awsDDh5+fX5BhPT0+hq6srHBwclH53tZRMCM6DJyIiIiIiIiIiIlIHruFKREREREREREREpCYsuBIRERERERERERGpCQuuRERERERERERERGrCgisRERERERERERGRmrDgSkRERERERERERKQmLLgSERERERERERERqQkLrkRERERERERERERqwoIrERERERERERERkZqw4EpERETUSclkMuzYseOhx0lLS0P37t0fehxSLScnBzKZDOXl5e0yflxcHF5//XW1x5SSkoLg4OCHjI6IiIjoycOCKxEREVEHMmnSJMhkMshkMujo6MDCwgLDhw/HJ598goaGBqW+JSUlGDFihIYi1bzS0lJER0fDwcEBcrkcNjY2CA4ORlZWlqZDU+Lr64uSkhIYGxsDUG+Bu7S0FB9++CHefvvtJvtyc3Ohra2NUaNGtWnsKVOm4MSJE/j+++8fNkwiIiKiJwoLrkREREQdTFBQEEpKSlBcXIxvv/0W/v7+mDVrFl544QXU1dVJ/RQKBeRyuQYjvb/a2tp2G7u4uBgDBw7Evn378P777+P06dPYvXs3/P39ERUV1W7nbQtdXV0oFArIZDK1j71+/Xr4+vrC1ta2yb7U1FRER0fjwIEDuHTpUqvH1tXVxauvvoqkpCR1hEpERET0xGDBlYiIiKiDkcvlUCgUsLa2xoABA7BgwQJ8+eWX+Pbbb5GWlib1u3dJgZqaGsyYMQOWlpbQ09ODra0tEhISpL7l5eV44403YGFhAT09Pbi5ueGbb75ROu93330HV1dXGBgYSEXfRj/++COGDx8OMzMzGBsbw8/PDydOnFA6XiaTYc2aNXjxxRfRrVs3xMfHAwCWLl0Kc3NzGBoaYurUqXjrrbfg6empdOz69evh6uoKPT09uLi44OOPP77vcxQZGQmZTIajR49i7Nix6N27N/r27YuYmBgcPnxY6peYmAh3d3d069YNNjY2iIyMREVFhbS/cbbpjh074OTkBD09PQQGBuL333+X+hQVFWH06NGwsLCAgYEBBg0ahL179yrFU11djXnz5sHGxgZyuRyOjo5ITU0FoPzx/ZycHEyePBk3btyQZjIvXrwY7777Ltzc3Jpcp6enJ+Li4pp9HjIzM1V+7L+iogKbN2/G9OnTMWrUKKX/b+51/PhxeHl5oWvXrvD19UVBQYHS/uDgYHz11Ve4fft2szEQERERdTQsuBIRERF1AkOHDoWHhwe2bdumcn9SUhK++uorbNmyBQUFBdi0aRPs7OwAAA0NDRgxYgQOHTqEf//73zh79iyWL18ObW1t6fiqqip88MEH+Oyzz3DgwAFcuHABsbGx0v5bt24hLCwMBw8exOHDh+Hk5ISRI0fi1q1bSnEsXrwYY8aMwenTpzFlyhRs2rQJ8fHxeO+993D8+HH07NkTa9asUTpm06ZNWLhwIeLj45Gfn49ly5YhLi4On376qcprvXbtGnbv3o2oqCh069atyf57P66vpaWFpKQk/PTTT/j000+xb98+zJ07V6l/VVUV4uPjsXHjRhw6dAjl5eUYN26ctL+iogIjR45EVlYWTp48iaCgIAQHB+PChQtSn4kTJyIjIwNJSUnIz8/Hv/71LxgYGDSJzdfXF6tWrYKRkRFKSkpQUlKC2NhYTJkyBfn5+fjxxx+lvidPnsSpU6cwefLkZp+Hs2fPwsvLq8m+LVu2wMXFBc7OzvjHP/6BTz75BEKIJv3efvttrFy5EseOHUOXLl0wZcoUpf1eXl6oq6vDkSNHVMZARERE1CEJIiIiIuowwsLCxOjRo1XuCw0NFa6urtI2ALF9+3YhhBDR0dFi6NChoqGhoclx3333ndDS0hIFBQUqx92wYYMAIAoLC6W25ORkYWFh0Wyc9fX1wtDQUHz99ddK8cyePVupn7e3t4iKilJqGzJkiPDw8JC2e/XqJdLT05X6LFmyRPj4+Kg895EjRwQAsW3btmbja87WrVvFU089JW03Xvvhw4eltvz8fAFAHDlypNlx+vbtKz766CMhhBAFBQUCgNizZ4/KvtnZ2QKAuH79unROY2PjJv1GjBghpk+fLm1HR0eL5557rtkYTp48KQCICxcuNNnn6+srVq1aJYQQora2VpiZmYns7OwmMe3du1dq27lzpwAgbt++rTSWiYmJSEtLazYOIiIioo6GM1yJiIiIOgkhRLPrgE6aNAl5eXlwdnbGzJkz8Z///Efal5eXhx49eqB3797Njt21a1f06tVL2ra0tMTly5el7bKyMkRERMDJyQnGxsYwMjJCRUWF0ixPAE1mWxYUFGDw4MFKbfduV1ZWoqioCOHh4TAwMJAeS5cuRVFRUbPPQ0vt3bsXw4YNg7W1NQwNDTFhwgRcvXoVVVVVUp8uXbpg0KBB0raLiwu6d++O/Px8AHdnuMbGxsLV1RXdu3eHgYEB8vPzpWvPy8uDtrY2/Pz8WhyXKhEREcjIyMCdO3dQU1OD9PT0JjNO79X4MX89PT2l9oKCAhw9ehTjx4+Xri80NFRa4uBe/fr1k362tLQEAKV/dwDQ19dXer6IiIiIOroumg6AiIiIiB6N/Px82Nvbq9w3YMAAnD9/Ht9++y327t2LV155BQEBAfj888+hr6//wLF1dHSUtmUymVJhMywsDFevXsWHH34IW1tbyOVy+Pj4oKamRuk4VR/xv5/G9VTXrVsHb29vpX33LnlwLycnJ8hkMpw7d+6+YxcXF+OFF17A9OnTER8fD1NTUxw8eBDh4eGoqalB165dWxRjbGws9uzZgw8++ACOjo7Q19fHyy+/LF17S57flggODoZcLsf27duhq6uL2tpavPzyy832NzMzAwBcv34dTz/9tNSempqKuro6WFlZSW1CCMjlcqxevRrGxsZS+73/7o3F/IaGBqXzXLt2TWl8IiIioo6OM1yJiIiIOoF9+/bh9OnTGDt2bLN9jIyMEBoainXr1mHz5s344osvcO3aNfTr1w9//PEHfv755zaf/9ChQ5g5cyZGjhyJvn37Qi6X48qVKw88ztnZWWldUgBK2xYWFrCyssKvv/4KR0dHpUdzxWVTU1MEBgYiOTkZlZWVTfaXl5cDuPuFUA0NDVi5ciWeeeYZ9O7dG5cuXWrSv66uDseOHZO2CwoKUF5eDldXV+naJ02ahDFjxsDd3R0KhQLFxcVSf3d3dzQ0NGD//v0PfD4AQFdXF/X19U3au3TpgrCwMGzYsAEbNmzAuHHj7lvM7dWrF4yMjHD27Fmla9m4cSNWrlyJvLw86fHf//4XVlZWyMjIaFGMjYqKinDnzh3079+/VccRERERPck4w5WIiIiog6murkZpaSnq6+tRVlaG3bt3IyEhAS+88AImTpyo8pjExERYWlqif//+0NLSwtatW6FQKNC9e3f4+fnh2WefxdixY5GYmAhHR0ecO3cOMpkMQUFBLYrJyckJn332Gby8vHDz5k28+eabLZrZGR0djYiICHh5ecHX1xebN2/GqVOn4ODgIPV55513MHPmTBgbGyMoKAjV1dU4duwYrl+/jpiYGJXjJicnY8iQIRg8eDDeffdd9OvXD3V1ddizZw/WrFmD/Px8ODo6ora2Fh999BGCg4Nx6NAhpKSkNBlLR0cH0dHRSEpKQpcuXTBjxgw888wz0tIHTk5O2LZtG4KDgyGTyRAXF6c0C9TOzg5hYWGYMmUKkpKS4OHhgd9++w2XL1/GK6+80uR8dnZ2qKioQFZWFjw8PNC1a1dptu3UqVOVCr33o6WlhYCAABw8eBAhISEAgG+++QbXr19HeHi40kxWABg7dixSU1Mxbdq0+457r++//x4ODg5Ky00QERERdXSc4UpERETUwezevRuWlpaws7NDUFAQsrOzkZSUhC+//LLZj9kbGhpixYoV8PLywqBBg1BcXIxdu3ZBS+vuy8UvvvgCgwYNwvjx49GnTx/MnTtX5SzL5qSmpuL69esYMGAAJkyYgJkzZ8Lc3PyBx7322muYP38+YmNjpWUPJk2apLTu6NSpU7F+/Xps2LAB7u7u8PPzQ1paWrMzXAHAwcEBJ06cgL+/P/75z3/Czc0Nw4cPR1ZWFtasWQMA8PDwQGJiIt577z24ublh06ZNSEhIaDJW165dMW/ePLz66qsYMmQIDAwMsHnzZml/YmIiTExM4Ovri+DgYAQGBmLAgAFKY6xZswYvv/wyIiMj4eLigoiICJWzbwHA19cX06ZNQ2hoKJ5++mmsWLFC2ufk5ARfX1+4uLg0WWJBlalTpyIzM1MqAKempiIgIKBJsRW4W3A9duwYTp069cBxG2VkZCAiIqLF/YmIiIg6AplozbcGEBERERFp2PDhw6FQKPDZZ59pOhSkpaVh9uzZ0jIEmiaEgJOTEyIjI5ud3fvX/t7e3pgzZ470JVnq8tNPP2Ho0KH4+eefVRZwiYiIiDoqLilARERERI+tqqoqpKSkIDAwENra2sjIyMDevXuxZ88eTYf22Pnzzz+RmZmJ0tJSTJ48uUXHyGQyrF27FqdPn1Z7PCUlJdi4cSOLrURERNTpsOBKRERERI8tmUyGXbt2IT4+Hnfu3IGzszO++OILBAQEaDq0x465uTnMzMywdu1amJiYtPg4T09PeHp6qj0e/hsRERFRZ8UlBYiIiIiIiIiIiIjUhF+aRURERERERERERKQmLLgSERERERERERERqQkLrkRERERERERERERqwoIrERERERERERERkZqw4EpERERERERERESkJiy4EhEREREREREREakJC65EREREREREREREasKCKxEREREREREREZGa/D/YIodRXfV8fAAAAABJRU5ErkJggg==", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -167,11 +175,13 @@ "for key, C_rate in C_rates.items():\n", " # load the comsol results\n", " comsol_results_path = pybamm.get_parameters_filepath(\n", - " f\"input/comsol_results/comsol_{key}C.pickle\",\n", + " data_loader.get_data(f\"comsol_{key}C.json\"),\n", " )\n", - " comsol_variables = pickle.load(open(comsol_results_path, \"rb\"))\n", - " comsol_time = comsol_variables[\"time\"]\n", - " comsol_voltage = comsol_variables[\"voltage\"]\n", + "\n", + " comsol_variables = json.load(open(comsol_results_path, \"rb\"))\n", + "\n", + " comsol_time = np.array(comsol_variables[\"time\"])\n", + " comsol_voltage = np.array(comsol_variables[\"voltage\"])\n", "\n", " # update current density\n", " current = 24 * C_rate\n", @@ -278,7 +288,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.3" }, "toc": { "base_numbering": 1, @@ -295,5 +305,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/source/examples/notebooks/models/compare-ecker-data.ipynb b/docs/source/examples/notebooks/models/compare-ecker-data.ipynb index b0db095926..320d35fceb 100644 --- a/docs/source/examples/notebooks/models/compare-ecker-data.ipynb +++ b/docs/source/examples/notebooks/models/compare-ecker-data.ipynb @@ -7,7 +7,7 @@ "source": [ "# Comparing with Experimental Data\n", "\n", - "In this notebook we show how to compare results generated in PyBaMM with experimental data. We compare the results of the DFN model (see the [DFN notebook](DFN.ipynb)) with the experimental data from Ecker et. al. [[3]](#References). Results are compared for a constant current discharge at 1C and at 5C." + "In this notebook we show how to compare results generated in PyBaMM with experimental data. We compare the results of the DFN model (see the [DFN notebook](DFN.ipynb)) with the experimental data from Ecker et. al. [[5]](#References). Results are compared for a constant current discharge at 1C and at 5C." ] }, { @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -52,15 +52,17 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ + "data_loader = pybamm.DataLoader()\n", + "\n", "voltage_data_1C = pd.read_csv(\n", - " \"pybamm/input/discharge_data/Ecker2015/Ecker_1C.csv\", header=None\n", + " f\"{data_loader.get_data(\"Ecker_1C.csv\")}\", header=None\n", ").to_numpy()\n", "voltage_data_5C = pd.read_csv(\n", - " \"pybamm/input/discharge_data/Ecker2015/Ecker_5C.csv\", header=None\n", + " f\"{data_loader.get_data(\"Ecker_5C.csv\")}\", header=None\n", ").to_numpy()" ] }, @@ -82,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -104,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -128,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -145,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -178,21 +180,19 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -270,7 +270,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.8.12 ('conda_jl')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -284,7 +284,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.12.3" }, "toc": { "base_numbering": 1, @@ -306,5 +306,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/source/examples/notebooks/models/composite_particle.ipynb b/docs/source/examples/notebooks/models/composite_particle.ipynb index 5057d57589..387ffd7512 100644 --- a/docs/source/examples/notebooks/models/composite_particle.ipynb +++ b/docs/source/examples/notebooks/models/composite_particle.ipynb @@ -90,8 +90,8 @@ " {\n", " \"Primary: Maximum concentration in negative electrode [mol.m-3]\": 28700,\n", " \"Primary: Initial concentration in negative electrode [mol.m-3]\": 23000,\n", - " \"Primary: Negative electrode diffusivity [m2.s-1]\": 5.5e-14,\n", - " \"Secondary: Negative electrode diffusivity [m2.s-1]\": 1.67e-14,\n", + " \"Primary: Negative particle diffusivity [m2.s-1]\": 5.5e-14,\n", + " \"Secondary: Negative particle diffusivity [m2.s-1]\": 1.67e-14,\n", " \"Secondary: Initial concentration in negative electrode [mol.m-3]\": 277000,\n", " \"Secondary: Maximum concentration in negative electrode [mol.m-3]\": 278000,\n", " }\n", diff --git a/docs/source/examples/notebooks/models/differential-capacity-hysteresis-state.ipynb b/docs/source/examples/notebooks/models/differential-capacity-hysteresis-state.ipynb new file mode 100644 index 0000000000..3a3bb81a53 --- /dev/null +++ b/docs/source/examples/notebooks/models/differential-capacity-hysteresis-state.ipynb @@ -0,0 +1,269 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Differential Capacity Hysteresis State model" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install \"pybamm[plot,cite]\" -q # install PyBaMM if it is not installed\n", + "import pybamm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Equations\n", + "\n", + "Herein the model equations for the Differential Capacity Hysteresis State open-circuit potential model are outlined, as described in Wycisk (2022).\n", + "\n", + "### Hysteresis State Variable\n", + "\n", + "This approach utilizes a state variable to represent the degree of hysteresis at a given time and stoichiometry, $h(z,t)$. The hysteresis is treated separately from the open-circuit potential, where the potential of the electrode is written as\n", + "\n", + "$$ U = U_{avg}^0(z) + H(z) \\cdot h(z,t) - \\eta $$\n", + "\n", + "Where $H(z)$ is a function representing the hysteresis as a function of stoichiometry, $z$, and where $\\eta$ represents the sum of the overpotentials. $U_{avg}^0(z)$ is simply the average of the delithiation and lithiation open-circuit potential branches. $H(z)$ can be determined by finding the half-difference value between the lithiation and delithiation branches across the entire stoichiometry range. The state variable $h(z,t)$ is both stoichiometry and time-dependant, and spans between the range of -1 and 1. The hysteresis state variable $h(z,t)$ can be expressed in differential form with respect to time as\n", + "\n", + "$$ \\frac{dh(z,t)}{dt} = \\left(\\frac{k(z) \\cdot I(t)}{Q_{cell}}\\right)\\left(1-\\text{sgn}\\left(\\frac{dz(t)}{dt}\\right) h(z,t)\\right) $$\n", + "\n", + "where $ k(z) $ is expressed as \n", + "\n", + "$$ k(z) = K \\cdot \\frac{1}{\\left(C_{diff}\\left(z\\right)\\right)^{x}} $$\n", + "\n", + "And where $C_{diff}(z)$ is the differential capacity with respect to potential, expressed as \n", + "\n", + "$$ C_{diff}(z) = \\frac{dQ}{dU_{avg}^0(z)} $$\n", + "\n", + "Here, $Q$ is the capacity of the phase or active material experiencing the voltage hysteresis. The remaining parameters are $K$ and $x$ which are both fitting parameters that affect the response of the hysteresis state decay when passing charge in either direction.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparing the DCHS and Current-Sigmoid model approaches\n", + "\n", + "The behavior of the DCHS model is different than the current-sigmoid model approach for open-circuit potential in systems with hysteresis. Where the current-sigmoid model switches between hysteresis states simply based on the instantaneous current, the DCHS model switches based on the amount of charge passed through the active material phase while also relying on the previous hysteresis state. To assess this differentiated performance, we will compare it to the current-sigmoid model by adapting the Chen2020_composite parameter set.\n", + "\n", + "First we generate the model, and specify the open-circuit potential methods for the negative and positive electrodes. To maintain consistency with the parameter set, two phases for the negative electrode will be defined." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "model_DCHS = pybamm.lithium_ion.DFN(\n", + " {\n", + " \"open-circuit potential\": ((\"single\", \"Wycisk\"), \"single\"),\n", + " \"particle phases\": (\"2\", \"1\"),\n", + " }\n", + ")\n", + "\n", + "model_current_sigmoid = pybamm.lithium_ion.DFN(\n", + " {\n", + " \"open-circuit potential\": ((\"single\", \"current sigmoid\"), \"single\"),\n", + " \"particle phases\": (\"2\", \"1\"),\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, lets define the modifications to the parameter set" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "parameters_DCHS = pybamm.ParameterValues(\"Chen2020_composite\")\n", + "parameters_current_sigmoid = pybamm.ParameterValues(\"Chen2020_composite\")\n", + "\n", + "\n", + "# get the lithiation and delithiation functions\n", + "lithiation_ocp = parameters_DCHS[\"Secondary: Negative electrode lithiation OCP [V]\"]\n", + "delithiation_ocp = parameters_DCHS[\"Secondary: Negative electrode delithiation OCP [V]\"]\n", + "\n", + "\n", + "# define an additional OCP function\n", + "def ocp_avg(sto):\n", + " return (lithiation_ocp(sto) + delithiation_ocp(sto)) / 2\n", + "\n", + "\n", + "# add additional parameters\n", + "parameters_DCHS.update(\n", + " {\n", + " \"Secondary: Negative electrode OCP [V]\": ocp_avg,\n", + " },\n", + " check_already_exists=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to add the additional parameters required by the model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "parameters_DCHS.update(\n", + " {\n", + " \"Secondary: Negative particle hysteresis decay rate\": 0.005,\n", + " \"Secondary: Negative particle hysteresis switching factor\": 10,\n", + " },\n", + " check_already_exists=False,\n", + ")\n", + "\n", + "\n", + "experiment = pybamm.Experiment(\n", + " [\n", + " (\"Discharge at 1 C for 1 hour or until 2.5 V\", \"Rest for 15 minutes\"),\n", + " (\n", + " \"Charge at 1C until 4.2 V\",\n", + " \"Hold at 4.2 V until 0.05 C\",\n", + " \"Rest for 15 minutes\",\n", + " ),\n", + " ]\n", + ")\n", + "\n", + "\n", + "simulation_dchs = pybamm.Simulation(\n", + " model_DCHS, experiment=experiment, parameter_values=parameters_DCHS\n", + ")\n", + "solution_dchs = simulation_dchs.solve(calc_esoh=False)\n", + "\n", + "simulation_current_sigmoid = pybamm.Simulation(\n", + " model_current_sigmoid,\n", + " experiment=experiment,\n", + " parameter_values=parameters_current_sigmoid,\n", + ")\n", + "\n", + "solution_current_sigmoid = simulation_current_sigmoid.solve(calc_esoh=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now plotting the results and the hysteresis state " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e6677ed985c14dd8941223b20650f6fd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=0.0, description='t', max=3.1492654802910014, step=0.03149265480291001…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "output_variables = [\n", + " \"X-averaged negative electrode secondary hysteresis state\",\n", + " \"Negative electrode secondary open-circuit potential [V]\",\n", + " \"Negative electrode secondary stoichiometry\",\n", + " \"Terminal voltage [V]\",\n", + " \"X-averaged negative electrode secondary open-circuit potential [V]\",\n", + "]\n", + "\n", + "pybamm.QuickPlot(solution_dchs, output_variables=output_variables).dynamic_plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "51ea98d2812c4afd97b1b9c33ee95eef", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=0.0, description='t', max=3.1492654802910014, step=0.03149265480291001…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "output_variables = [\n", + " \"Terminal voltage [V]\",\n", + " \"Current [A]\",\n", + " \"Negative electrode secondary open-circuit potential [V]\",\n", + "]\n", + "pybamm.QuickPlot(\n", + " [solution_current_sigmoid, solution_dchs],\n", + " labels=[\"Current sigmoid\", \"DCHS\"],\n", + " output_variables=output_variables,\n", + ").dynamic_plot()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "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": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/source/examples/notebooks/models/latexify.ipynb b/docs/source/examples/notebooks/models/latexify.ipynb index c2a45ff2c8..3901283cc7 100644 --- a/docs/source/examples/notebooks/models/latexify.ipynb +++ b/docs/source/examples/notebooks/models/latexify.ipynb @@ -1,7 +1,6 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -10,7 +9,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -22,6 +20,13 @@ "execution_count": 1, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ERROR: Invalid requirement: '#'\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -36,7 +41,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -53,7 +57,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -67,9 +70,9 @@ "outputs": [ { "data": { - "image/png": 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", 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\n", "text/latex": [ - "$\\displaystyle \\large{\\underline{\\textbf{Single Particle Model Equations}}}\\\\\\\\\\\\ \\textbf{Discharge capacity [A.h]}\\\\\\\\\\frac{d}{d t} Q_{Ah} = \\frac{I}{3600}\\\\\\\\Q_{Ah} = 0.0\\quad \\text{at}\\; t=0\\\\\\\\\\\\ \\textbf{X-averaged negative particle concentration [mol.m-3]}\\\\\\\\\\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,n}} = \\nabla\\cdot \\left(D_{\\mathrm{n}} \\left(\\nabla \\overline{c}_{\\mathrm{s,n}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{n}}^{\\mathrm{typ}}\\\\\\\\\\overline{c}_{\\mathrm{s,n}} = \\int c_{\\mathrm{n}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,n}} = 0.0\\quad \\text{at } r = 0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,n}} = - \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{n}}^{surf} F L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}}}\\quad \\text{at } r = R_{\\mathrm{n}}^{\\mathrm{typ}}\\\\\\\\\\\\ \\textbf{X-averaged positive particle concentration [mol.m-3]}\\\\\\\\\\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,p}} = \\nabla\\cdot \\left(D_{\\mathrm{p}} \\left(\\nabla \\overline{c}_{\\mathrm{s,p}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{p}}^{\\mathrm{typ}}\\\\\\\\\\overline{c}_{\\mathrm{s,p}} = \\int c_{\\mathrm{p}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,p}} = 0.0\\quad \\text{at } r = 0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,p}} = - \\frac{j_{\\mathrm{p}}}{D_{\\mathrm{p}}^{surf} F}\\quad \\text{at } r = R_{\\mathrm{p}}^{\\mathrm{typ}}\\\\\\\\\\\\ \\textbf{Voltage [V]}\\\\\\\\V = - U_\\mathrm{n}(c^\\mathrm{surf}_\\mathrm{s,n}, T) + U_\\mathrm{p}(c^\\mathrm{surf}_\\mathrm{s,p}, T) + \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 j_{\\mathrm{p}}}{j_{\\mathrm{p}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{p}}} - \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}} j_{\\mathrm{n}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{n}}}\\\\\\\\\\\\ \\textbf{Parameters and Variables}\\\\\\\\I = \\text{Current function [A]}\\\\\\\\\\overline{c}_{\\mathrm{s,n}} = \\text{X-averaged negative particle concentration [mol.m-3]}\\\\\\\\D_{\\mathrm{n}} = \\text{Negative electrode diffusivity [m2.s-1]}\\\\\\\\T_{\\mathrm{amb}} = \\text{Ambient temperature [K]}\\\\\\\\c_{\\mathrm{n}}^{\\mathrm{max}} = \\text{Maximum concentration in negative electrode [mol.m-3]}\\\\\\\\\\overline{c}_{\\mathrm{s,p}} = \\text{X-averaged positive particle concentration [mol.m-3]}\\\\\\\\D_{\\mathrm{p}} = \\text{Positive electrode diffusivity [m2.s-1]}\\\\\\\\c_{\\mathrm{p}}^{\\mathrm{max}} = \\text{Maximum concentration in positive electrode [mol.m-3]}$" + "$\\displaystyle \\large{\\underline{\\textbf{Single Particle Model Equations}}}\\\\\\\\\\\\ \\textbf{Discharge capacity [A.h]}\\\\\\\\\\frac{d}{d t} Q_{Ah} = \\frac{I}{3600}\\\\\\\\Q_{Ah} = 0.0\\quad \\text{at}\\; t=0\\\\\\\\\\\\ \\textbf{X-averaged negative particle concentration [mol.m-3]}\\\\\\\\\\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,n}} = \\nabla\\cdot \\left(D_{\\mathrm{n}} \\left(\\nabla \\overline{c}_{\\mathrm{s,n}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{n}}^{\\mathrm{typ}}\\\\\\\\\\overline{c}_{\\mathrm{s,n}} = \\int c_{\\mathrm{n}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,n}} = 0.0\\quad \\text{at } r = 0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,n}} = - \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{n}}^{surf} F L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}}}\\quad \\text{at } r = R_{\\mathrm{n}}^{\\mathrm{typ}}\\\\\\\\\\\\ \\textbf{X-averaged positive particle concentration [mol.m-3]}\\\\\\\\\\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,p}} = \\nabla\\cdot \\left(D_{\\mathrm{p}} \\left(\\nabla \\overline{c}_{\\mathrm{s,p}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{p}}^{\\mathrm{typ}}\\\\\\\\\\overline{c}_{\\mathrm{s,p}} = \\int c_{\\mathrm{p}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,p}} = 0.0\\quad \\text{at } r = 0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,p}} = - \\frac{j_{\\mathrm{p}}}{D_{\\mathrm{p}}^{surf} F}\\quad \\text{at } r = R_{\\mathrm{p}}^{\\mathrm{typ}}\\\\\\\\\\\\ \\textbf{Voltage [V]}\\\\\\\\V = - U_\\mathrm{n}(c^\\mathrm{surf}_\\mathrm{s,n}, T) + U_\\mathrm{p}(c^\\mathrm{surf}_\\mathrm{s,p}, T) + \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 j_{\\mathrm{p}}}{j_{\\mathrm{p}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{p}}} - \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}} j_{\\mathrm{n}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{n}}}\\\\\\\\\\\\ \\textbf{Parameters and Variables}\\\\\\\\I = \\text{Current function [A]}\\\\\\\\\\overline{c}_{\\mathrm{s,n}} = \\text{X-averaged negative particle concentration [mol.m-3]}\\\\\\\\D_{\\mathrm{n}} = \\text{Negative particle diffusivity [m2.s-1]}\\\\\\\\T_{\\mathrm{amb}} = \\text{Ambient temperature [K]}\\\\\\\\c_{\\mathrm{n}}^{\\mathrm{max}} = \\text{Maximum concentration in negative electrode [mol.m-3]}\\\\\\\\\\overline{c}_{\\mathrm{s,p}} = \\text{X-averaged positive particle concentration [mol.m-3]}\\\\\\\\D_{\\mathrm{p}} = \\text{Positive particle diffusivity [m2.s-1]}\\\\\\\\c_{\\mathrm{p}}^{\\mathrm{max}} = \\text{Maximum concentration in positive electrode [mol.m-3]}$" ], "text/plain": [ "\\large{\\underline{\\textbf{Single Particle Model Equations}}}\\\\\\\\\\\\ \\textbf{Dis\n", @@ -91,10 +94,10 @@ "\\overline{a}_{\\mathrm{n}} j_{\\mathrm{n}}_{\\mathrm{n}}}\\\\\\\\\\\\ \\textbf{Parameter\n", "s and Variables}\\\\\\\\I = \\text{Current function [A]}\\\\\\\\\\overline{c}_{\\mathrm{s\n", ",n}} = \\text{X-averaged negative particle concentration [mol.m-3]}\\\\\\\\D_{\\math\n", - "rm{n}} = \\text{Negative electrode diffusivity [m2.s-1]}\\\\\\\\T_{\\mathrm{amb}} = \n", + "rm{n}} = \\text{Negative particle diffusivity [m2.s-1]}\\\\\\\\T_{\\mathrm{amb}} = \n", "\\text{Ambient temperature [K]}\\\\\\\\c_{\\mathrm{n}}_{\\mathrm{s,p}} = \\text{X-aver\n", "aged positive particle concentration [mol.m-3]}\\\\\\\\D_{\\mathrm{p}} = \\text{Posi\n", - "tive electrode diffusivity [m2.s-1]}\\\\\\\\c_{\\mathrm{p}}__{\\mathrm{typ}}\\\\\\\\\\ove\n", + "tive particle diffusivity [m2.s-1]}\\\\\\\\c_{\\mathrm{p}}__{\\mathrm{typ}}\\\\\\\\\\ove\n", "rline{c}__{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0\\\\\\\\\\nabla \\overline{c}__{\n", "surf} F L__{\\mathrm{typ}}\\\\\\\\\\\\ \\textbf{X-averaged positive particle concentra\n", "tion [mol.m-3]}\\\\\\\\\\frac{\\partial}{\\partial t} \\overline{c}__{\\mathrm{typ}}\\\\\\\n", @@ -116,7 +119,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -130,9 +132,9 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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\n", "text/latex": [ - "$\\displaystyle \\left[ \\large{\\underline{\\textbf{Single Particle Model Equations}}}, \\ \\\\ \\textbf{Discharge capacity [A.h]}, \\ \\frac{d}{d t} Q_{Ah} = \\frac{I}{3600}, \\ Q_{Ah} = 0.0\\quad \\text{at}\\; t=0, \\ \\\\ \\textbf{X-averaged negative particle concentration [mol.m-3]}, \\ \\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,n}} = \\nabla\\cdot \\left(D_{\\mathrm{n}} \\left(\\nabla \\overline{c}_{\\mathrm{s,n}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{n}}^{\\mathrm{typ}}, \\ \\overline{c}_{\\mathrm{s,n}} = \\int c_{\\mathrm{n}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0, \\ \\nabla \\overline{c}_{\\mathrm{s,n}} = 0.0\\quad \\text{at } r = 0, \\ \\nabla \\overline{c}_{\\mathrm{s,n}} = - \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{n}}^{surf} F L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}}}\\quad \\text{at } r = R_{\\mathrm{n}}^{\\mathrm{typ}}, \\ \\\\ \\textbf{X-averaged positive particle concentration [mol.m-3]}, \\ \\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,p}} = \\nabla\\cdot \\left(D_{\\mathrm{p}} \\left(\\nabla \\overline{c}_{\\mathrm{s,p}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{p}}^{\\mathrm{typ}}, \\ \\overline{c}_{\\mathrm{s,p}} = \\int c_{\\mathrm{p}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0, \\ \\nabla \\overline{c}_{\\mathrm{s,p}} = 0.0\\quad \\text{at } r = 0, \\ \\nabla \\overline{c}_{\\mathrm{s,p}} = - \\frac{j_{\\mathrm{p}}}{D_{\\mathrm{p}}^{surf} F}\\quad \\text{at } r = R_{\\mathrm{p}}^{\\mathrm{typ}}, \\ \\\\ \\textbf{Voltage [V]}, \\ V = - U_\\mathrm{n}(c^\\mathrm{surf}_\\mathrm{s,n}, T) + U_\\mathrm{p}(c^\\mathrm{surf}_\\mathrm{s,p}, T) + \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 j_{\\mathrm{p}}}{j_{\\mathrm{p}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{p}}} - \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}} j_{\\mathrm{n}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{n}}}, \\ \\\\ \\textbf{Parameters and Variables}, \\ I = \\text{Current function [A]}, \\ \\overline{c}_{\\mathrm{s,n}} = \\text{X-averaged negative particle concentration [mol.m-3]}, \\ D_{\\mathrm{n}} = \\text{Negative electrode diffusivity [m2.s-1]}, \\ T_{\\mathrm{amb}} = \\text{Ambient temperature [K]}, \\ c_{\\mathrm{n}}^{\\mathrm{max}} = \\text{Maximum concentration in negative electrode [mol.m-3]}, \\ \\overline{c}_{\\mathrm{s,p}} = \\text{X-averaged positive particle concentration [mol.m-3]}, \\ D_{\\mathrm{p}} = \\text{Positive electrode diffusivity [m2.s-1]}, \\ c_{\\mathrm{p}}^{\\mathrm{max}} = \\text{Maximum concentration in positive electrode [mol.m-3]}\\right]$" + "$\\displaystyle \\left[ \\large{\\underline{\\textbf{Single Particle Model Equations}}}, \\ \\\\ \\textbf{Discharge capacity [A.h]}, \\ \\frac{d}{d t} Q_{Ah} = \\frac{I}{3600}, \\ Q_{Ah} = 0.0\\quad \\text{at}\\; t=0, \\ \\\\ \\textbf{X-averaged negative particle concentration [mol.m-3]}, \\ \\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,n}} = \\nabla\\cdot \\left(D_{\\mathrm{n}} \\left(\\nabla \\overline{c}_{\\mathrm{s,n}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{n}}^{\\mathrm{typ}}, \\ \\overline{c}_{\\mathrm{s,n}} = \\int c_{\\mathrm{n}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0, \\ \\nabla \\overline{c}_{\\mathrm{s,n}} = 0.0\\quad \\text{at } r = 0, \\ \\nabla \\overline{c}_{\\mathrm{s,n}} = - \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{n}}^{surf} F L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}}}\\quad \\text{at } r = R_{\\mathrm{n}}^{\\mathrm{typ}}, \\ \\\\ \\textbf{X-averaged positive particle concentration [mol.m-3]}, \\ \\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,p}} = \\nabla\\cdot \\left(D_{\\mathrm{p}} \\left(\\nabla \\overline{c}_{\\mathrm{s,p}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{p}}^{\\mathrm{typ}}, \\ \\overline{c}_{\\mathrm{s,p}} = \\int c_{\\mathrm{p}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0, \\ \\nabla \\overline{c}_{\\mathrm{s,p}} = 0.0\\quad \\text{at } r = 0, \\ \\nabla \\overline{c}_{\\mathrm{s,p}} = - \\frac{j_{\\mathrm{p}}}{D_{\\mathrm{p}}^{surf} F}\\quad \\text{at } r = R_{\\mathrm{p}}^{\\mathrm{typ}}, \\ \\\\ \\textbf{Voltage [V]}, \\ V = - U_\\mathrm{n}(c^\\mathrm{surf}_\\mathrm{s,n}, T) + U_\\mathrm{p}(c^\\mathrm{surf}_\\mathrm{s,p}, T) + \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 j_{\\mathrm{p}}}{j_{\\mathrm{p}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{p}}} - \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}} j_{\\mathrm{n}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{n}}}, \\ \\\\ \\textbf{Parameters and Variables}, \\ I = \\text{Current function [A]}, \\ \\overline{c}_{\\mathrm{s,n}} = \\text{X-averaged negative particle concentration [mol.m-3]}, \\ D_{\\mathrm{n}} = \\text{Negative particle diffusivity [m2.s-1]}, \\ T_{\\mathrm{amb}} = \\text{Ambient temperature [K]}, \\ c_{\\mathrm{n}}^{\\mathrm{max}} = \\text{Maximum concentration in negative electrode [mol.m-3]}, \\ \\overline{c}_{\\mathrm{s,p}} = \\text{X-averaged positive particle concentration [mol.m-3]}, \\ D_{\\mathrm{p}} = \\text{Positive particle diffusivity [m2.s-1]}, \\ c_{\\mathrm{p}}^{\\mathrm{max}} = \\text{Maximum concentration in positive electrode [mol.m-3]}\\right]$" ], "text/plain": [ "⎡ \n", @@ -149,159 +151,177 @@ "\n", " \n", " \n", - " d \n", - "\\textbf{X-averaged negative particle concentration [mol.m-3]}, ──(\\overline{c}\n", - " dt \n", + " d │I│ \n", + "\\textbf{Throughput capacity [A.h]}, ──(Qt_Ah) = ────, Qt_{Ah} = 0.0\\quad \\text\n", + " dt 3600 \n", "\n", " \n", " \n", " \n", - "_{\\mathrm{s,n}}) = \\nabla\\cdot \\left(D_{\\mathrm{n}} \\left(\\nabla \\overline{c}_\n", + "{at}\\; t=0, \\\\ \\textbf{X-averaged negative particle concentration [mol.m-3]}, \n", " \n", "\n", " \n", " \n", + "d \n", + "──(\\overline{c}_{\\mathrm{s,n}}) = \\nabla\\cdot \\left(D_{\\mathrm{n}} \\left(\\nabl\n", + "dt \n", + "\n", + " \n", " \n", - "{\\mathrm{s,n}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{n}}__{\\mathrm{typ}}, \\ove\n", + " \n", + "a \\overline{c}_{\\mathrm{s,n}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{n}}__{\\mat\n", " \n", "\n", " \n", " \n", " \n", - "rline{c}_{\\mathrm{s,n}} = \\int c_{\\mathrm{n}}__{\\mathrm{init}}\\, dxn\\quad \\tex\n", + "hrm{typ}}, \\overline{c}_{\\mathrm{s,n}} = \\int c_{\\mathrm{n}}__{\\mathrm{init}}\\\n", " \n", "\n", " \n", " \n", " \n", - "t{at}\\; t=0, \\nabla \\overline{c}_{\\mathrm{s,n}} = 0.0\\quad \\text{at } r = 0, \n", + ", dxn\\quad \\text{at}\\; t=0, \\nabla \\overline{c}_{\\mathrm{s,n}} = 0.0\\quad \\te\n", " \n", "\n", " \n", " \n", " \n", - "\\nabla \\overline{c}_{\\mathrm{s,n}} = - \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{n}}\n", + "xt{at } r = 0, \\nabla \\overline{c}_{\\mathrm{s,n}} = - \\frac{i_{\\mathrm{cell}}}\n", " \n", "\n", " \n", " \n", " \n", - "_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}}}\\quad \\text{at } r = R_{\\mathrm{n}}__\n", + "{D_{\\mathrm{n}}_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}}}\\quad \\text{at } r = R\n", " \n", "\n", " \n", " \n", " \n", - "{surf} F L__{\\mathrm{typ}}, \\\\ \\textbf{X-averaged positive particle concentrat\n", + "_{\\mathrm{n}}__{surf} F L__{\\mathrm{typ}}, \\\\ \\textbf{X-averaged positive part\n", " \n", "\n", " \n", " \n", - " d \n", - "ion [mol.m-3]}, ──(\\overline{c}_{\\mathrm{s,p}}) = \\nabla\\cdot \\left(D_{\\mathrm\n", - " dt \n", + " d \n", + "icle concentration [mol.m-3]}, ──(\\overline{c}_{\\mathrm{s,p}}) = \\nabla\\cdot \\\n", + " dt \n", "\n", " \n", " \n", " \n", - "{p}} \\left(\\nabla \\overline{c}_{\\mathrm{s,p}}\\right)\\right)\\quad 0 < r < R_{\\m\n", + "left(D_{\\mathrm{p}} \\left(\\nabla \\overline{c}_{\\mathrm{s,p}}\\right)\\right)\\qua\n", " \n", "\n", " \n", " \n", " \n", - "athrm{p}}__{\\mathrm{typ}}, \\overline{c}_{\\mathrm{s,p}} = \\int c_{\\mathrm{p}}__\n", + "d 0 < r < R_{\\mathrm{p}}__{\\mathrm{typ}}, \\overline{c}_{\\mathrm{s,p}} = \\int c\n", " \n", "\n", " \n", " \n", " \n", - "{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0, \\nabla \\overline{c}_{\\mathrm{s,p}}\n", + "_{\\mathrm{p}}__{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0, \\nabla \\overline{c}\n", " \n", "\n", " \n", " \n", " \n", - " = 0.0\\quad \\text{at } r = 0, \\nabla \\overline{c}_{\\mathrm{s,p}} = - \\frac{j_\n", + "_{\\mathrm{s,p}} = 0.0\\quad \\text{at } r = 0, \\nabla \\overline{c}_{\\mathrm{s,p\n", " \n", "\n", " \n", " \n", " \n", - "{\\mathrm{p}}}{D_{\\mathrm{p}}_{\\mathrm{p}}__{surf} F}\\quad \\text{at } r = R__{\n", + "}} = \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{p}}_{\\mathrm{p}} \\overline{a}_{\\mathr\n", " \n", "\n", " \n", " \n", " \n", - "\\mathrm{typ}}, \\\\ \\textbf{Voltage [V]}, V = -U_\\mathrm{n}(c_\\mathrm{s,n}, T)__\n", + "m{p}}}\\quad \\text{at } r = R_{\\mathrm{p}}__{surf} F L__{\\mathrm{typ}}, \\\\ \\te\n", " \n", "\n", " \n", - " 2.0⋅R⋅T_{\\mat\n", " \n", - "\\mathrm{surf} + U_\\mathrm{p}(c_\\mathrm{s,p}, T)__\\mathrm{surf} + ─────────────\n", + " \n", + "xtbf{Voltage [V]}, V = -U_\\mathrm{n}(c_\\mathrm{s,n}, T)__\\mathrm{surf} + U_\\ma\n", " \n", "\n", - " ⎛ 0.5⋅j_{\\mathrm{p}} ⎞ ⎛ \n", - "hrm{amb}}⋅asinh⎜────────────────────────────⎟ 2.0⋅R⋅T_{\\mathrm{amb}}⋅asinh⎜─\n", - " ⎝j_{\\mathrm{p}}__{\\mathrm{0}}⎠ ⎝L\n", - "───────────────────────────────────────────── - ──────────────────────────────\n", - " F⋅ne_{\\mathrm{p}} \n", + " ⎛ \n", + " 2.0⋅R⋅T_{\\mathrm{amb}}⋅asinh⎜─────\n", + " ⎝L_{\\m\n", + "thrm{p}(c_\\mathrm{s,p}, T)__\\mathrm{surf} - ──────────────────────────────────\n", + " \n", + "\n", + " 0.5⋅i_{\\mathrm{cell}} ⎞ \n", + "────────────────────────────────────────────────────────────────⎟ 2.0⋅R⋅T_{\\\n", + "athrm{n}}⋅\\overline{a}_{\\mathrm{n}}⋅j_{\\mathrm{n}}__{\\mathrm{0}}⎠ \n", + "───────────────────────────────────────────────────────────────── - ──────────\n", + " F \n", "\n", - " 0.5⋅i_{\\mathrm{cell}} ⎞ \n", - "────────────────────────────────────────────────────────────────────⎟ \n", - "_{\\mathrm{n}}⋅\\overline{a}_{\\mathrm{n}}⋅j_{\\mathrm{n}}__{\\mathrm{0}}⎠ \n", - "─────────────────────────────────────────────────────────────────────, \\\\ \\tex\n", - " F⋅ne_{\\mathrm{n}} \n", + " ⎛ 0.5⋅i_{\\mathrm{cell}} \n", + "mathrm{amb}}⋅asinh⎜───────────────────────────────────────────────────────────\n", + " ⎝L_{\\mathrm{p}}⋅\\overline{a}_{\\mathrm{p}}⋅j_{\\mathrm{p}}__{\\\n", + "──────────────────────────────────────────────────────────────────────────────\n", + " F \n", + "\n", + " ⎞ \n", + "──────────⎟ \n", + "mathrm{0}}⎠ \n", + "───────────, \\\\ \\textbf{Parameters and Variables}, I = \\text{Current function \n", + " \n", "\n", " \n", " \n", " \n", - "tbf{Parameters and Variables}, I = \\text{Current function [A]}, \\overline{c}_{\n", + "[A]}, \\overline{c}_{\\mathrm{s,n}} = \\text{X-averaged negative particle concent\n", " \n", "\n", " \n", " \n", " \n", - "\\mathrm{s,n}} = \\text{X-averaged negative particle concentration [mol.m-3]}, D\n", + "_{\\mathrm{n}} = \\text{Negative particle diffusivity [m2.s-1]}, T_{\\mathrm{amb\n", " \n", "\n", " \n", " \n", " \n", - "_{\\mathrm{n}} = \\text{Negative electrode diffusivity [m2.s-1]}, T_{\\mathrm{amb\n", + "-1]}, T_{\\mathrm{amb}} = \\text{Ambient temperature [K]}, c_{\\mathrm{n}}__{\\mat\n", " \n", "\n", " \n", " \n", " \n", - "}} = \\text{Ambient temperature [K]}, c_{\\mathrm{n}}__{\\mathrm{max}} = \\text{Ma\n", + "hrm{max}} = \\text{Maximum concentration in negative electrode [mol.m-3]}, \\ove\n", " \n", "\n", " \n", " \n", " \n", - "ximum concentration in negative electrode [mol.m-3]}, \\overline{c}_{\\mathrm{s,\n", + "rline{c}_{\\mathrm{s,p}} = \\text{X-averaged positive particle concentration [mo\n", " \n", "\n", " \n", " \n", " \n", - "p}} = \\text{X-averaged positive particle concentration [mol.m-3]}, D_{\\mathrm{\n", + "p}} = \\text{Positive particle diffusivity [m2.s-1]}, c_{\\mathrm{p}}__{\\mathrm\n", " \n", "\n", " \n", " \n", " \n", - "p}} = \\text{Positive electrode diffusivity [m2.s-1]}, c_{\\mathrm{p}}__{\\mathrm\n", + "mathrm{p}}__{\\mathrm{max}} = \\text{Maximum concentration in positive electrode\n", " \n", "\n", - " ⎤\n", - " ⎥\n", - " ⎥\n", - "{max}} = \\text{Maximum concentration in positive electrode [mol.m-3]}⎥\n", - " ⎦" + " ⎤\n", + " ⎥\n", + " ⎥\n", + " [mol.m-3]}⎥\n", + " ⎦" ] }, "execution_count": 4, @@ -314,7 +334,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -331,7 +350,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -348,7 +366,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -362,7 +379,7 @@ "outputs": [ { "data": { - "image/png": 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", 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\n", "text/latex": [ "$\\displaystyle \\large{\\underline{\\textbf{Single Particle Model with electrolyte Equations}}}$" ], @@ -375,7 +392,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ "$\\displaystyle \\\\ \\textbf{Discharge capacity [A.h]}$" ], @@ -388,7 +405,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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\n", "text/latex": [ "$\\displaystyle \\frac{d}{d t} Q_{Ah} = \\frac{I}{3600}$" ], @@ -403,7 +420,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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\n", "text/latex": [ "$\\displaystyle Q_{Ah} = 0.0\\quad \\text{at}\\; t=0$" ], @@ -416,7 +433,48 @@ }, { "data": { - "image/png": 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QEGgINARWIxA3zPRFa7upWA3lYkNhy0vm/njjsr2TsghZq9AQaAg0BBoCtxwB3idMXzjeciz27j5fIfmrwIv/AyudsADQ0wIjAAAAAElFTkSuQmCC", + "image/png": 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+ "text/latex": [ + "$\\displaystyle \\\\ \\textbf{Throughput capacity [A.h]}$" + ], + "text/plain": [ + "\\\\ \\textbf{Throughput capacity [A.h]}" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/latex": [ + "$\\displaystyle \\frac{d}{d t} Qt_{Ah} = \\frac{\\left|{I}\\right|}{3600}$" + ], + "text/plain": [ + "d │I│ \n", + "──(Qt_Ah) = ────\n", + "dt 3600" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/latex": [ + "$\\displaystyle Qt_{Ah} = 0.0\\quad \\text{at}\\; t=0$" + ], + "text/plain": [ + "Qt_{Ah} = 0.0\\quad \\text{at}\\; t=0" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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QEGgINARWIxA3zPRFa7upWA3lYkNhy0vm/njjsr2TsghZq9AQaAg0BBoCtxwB3idMXzjeciz27j5fIfmrwIv/AyudsADQ0wIjAAAAAElFTkSuQmCC\n", "text/latex": [ "$\\displaystyle \\\\ \\textbf{X-averaged negative particle concentration [mol.m-3]}$" ], @@ -429,7 +487,7 @@ }, { "data": { - "image/png": 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6Bj0Y8jQ/GvECG892BBwBR8AR2AcBPGyOmvS3OgJPlfFSEcOc+0r6zv51rIun7uQIOAKOgCOwAgJ42BBedHeUjwwZYE6FcJ6Qv7XJ/YALnnnIV3nYVlFc2gNXVSdHwBFwBByBfRBIj/XhTT9SSL9iziHvkveNN82+Nr+St1G97rgf106OgCPgCDgCbRH4Dz+2hkkLf2m4AAAAAElFTkSuQmCC", + "image/png": "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\n", "text/latex": [ "$\\displaystyle \\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,n}} = \\nabla\\cdot \\left(D_{\\mathrm{n}} \\left(\\nabla \\overline{c}_{\\mathrm{s,n}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{n}}^{\\mathrm{typ}}$" ], @@ -452,7 +510,7 @@ }, { "data": { - "image/png": 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+ "image/png": "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\n", "text/latex": [ "$\\displaystyle \\overline{c}_{\\mathrm{s,n}} = \\int c_{\\mathrm{n}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0$" ], @@ -466,7 +524,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ "$\\displaystyle \\nabla \\overline{c}_{\\mathrm{s,n}} = 0.0\\quad \\text{at } r = 0$" ], @@ -479,7 +537,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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1xjaxgwZ+tfJKQzPH1e6slqDb2FSSrMjPG43SaIcTwWc5CO22DbK9dW6LxO85SiCO8zf41LVI0S45VtpxPsW9R6RDLOTagrbMNf5oHPT9Uq42tvbb8M4QivKmUcLQkg3pbgJ5r7yrdiWttE6++LEh81ev0GTvyaEpo4lyvYm0zkuvCXWqWNC5SSBA9AafuBbllK+DrtNDwOvGKIHNtce2b/kpAx9o49r9ntJu8JyeBC5Ob0jnMyIBAWDJcXajsGlO8nmD/73cM4Xd1grLOdMcm8ocdZ7zMzrasQ/+o7qjHVV0/CwloE0DYMOMgH2Tt/X+dyPZaDCLYEoo7ZpKGqY56hxuMXKPTQL/A3yY1PEhc25PAAAAAElFTkSuQmCC\n", "text/latex": [ "$\\displaystyle \\nabla \\overline{c}_{\\mathrm{s,n}} = - \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{n}}^{surf} F L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}}}\\quad \\text{at } r = R_{\\mathrm{n}}^{\\mathrm{typ}}$" ], @@ -494,7 +552,7 @@ }, { "data": { - "image/png": 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PVEcQwsNrGm5PGjQNNA00DTQNNA00DUgD7TXNym6gQMPvgXlvxu0H78v83mzwndnKIsySk4yD741V372DnCXSECY10HQ8qZ7W2DRwLTWgecuBsffNyLUU9BYJZZ23YOQWGbUNpWmgaaBpoGlguQbixtj9+k/ldkBbrs7JntItH5/7g/Dz9s3IpLpaY9NA00DTQNPAHdIA3/V1v466Q+M+xVD51Y9/hffh/8t9YNBuNsUnAAAAAElFTkSuQmCC", + "image/png": 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PVEcQwsNrGm5PGjQNNA00DTQNNA00DUgD7TXNym6gQMPvgXlvxu0H78v83mzwndnKIsySk4yD741V372DnCXSECY10HQ8qZ7W2DRwLTWgecuBsffNyLUU9BYJZZ23YOQWGbUNpWmgaaBpoGlguQbixtj9+k/ldkBbrs7JntItH5/7g/Dz9s3IpLpaY9NA00DTQNPAHdIA3/V1v466Q+M+xVD51Y9/hffh/8t9YNBuNsUnAAAAAElFTkSuQmCC\n", "text/latex": [ "$\\displaystyle \\\\ \\textbf{X-averaged positive particle concentration [mol.m-3]}$" ], @@ -507,7 +565,7 @@ }, { "data": { - "image/png": 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uX/f680tHwBFwBDaLgGwk28bVv69iAz5SB7YP3X+pF+qoHKMMcSypoVa7xb8B0DD1hCPgCDgCjkAWAbZEbsXSt5laeN/8mlffoAdD3s6PRjzDxrMdAUfAEXAEliCAh40L39/qCDxVxktFDHPKxe/sX8e6bJ04OQKOgCPgCKyAAB42hBfdHOUjQwaYN50vFPhbm3Ccj/wW4ZnbMb+wraJ6uT3wVjNPOgKOgCPgCMxBoH2sD2/6VIGvehtl/xBAxhlvmn1tzl/vdN0c9+PayRFwBBwBR6AuAv8BAIm8inwXD9QAAAAASUVORK5CYII=", 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uX/f680tHwBFwBDaLgGwk28bVv69iAz5SB7YP3X+pF+qoHKMMcSypoVa7xb8B0DD1hCPgCDgCjkAWAbZEbsXSt5laeN/8mlffoAdD3s6PRjzDxrMdAUfAEXAEliCAh40L39/qCDxVxktFDHPKxe/sX8e6bJ04OQKOgCPgCKyAAB42hBfdHOUjQwaYN50vFPhbm3Ccj/wW4ZnbMb+wraJ6uT3wVjNPOgKOgCPgCMxBoH2sD2/6VIGvehtl/xBAxhlvmn1tzl/vdN0c9+PayRFwBBwBR6AuAv8BAIm8inwXD9QAAAAASUVORK5CYII=\n", "text/latex": [ "$\\displaystyle \\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,p}} = \\nabla\\cdot \\left(D_{\\mathrm{p}} \\left(\\nabla \\overline{c}_{\\mathrm{s,p}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{p}}^{\\mathrm{typ}}$" ], @@ -530,7 +588,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ "$\\displaystyle \\overline{c}_{\\mathrm{s,p}} = \\int c_{\\mathrm{p}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0$" ], @@ -544,7 +602,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ "$\\displaystyle \\nabla \\overline{c}_{\\mathrm{s,p}} = 0.0\\quad \\text{at } r = 0$" ], @@ -557,7 +615,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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6/lA/X8AdxafJ1hho91oOvz6u+hvXYqMZDwAqgFRNNqTZsQH/R3pdZFRNpEwCPIW5HMuv56BBcm6KlooGyBUY0vq8pKnzVDWnc5KAA+AOT1sLbaob6rwBxQUgiAv4ID/6Gvuw07im4FF4LJdKSxtCSxtD+l7hWWjTkxYugYuFj++Uhrf4H30d8DDChmDlJ9oQ5uBpXXT/RCXgGuBxPLi1Fnn9Pttx9OxwvZj8t980lDl4Hk5C3vLOEnAA3FmEkzDAFHP6XwJzbAhz8Py/xx46SQm4CXzAxyatj0P9N3RBYd6GOm0kMMeGMAdPf14nLoF7/Ps7J5fAMUggbghsBFxpsTuAO3VtDp47dcgrH5UE/gMXdJiPZc/TCQAAAABJRU5ErkJggg==\n", "text/latex": [ "$\\displaystyle \\nabla \\overline{c}_{\\mathrm{s,p}} = \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{p}}^{surf} F L_{\\mathrm{p}} \\overline{a}_{\\mathrm{p}}}\\quad \\text{at } r = R_{\\mathrm{p}}^{\\mathrm{typ}}$" ], @@ -572,7 +630,7 @@ }, { "data": { - "image/png": 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", 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\n", "text/latex": [ "$\\displaystyle \\\\ \\textbf{Porosity times concentration [mol.m-3](Negative electrode porosity times concentration [mol.m-3], Separator porosity times concentration [mol.m-3], Positive electrode porosity times concentration [mol.m-3])}$" ], @@ -587,26 +645,22 @@ }, { "data": { - "image/png": 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", + "image/png": 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\n", "text/latex": [ - "$\\displaystyle \\frac{\\partial}{\\partial t} (\\epsilon c)_{\\mathrm{e}} = - \\nabla\\cdot \\left(- D_{\\mathrm{e}} \\epsilon^{b_e} \\left(\\nabla c_{\\mathrm{e}}\\right)\\right) + \\frac{- \\begin{cases}a_{\\mathrm{n}} j_{\\mathrm{n}}\\\\0.0\\\\a_{\\mathrm{p}} j_{\\mathrm{p}}\\end{cases} t_{\\mathrm{+}} + aj}{F}\\quad 0 < x < L_x$" + "$\\displaystyle \\frac{\\partial}{\\partial t} (\\epsilon c)_{\\mathrm{e}} = - \\nabla\\cdot \\left(- D_{\\mathrm{e}} \\mathcal{B} \\left(\\nabla c_{\\mathrm{e}}\\right) + \\frac{i_{\\mathrm{e}} t_{\\mathrm{+}}}{F}\\right) + \\frac{aj}{F}\\quad 0 < x < L_x$" ], "text/plain": [ "d \n", - "──((\\epsilon c)_{\\mathrm{e}}) = - \\nabla\\cdot \\left(- D_{\\mathrm{e}} \\epsilon_\n", + "──((\\epsilon c)_{\\mathrm{e}}) = - \\nabla\\cdot \\left(- D_{\\mathrm{e}} \\mathcal{\n", "dt \n", "\n", " \n", - "e} \\left(\\nabla c_{\\mathrm{e}}\\right)\\right) + \\frac{- \\begin{cases}a_{\\mathrm\n", - " \n", - "\n", - " \n", - "{n}} j_{\\mathrm{n}}\\\\0.0\\\\a_{\\mathrm{p}} j_{\\mathrm{p}}\\end{cases} t_{\\mathrm{\n", + "B} \\left(\\nabla c_{\\mathrm{e}}\\right) + \\frac{i_{\\mathrm{e}} t_{\\mathrm{+}}}{F\n", " \n", "\n", - " \n", - "+}} + aj}{F}\\quad 0 < x < L_x__{b\n", - " " + " \n", + "}\\right) + \\frac{aj}{F}\\quad 0 < x < L_x\n", + " " ] }, "metadata": {}, @@ -614,7 +668,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ "$\\displaystyle (\\epsilon c)_{\\mathrm{e}} = \\epsilon^{\\mathrm{init}} c_{\\mathrm{e}}^{\\mathrm{init}}\\quad \\text{at}\\; t=0$" ], @@ -628,7 +682,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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\n", "text/latex": [ "$\\displaystyle \\\\ \\textbf{Voltage [V]}$" ], @@ -641,9 +695,9 @@ }, { "data": { - "image/png": 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", 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\n", "text/latex": [ - "$\\displaystyle V = - L_{\\mathrm{n}} i_{\\mathrm{cell}} \\left(- \\frac{1.0}{\\kappa_{\\mathrm{e}} \\int \\left(\\epsilon_{\\mathrm{s}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,s}}}\\, dxn} + \\frac{0.333333333333333}{\\kappa_{\\mathrm{e}} \\int \\left(\\epsilon_{\\mathrm{n}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,n}}}\\, dxn}\\right) - U_\\mathrm{n}(c^\\mathrm{surf}_\\mathrm{s,n}, T) + U_\\mathrm{p}(c^\\mathrm{surf}_\\mathrm{s,p}, T) - \\int \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}} j_{\\mathrm{n}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{n}}}\\, dxn + \\int \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 j_{\\mathrm{p}}}{j_{\\mathrm{p}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{p}}}\\, dxn - \\int \\frac{0.5 i_{\\mathrm{cell}} \\left(- L_{x}^{2.0} + L_{\\mathrm{p}}^{2.0} + x_{p} \\left(2.0 L_{x} - x_{p}\\right)\\right)}{L_{\\mathrm{p}} \\kappa_{\\mathrm{e}} \\int \\left(\\epsilon_{\\mathrm{p}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,p}}}\\, dxn}\\, dxn + \\int \\frac{R T_{\\mathrm{amb}} \\left(1+\\frac{dlnf}{dlnc}\\right) \\left(2.0 - 2.0 t_{\\mathrm{+}}\\right) \\log{\\left(\\max\\left(1.0 \\cdot 10^{-15}, \\frac{(\\epsilon c)_{\\mathrm{e,p}}}{\\epsilon_{\\mathrm{p}}^{\\mathrm{init}} \\int c_{\\mathrm{e}}\\, dxn}\\right) \\right)}}{F}\\, dxn + \\int \\frac{0.5 i_{\\mathrm{cell}} x_{n} \\left(- 2.0 L_{\\mathrm{n}} + x_{n}\\right)}{L_{\\mathrm{n}} \\sigma_{\\mathrm{n}} \\int \\epsilon_{\\mathrm{s,n}}^{b_{\\mathrm{s,n}}}\\, dxn}\\, dxn + \\frac{i_{\\mathrm{cell}} \\left(L_{x} - 0.333333333333333 L_{\\mathrm{p}}\\right)}{\\sigma_{\\mathrm{p}} \\int \\epsilon_{\\mathrm{s,p}}^{b_{\\mathrm{s,p}}}\\, dxn} - \\frac{i_{\\mathrm{cell}} \\left(x_{p} + \\frac{0.5 \\left(- L_{x} + x_{p}\\right)^{2.0}}{L_{\\mathrm{p}}}\\right)}{\\sigma_{\\mathrm{p}} \\int \\epsilon_{\\mathrm{s,p}}^{b_{\\mathrm{s,p}}}\\, dxn} - \\frac{i_{\\mathrm{cell}} \\left(L_{\\mathrm{n}} + L_{\\mathrm{s}}\\right)}{\\kappa_{\\mathrm{e}} \\int \\left(\\epsilon_{\\mathrm{s}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,s}}}\\, dxn} - \\frac{R T_{\\mathrm{amb}} \\left(1+\\frac{dlnf}{dlnc}\\right) \\left(2.0 - 2.0 t_{\\mathrm{+}}\\right) \\int \\log{\\left(\\max\\left(1.0 \\cdot 10^{-15}, \\frac{(\\epsilon c)_{\\mathrm{e,n}}}{\\epsilon_{\\mathrm{n}}^{\\mathrm{init}} \\int c_{\\mathrm{e}}\\, dxn}\\right) \\right)}\\, dxn}{F}^{\\mathtt{\\text{right}}}$" + "$\\displaystyle V = - L_{\\mathrm{n}} i_{\\mathrm{cell}} \\left(- \\frac{1.0}{\\kappa_{\\mathrm{e}} \\int \\left(\\epsilon_{\\mathrm{s}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,s}}}\\, dxn} + \\frac{0.333333333333333}{\\kappa_{\\mathrm{e}} \\int \\left(\\epsilon_{\\mathrm{n}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,n}}}\\, dxn}\\right) - U_\\mathrm{n}(c^\\mathrm{surf}_\\mathrm{s,n}, T) + U_\\mathrm{p}(c^\\mathrm{surf}_\\mathrm{s,p}, T) - \\int \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}} j_{\\mathrm{n}}^{\\mathrm{0}}} \\right)}}{F}\\, dxn + \\int \\left(- \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{p}} \\overline{a}_{\\mathrm{p}} j_{\\mathrm{p}}^{\\mathrm{0}}} \\right)}}{F}\\right)\\, dxn - \\int \\frac{0.5 i_{\\mathrm{cell}} \\left(- L_{x}^{2.0} + L_{\\mathrm{p}}^{2.0} + x_{p} \\left(2.0 L_{x} - x_{p}\\right)\\right)}{L_{\\mathrm{p}} \\kappa_{\\mathrm{e}} \\int \\left(\\epsilon_{\\mathrm{p}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,p}}}\\, dxn}\\, dxn + \\int \\frac{R T_{\\mathrm{amb}} \\left(1+\\frac{dlnf}{dlnc}\\right) \\left(2.0 - 2.0 t_{\\mathrm{+}}\\right) \\log{\\left(\\max\\left(1.0 \\cdot 10^{-15}, \\frac{(\\epsilon c)_{\\mathrm{e,p}}}{\\epsilon_{\\mathrm{p}}^{\\mathrm{init}} \\int c_{\\mathrm{e}}\\, dxn}\\right) \\right)}}{F}\\, dxn + \\int \\frac{0.5 i_{\\mathrm{cell}} x_{n} \\left(- 2.0 L_{\\mathrm{n}} + x_{n}\\right)}{L_{\\mathrm{n}} \\sigma_{\\mathrm{n}} \\int \\left(1.0 - \\epsilon_{\\mathrm{n}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{s,n}}}\\, dxn}\\, dxn + \\frac{i_{\\mathrm{cell}} \\left(L_{x} - 0.333333333333333 L_{\\mathrm{p}}\\right)}{\\sigma_{\\mathrm{p}} \\int \\left(1.0 - \\epsilon_{\\mathrm{p}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{s,p}}}\\, dxn} - \\frac{i_{\\mathrm{cell}} \\left(x_{p} + \\frac{0.5 \\left(- L_{x} + x_{p}\\right)^{2.0}}{L_{\\mathrm{p}}}\\right)}{\\sigma_{\\mathrm{p}} \\int \\left(1.0 - \\epsilon_{\\mathrm{p}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{s,p}}}\\, dxn} - \\frac{i_{\\mathrm{cell}} \\left(L_{\\mathrm{n}} + L_{\\mathrm{s}}\\right)}{\\kappa_{\\mathrm{e}} \\int \\left(\\epsilon_{\\mathrm{s}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,s}}}\\, dxn} - \\frac{R T_{\\mathrm{amb}} \\left(1+\\frac{dlnf}{dlnc}\\right) \\left(2.0 - 2.0 t_{\\mathrm{+}}\\right) \\int \\log{\\left(\\max\\left(1.0 \\cdot 10^{-15}, \\frac{(\\epsilon c)_{\\mathrm{e,n}}}{\\epsilon_{\\mathrm{n}}^{\\mathrm{init}} \\int c_{\\mathrm{e}}\\, dxn}\\right) \\right)}\\, dxn}{F}^{\\mathtt{\\text{right}}}$" ], "text/plain": [ "V = - L_{\\mathrm{n}} i_{\\mathrm{cell}} \\left(- \\frac{1.0}{\\kappa_{\\mathrm{e}} \n", @@ -652,30 +706,31 @@ "n}\\right) - U_\\mathrm{n}(c_\\mathrm{s,n}, T) + U_\\mathrm{p}(c_\\mathrm{s,p}, T) \n", "- \\int \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\m\n", "athrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}} j_{\\mathrm{n}}_{\\mathrm\n", - "{n}}}\\, dxn + \\int \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\fr\n", - "ac{0.5 j_{\\mathrm{p}}}{j_{\\mathrm{p}}_{\\mathrm{p}}}\\, dxn - \\int \\frac{0.5 i_{\n", - "\\mathrm{cell}} \\left(- L_{x}_{\\mathrm{p}}_{p} \\left(2.0 L_{x} - x_{p}\\right)\\r\n", - "ight)}{L_{\\mathrm{p}} \\kappa_{\\mathrm{e}} \\int \\left(\\epsilon_{\\mathrm{p}}_{\\m\n", - "athrm{e,p}}}\\, dxn}\\, dxn + \\int \\frac{R T_{\\mathrm{amb}} \\left(1+\\frac{dlnf}{\n", - "dlnc}\\right) \\left(2.0 - 2.0 t_{\\mathrm{+}}\\right) \\log{\\left(\\max\\left(1.0 \\c\n", - "dot 10_{\\mathrm{e,p}}}{\\epsilon_{\\mathrm{p}}_{\\mathrm{e}}\\, dxn}\\right) \\right\n", - ")}}{F}\\, dxn + \\int \\frac{0.5 i_{\\mathrm{cell}} x_{n} \\left(- 2.0 L_{\\mathrm{n\n", - "}} + x_{n}\\right)}{L_{\\mathrm{n}} \\sigma_{\\mathrm{n}} \\int \\epsilon_{\\mathrm{s\n", - ",n}}_{\\mathrm{s,n}}}\\, dxn}\\, dxn + \\frac{i_{\\mathrm{cell}} \\left(L_{x} - 0.33\n", - "3333333333333 L_{\\mathrm{p}}\\right)}{\\sigma_{\\mathrm{p}} \\int \\epsilon_{\\mathr\n", - "m{s,p}}_{\\mathrm{s,p}}}\\, dxn} - \\frac{i_{\\mathrm{cell}} \\left(x_{p} + \\frac{0\n", - ".5 \\left(- L_{x} + x_{p}\\right)_{\\mathrm{p}}}\\right)}{\\sigma_{\\mathrm{p}} \\int\n", - " \\epsilon_{\\mathrm{s,p}}_{\\mathrm{s,p}}}\\, dxn} - \\frac{i_{\\mathrm{cell}} \\lef\n", - "t(L_{\\mathrm{n}} + L_{\\mathrm{s}}\\right)}{\\kappa_{\\mathrm{e}} \\int \\left(\\epsi\n", - "lon_{\\mathrm{s}}_{\\mathrm{e,s}}}\\, dxn} - \\frac{R T_{\\mathrm{amb}} \\left(1+\\fr\n", - "ac{dlnf}{dlnc}\\right) \\left(2.0 - 2.0 t_{\\mathrm{+}}\\right) \\int \\log{\\left(\\m\n", - "ax\\left(1.0 \\cdot 10_{\\mathrm{e,n}}}{\\epsilon_{\\mathrm{n}}_{\\mathrm{e}}\\, dxn}\n", - "\\right) \\right)}\\, dxn}{F}__{\\mathrm{init}}\\right)__{b__{\\mathrm{init}}\\right)\n", - "__{b__\\mathrm{surf}__\\mathrm{surf}__{\\mathrm{0}}} \\right)}}{F ne__{\\mathrm{0}}\n", - "} \\right)}}{F ne__{2.0} + L__{2.0} + x__{\\mathrm{init}}\\right)__{b__{-15}, \\fr\n", - "ac{(\\epsilon c)__{\\mathrm{init}} \\int c__{b__{b__{2.0}}{L__{b__{\\mathrm{init}}\n", - "\\right)__{b__{-15}, \\frac{(\\epsilon c)__{\\mathrm{init}} \\int c__{\\mathtt{\\text\n", - "{right}}}" + "{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{p}} \n", + "\\overline{a}_{\\mathrm{p}} j_{\\mathrm{p}}_{\\mathrm{cell}} \\left(- L_{x}_{\\mathr\n", + "m{p}}_{p} \\left(2.0 L_{x} - x_{p}\\right)\\right)}{L_{\\mathrm{p}} \\kappa_{\\mathr\n", + "m{e}} \\int \\left(\\epsilon_{\\mathrm{p}}_{\\mathrm{e,p}}}\\, dxn}\\, dxn + \\int \\fr\n", + "ac{R T_{\\mathrm{amb}} \\left(1+\\frac{dlnf}{dlnc}\\right) \\left(2.0 - 2.0 t_{\\mat\n", + "hrm{+}}\\right) \\log{\\left(\\max\\left(1.0 \\cdot 10_{\\mathrm{e,p}}}{\\epsilon_{\\ma\n", + "thrm{p}}_{\\mathrm{e}}\\, dxn}\\right) \\right)}}{F}\\, dxn + \\int \\frac{0.5 i_{\\ma\n", + "thrm{cell}} x_{n} \\left(- 2.0 L_{\\mathrm{n}} + x_{n}\\right)}{L_{\\mathrm{n}} \\s\n", + "igma_{\\mathrm{n}} \\int \\left(1.0 - \\epsilon_{\\mathrm{n}}_{\\mathrm{s,n}}}\\, dxn\n", + "}\\, dxn + \\frac{i_{\\mathrm{cell}} \\left(L_{x} - 0.333333333333333 L_{\\mathrm{p\n", + "}}\\right)}{\\sigma_{\\mathrm{p}} \\int \\left(1.0 - \\epsilon_{\\mathrm{p}}_{\\mathrm\n", + "{s,p}}}\\, dxn} - \\frac{i_{\\mathrm{cell}} \\left(x_{p} + \\frac{0.5 \\left(- L_{x}\n", + " + x_{p}\\right)_{\\mathrm{p}}}\\right)}{\\sigma_{\\mathrm{p}} \\int \\left(1.0 - \\ep\n", + "silon_{\\mathrm{p}}_{\\mathrm{s,p}}}\\, dxn} - \\frac{i_{\\mathrm{cell}} \\left(L_{\\\n", + "mathrm{n}} + L_{\\mathrm{s}}\\right)}{\\kappa_{\\mathrm{e}} \\int \\left(\\epsilon_{\\\n", + "mathrm{s}}_{\\mathrm{e,s}}}\\, dxn} - \\frac{R T_{\\mathrm{amb}} \\left(1+\\frac{dln\n", + "f}{dlnc}\\right) \\left(2.0 - 2.0 t_{\\mathrm{+}}\\right) \\int \\log{\\left(\\max\\lef\n", + "t(1.0 \\cdot 10_{\\mathrm{e,n}}}{\\epsilon_{\\mathrm{n}}_{\\mathrm{e}}\\, dxn}\\right\n", + ") \\right)}\\, dxn}{F}__{\\mathrm{init}}\\right)__{b__{\\mathrm{init}}\\right)__{b__\n", + "\\mathrm{surf}__\\mathrm{surf}__{\\mathrm{0}}} \\right)}}{F}\\, dxn + \\int \\left(- \n", + "\\frac{2.0 R T__{\\mathrm{0}}} \\right)}}{F}\\right)\\, dxn - \\int \\frac{0.5 i__{2.\n", + "0} + L__{2.0} + x__{\\mathrm{init}}\\right)__{b__{-15}, \\frac{(\\epsilon c)__{\\ma\n", + "thrm{init}} \\int c__{\\mathrm{init}}\\right)__{b__{\\mathrm{init}}\\right)__{b__{2\n", + ".0}}{L__{\\mathrm{init}}\\right)__{b__{\\mathrm{init}}\\right)__{b__{-15}, \\frac{(\n", + "\\epsilon c)__{\\mathrm{init}} \\int c__{\\mathtt{\\text{right}}}" ] }, "metadata": {}, @@ -683,7 +738,7 @@ }, { "data": { - "image/png": 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+ "image/png": "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\n", "text/latex": [ "$\\displaystyle \\\\ \\textbf{Parameters and Variables}$" ], @@ -696,7 +751,7 @@ }, { "data": { - "image/png": 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+ "image/png": "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\n", "text/latex": [ "$\\displaystyle I = \\text{Current function [A]}$" ], @@ -709,7 +764,7 @@ }, { "data": { - "image/png": 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AAElFTkSuQmCC", + "image/png": 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AAElFTkSuQmCC\n", "text/latex": [ "$\\displaystyle \\overline{c}_{\\mathrm{s,n}} = \\text{X-averaged negative particle concentration [mol.m-3]}$" ], @@ -723,12 +778,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle D_{\\mathrm{n}} = \\text{Negative electrode diffusivity [m2.s-1]}$" + "$\\displaystyle D_{\\mathrm{n}} = \\text{Negative particle diffusivity [m2.s-1]}$" ], "text/plain": [ - "D_{\\mathrm{n}} = \\text{Negative electrode diffusivity [m2.s-1]}" + "D_{\\mathrm{n}} = \\text{Negative particle diffusivity [m2.s-1]}" ] }, "metadata": {}, @@ -736,7 +791,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ "$\\displaystyle T_{\\mathrm{amb}} = \\text{Ambient temperature [K]}$" ], @@ -749,7 +804,7 @@ }, { "data": { - "image/png": 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NDHzswVSuDhBiUPE7mtqIsjBAXEin+vf2roGuga6Bm6SBYX0kwLEXRTbI20psHmxeMfhRuUPXQJMG5E+jwKepU0cKGjDd3ZqrD3W0SJCuvjyXVMfvGuga6Br4KjWgdZI30JVyfxrHekniJJAAiDfU/tIoJXToGjikBk4Oyazz6hroGugauCEauNA4udPD6U4O+GRpn5Nzz3tb10DXwEIamH3ik8hxmtR7tWuga6Br4MZrQAEPd7K4q/dUOb/isbsf3P9i3eQ/stvlfFU7dA10DRxKA013fDRBmagvlLigxIVnbkpz8ZZvjVyO4nLT6BKR2jp0DXQNdA10DXQNdA3sUQPaa9l32YsBfqjSA+i1Lib/Skf8KIlT1vCJ+f9I22mI1M9EwgAAAABJRU5ErkJggg==", + "image/png": 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NDHzswVSuDhBiUPE7mtqIsjBAXEin+vf2roGuga6Bm6SBYX0kwLEXRTbI20psHmxeMfhRuUPXQJMG5E+jwKepU0cKGjDd3ZqrD3W0SJCuvjyXVMfvGuga6Br4KjWgdZI30JVyfxrHekniJJAAiDfU/tIoJXToGjikBk4Oyazz6hroGugauCEauNA4udPD6U4O+GRpn5Nzz3tb10DXwEIamH3ik8hxmtR7tWuga6Br4MZrQAEPd7K4q/dUOb/isbsf3P9i3eQ/stvlfFU7dA10DRxKA013fDRBmagvlLigxIVnbkpz8ZZvjVyO4nLT6BKR2jp0DXQNdA10DXQNdA3sUQPaa9l32YsBfqjSA+i1Lib/Skf8KIlT1vCJ+f9I22mI1M9EwgAAAABJRU5ErkJggg==\n", "text/latex": [ "$\\displaystyle c_{\\mathrm{n}}^{\\mathrm{max}} = \\text{Maximum concentration in negative electrode [mol.m-3]}$" ], @@ -763,7 +818,7 @@ }, { "data": { - "image/png": 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PK45ckgHuTkFyOTUhmUnfW8Z3iTsVfqTMm82O1HFN7Wu1gOZNe82xpQdsu9WSiW4R4732BTqpJJngI7NbW+rYyJoFmgWaBZoFmgV2bgHtUyQTPLQA/FtB1/IAdSn+OP7KRryp4GGP7+ser5lM8Joj/FsJKv2NBc7hp3v+RYKaDZoFmgWaBZoFmgWaBU7JAv8D3DpecfnLRiwAAAAASUVORK5CYII=", + "image/png": "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\n", "text/latex": [ "$\\displaystyle \\overline{c}_{\\mathrm{s,p}} = \\text{X-averaged positive particle concentration [mol.m-3]}$" ], @@ -777,12 +832,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle D_{\\mathrm{p}} = \\text{Positive electrode diffusivity [m2.s-1]}$" + "$\\displaystyle D_{\\mathrm{p}} = \\text{Positive particle diffusivity [m2.s-1]}$" ], "text/plain": [ - "D_{\\mathrm{p}} = \\text{Positive electrode diffusivity [m2.s-1]}" + "D_{\\mathrm{p}} = \\text{Positive particle diffusivity [m2.s-1]}" ] }, "metadata": {}, @@ -790,7 +845,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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rvo0lxZ7vUjFFpfa6lxu3sC4it9TW9Ipncv51snI4Tc6XxA7Dvbd2mC0dr9kDr40ra4z5Dus3wVCgmj4ab+6q9qx7wV+4r7Jh6teAReu8sy3bb6fLrwN+/PAX/Jg1aef497YNeWMkPdgTMVae8eLQJaz11k7lgF0McuzG2LXrwFtde7+4Uvmz2vT+P8CYjFbfEGgINAS2ioDWMjYqAviwaG/VzmbXzUFAvtgLcm5Oz/fvqWF3tL+o8ORMJNWoIdAQaAg0BBoCDYGGwGYQWCPI4fguvjbYTM+aIQ2BhkBDYB4CrGUn85o07oZAQ2DLCMwNcvjYiIUgkPJ8k8M7ud67sO52uzQEGgINgc0joPXrvhLfdHAiHfIqk2/UEGgIXHME5n6Twztr3lefKbEI8BFye38tIBo1BBoCDYGGQENgTQS0v/JNDh/yQvxgxP/C77y2/e0hIIw4fCEu4VT24dwgZ/DhcU96KzQEGgINgYZAQ6Ah0BDYCAJHM+2Ir6pmtmvsDYGGQEOgIdAQaAg0BC4VgaogR8c/vJri/znslOf4rFFDoCHQEGgINAQaAg2BTSPwHwQAUwPhlbOGAAAAAElFTkSuQmCC\n", "text/latex": [ "$\\displaystyle c_{\\mathrm{p}}^{\\mathrm{max}} = \\text{Maximum concentration in positive electrode [mol.m-3]}$" ], @@ -804,12 +859,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle \\epsilon_{\\mathrm{p}}^{\\mathrm{init}} = \\text{Positive electrode porosity}$" + "$\\displaystyle F = \\text{Faraday constant [C.mol-1]}$" ], "text/plain": [ - "\\epsilon_{\\mathrm{p}}__{\\mathrm{init}} = \\text{Positive electrode porosity}" + "F = \\text{Faraday constant [C.mol-1]}" ] }, "metadata": {}, @@ -817,12 +872,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle \\epsilon_{\\mathrm{s}}^{\\mathrm{init}} = \\text{Separator porosity}$" + "$\\displaystyle L_{\\mathrm{p}} = \\text{Positive electrode thickness [m]}$" ], "text/plain": [ - "\\epsilon_{\\mathrm{s}}__{\\mathrm{init}} = \\text{Separator porosity}" + "L_{\\mathrm{p}} = \\text{Positive electrode thickness [m]}" ] }, "metadata": {}, @@ -830,12 +885,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle \\epsilon_{\\mathrm{n}}^{\\mathrm{init}} = \\text{Negative electrode porosity}$" + "$\\displaystyle L_{\\mathrm{s}} = \\text{Separator thickness [m]}$" ], "text/plain": [ - "\\epsilon_{\\mathrm{n}}__{\\mathrm{init}} = \\text{Negative electrode porosity}" + "L_{\\mathrm{s}} = \\text{Separator thickness [m]}" ] }, "metadata": {}, @@ -843,13 +898,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle (\\epsilon c)_{\\mathrm{e,p}} = \\text{Positive electrode porosity times concentration [mol.m-3]}$" + "$\\displaystyle L_{\\mathrm{n}} = \\text{Negative electrode thickness [m]}$" ], "text/plain": [ - "(\\epsilon c)_{\\mathrm{e,p}} = \\text{Positive electrode porosity times concentr\n", - "ation [mol.m-3]}" + "L_{\\mathrm{n}} = \\text{Negative electrode thickness [m]}" ] }, "metadata": {}, @@ -857,13 +911,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle (\\epsilon c)_{\\mathrm{e,s}} = \\text{Separator porosity times concentration [mol.m-3]}$" + "$\\displaystyle L_{z} = \\text{Electrode height [m]}$" ], "text/plain": [ - "(\\epsilon c)_{\\mathrm{e,s}} = \\text{Separator porosity times concentration [mo\n", - "l.m-3]}" + "L_z = \\text{Electrode height [m]}" ] }, "metadata": {}, @@ -871,13 +924,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle (\\epsilon c)_{\\mathrm{e,n}} = \\text{Negative electrode porosity times concentration [mol.m-3]}$" + "$\\displaystyle L_{y} = \\text{Electrode width [m]}$" ], "text/plain": [ - "(\\epsilon c)_{\\mathrm{e,n}} = \\text{Negative electrode porosity times concentr\n", - "ation [mol.m-3]}" + "L_y = \\text{Electrode width [m]}" ] }, "metadata": {}, @@ -885,12 +937,13 @@ }, { "data": { - "image/png": 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+ "image/png": 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\n", "text/latex": [ - "$\\displaystyle D_{\\mathrm{e}} = \\text{Electrolyte diffusivity [m2.s-1]}$" + "$\\displaystyle n_{electrodes parallel} = \\text{Number of electrodes connected in parallel to make a cell}$" ], "text/plain": [ - "D_{\\mathrm{e}} = \\text{Electrolyte diffusivity [m2.s-1]}" + "n_electrodes_parallel = \\text{Number of electrodes connected in parallel to ma\n", + "ke a cell}" ] }, "metadata": {}, @@ -898,13 +951,12 @@ }, { "data": { - "image/png": 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", + "image/png": 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"text/latex": [ - "$\\displaystyle b_{\\mathrm{e,p}} = \\text{Positive electrode Bruggeman coefficient (electrolyte)}$" + "$\\displaystyle t_{\\mathrm{+}} = \\text{Cation transference number}$" ], "text/plain": [ - "b_{\\mathrm{e,p}} = \\text{Positive electrode Bruggeman coefficient (electrolyte\n", - ")}" + "t_{\\mathrm{+}} = \\text{Cation transference number}" ] }, "metadata": {}, @@ -912,12 +964,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle b_{\\mathrm{e,s}} = \\text{Separator Bruggeman coefficient (electrolyte)}$" + "$\\displaystyle \\epsilon_{\\mathrm{p}}^{\\mathrm{init}} = \\text{Positive electrode porosity}$" ], "text/plain": [ - "b_{\\mathrm{e,s}} = \\text{Separator Bruggeman coefficient (electrolyte)}" + "\\epsilon_{\\mathrm{p}}__{\\mathrm{init}} = \\text{Positive electrode porosity}" ] }, "metadata": {}, @@ -925,13 +977,12 @@ }, { "data": { - "image/png": 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", + "image/png": 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"text/latex": [ - "$\\displaystyle b_{\\mathrm{e,n}} = \\text{Negative electrode Bruggeman coefficient (electrolyte)}$" + "$\\displaystyle \\epsilon_{\\mathrm{s}}^{\\mathrm{init}} = \\text{Separator porosity}$" ], "text/plain": [ - "b_{\\mathrm{e,n}} = \\text{Negative electrode Bruggeman coefficient (electrolyte\n", - ")}" + "\\epsilon_{\\mathrm{s}}__{\\mathrm{init}} = \\text{Separator porosity}" ] }, "metadata": {}, @@ -939,12 +990,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle F = \\text{Faraday constant [C.mol-1]}$" + "$\\displaystyle \\epsilon_{\\mathrm{n}}^{\\mathrm{init}} = \\text{Negative electrode porosity}$" ], "text/plain": [ - "F = \\text{Faraday constant [C.mol-1]}" + "\\epsilon_{\\mathrm{n}}__{\\mathrm{init}} = \\text{Negative electrode porosity}" ] }, "metadata": {}, @@ -952,12 +1003,13 @@ }, { "data": { - "image/png": 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+ "image/png": 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\n", "text/latex": [ - "$\\displaystyle R_{\\mathrm{p}} = \\text{Positive particle radius [m]}$" + "$\\displaystyle (\\epsilon c)_{\\mathrm{e,p}} = \\text{Positive electrode porosity times concentration [mol.m-3]}$" ], "text/plain": [ - "R_{\\mathrm{p}} = \\text{Positive particle radius [m]}" + "(\\epsilon c)_{\\mathrm{e,p}} = \\text{Positive electrode porosity times concentr\n", + "ation [mol.m-3]}" ] }, "metadata": {}, @@ -965,13 +1017,13 @@ }, { "data": { - "image/png": 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OmxM/G7ITvRVtHYsXtwSDj4damKk9Gyfv6u1jkZbmvW5HoCPQEegI3GIEtHdw82y30nv9Fu9UYI77NgdWbrUfDxyFY+ykFOYVQz81HaNxuk4dgY5AR6AjcPII/B9YGgOaaoCs7gAAAABJRU5ErkJggg==\n", "text/latex": [ - "$\\displaystyle \\epsilon_{\\mathrm{s,p}} = \\text{Positive electrode active material volume fraction}$" + "$\\displaystyle (\\epsilon c)_{\\mathrm{e,s}} = \\text{Separator porosity times concentration [mol.m-3]}$" ], "text/plain": [ - "\\epsilon_{\\mathrm{s,p}} = \\text{Positive electrode active material volume frac\n", - "tion}" + "(\\epsilon c)_{\\mathrm{e,s}} = \\text{Separator porosity times concentration [mo\n", + "l.m-3]}" ] }, "metadata": {}, @@ -979,12 +1031,13 @@ }, { "data": { - "image/png": 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+ "image/png": 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\n", "text/latex": [ - "$\\displaystyle L_{\\mathrm{p}} = \\text{Positive electrode thickness [m]}$" + "$\\displaystyle (\\epsilon c)_{\\mathrm{e,n}} = \\text{Negative electrode porosity times concentration [mol.m-3]}$" ], "text/plain": [ - "L_{\\mathrm{p}} = \\text{Positive electrode thickness [m]}" + "(\\epsilon c)_{\\mathrm{e,n}} = \\text{Negative electrode porosity times concentr\n", + "ation [mol.m-3]}" ] }, "metadata": {}, @@ -992,12 +1045,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle L_{z} = \\text{Electrode height [m]}$" + "$\\displaystyle D_{\\mathrm{e}} = \\text{Electrolyte diffusivity [m2.s-1]}$" ], "text/plain": [ - "L_z = \\text{Electrode height [m]}" + "D_{\\mathrm{e}} = \\text{Electrolyte diffusivity [m2.s-1]}" ] }, "metadata": {}, @@ -1005,12 +1058,13 @@ }, { "data": { - "image/png": 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+ "image/png": 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\n", "text/latex": [ - "$\\displaystyle L_{y} = \\text{Electrode width [m]}$" + "$\\displaystyle b_{\\mathrm{e,p}} = \\text{Positive electrode Bruggeman coefficient (electrolyte)}$" ], "text/plain": [ - "L_y = \\text{Electrode width [m]}" + "b_{\\mathrm{e,p}} = \\text{Positive electrode Bruggeman coefficient (electrolyte\n", + ")}" ] }, "metadata": {}, @@ -1018,13 +1072,26 @@ }, { "data": { - "image/png": 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", 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A9EnelY6++1tdkAPrINqFgxrX3/Zf0tWlVL+8JuJ1Jd8ybPa2kIohCwvvCi9h3KBZoFmgWaBZYH8WUGzlRMphxW4C98e8caq2gOzPjWzng7t9LqRc4YZjbrVEDbFZoFmgWaBZoFnghlvgXz2gtRaeko6kAAAAAElFTkSuQmCC\n", "text/latex": [ - "$\\displaystyle n_{electrodes parallel} = \\text{Number of electrodes connected in parallel to make a cell}$" + "$\\displaystyle b_{\\mathrm{e,s}} = \\text{Separator Bruggeman coefficient (electrolyte)}$" ], "text/plain": [ - "n_electrodes_parallel = \\text{Number of electrodes connected in parallel to ma\n", - "ke a cell}" + "b_{\\mathrm{e,s}} = \\text{Separator Bruggeman coefficient (electrolyte)}" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/latex": [ + "$\\displaystyle b_{\\mathrm{e,n}} = \\text{Negative electrode Bruggeman coefficient (electrolyte)}$" + ], + "text/plain": [ + "b_{\\mathrm{e,n}} = \\text{Negative electrode Bruggeman coefficient (electrolyte\n", + ")}" ] }, "metadata": {}, @@ -1032,7 +1099,34 @@ }, { "data": { - "image/png": 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+ "image/png": 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+ "text/latex": [ + "$\\displaystyle R_{\\mathrm{p}} = \\text{Positive particle radius [m]}$" + ], + "text/plain": [ + "R_{\\mathrm{p}} = \\text{Positive particle radius [m]}" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/latex": [ + "$\\displaystyle \\epsilon_{\\mathrm{s,p}} = \\text{Positive electrode active material volume fraction}$" + ], + "text/plain": [ + "\\epsilon_{\\mathrm{s,p}} = \\text{Positive electrode active material volume frac\n", + "tion}" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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"text/latex": [ "$\\displaystyle R_{\\mathrm{n}} = \\text{Negative particle radius [m]}$" ], @@ -1045,7 +1139,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ "$\\displaystyle \\epsilon_{\\mathrm{s,n}} = \\text{Negative electrode active material volume fraction}$" ], @@ -1059,12 +1153,14 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle L_{\\mathrm{n}} = \\text{Negative electrode thickness [m]}$" + "$\\displaystyle i_{\\mathrm{e}} = \\begin{cases}\\frac{i_{\\mathrm{cell}} x_{n}}{L_{\\mathrm{n}}}\\\\i_{\\mathrm{cell}}\\\\\\frac{i_{\\mathrm{cell}} \\left(L_{x} - x_{p}\\right)}{L_{\\mathrm{p}}}\\end{cases}$" ], "text/plain": [ - "L_{\\mathrm{n}} = \\text{Negative electrode thickness [m]}" + "i_{\\mathrm{e}} = \\begin{cases}\\frac{i_{\\mathrm{cell}} x_{n}}{L_{\\mathrm{n}}}\\\\\n", + "i_{\\mathrm{cell}}\\\\\\frac{i_{\\mathrm{cell}} \\left(L_{x} - x_{p}\\right)}{L_{\\mat\n", + "hrm{p}}}\\end{cases}" ] }, "metadata": {}, @@ -1072,12 +1168,12 @@ }, { "data": { - "image/png": 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+ "image/png": "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\n", "text/latex": [ - "$\\displaystyle t_{\\mathrm{+}} = \\text{Cation transference number}$" + "$\\displaystyle L_{x} = L_{\\mathrm{n}} + L_{\\mathrm{p}} + L_{\\mathrm{s}}$" ], "text/plain": [ - "t_{\\mathrm{+}} = \\text{Cation transference number}" + "Lₓ = L_{\\mathrm{n}} + L_{\\mathrm{p}} + L_{\\mathrm{s}}" ] }, "metadata": {}, @@ -1085,14 +1181,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle c_{\\mathrm{e}} = \\frac{(\\epsilon c)_{\\mathrm{e}}}{\\begin{cases}\\epsilon_{\\mathrm{n}}^{\\mathrm{init}}\\\\\\epsilon_{\\mathrm{s}}^{\\mathrm{init}}\\\\\\epsilon_{\\mathrm{p}}^{\\mathrm{init}}\\end{cases}}$" + "$\\displaystyle i_{\\mathrm{cell}} = \\frac{I}{A_{\\mathrm{cc}} n_{electrodes parallel}}$" ], "text/plain": [ - "c_{\\mathrm{e}} = \\frac{(\\epsilon c)_{\\mathrm{e}}}{\\begin{cases}\\epsilon_{\\math\n", - "rm{n}}_{\\mathrm{s}}_{\\mathrm{p}}__{\\mathrm{init}}\\\\\\epsilon__{\\mathrm{init}}\\\\\n", - "\\epsilon__{\\mathrm{init}}\\end{cases}}" + "i_{\\mathrm{cell}} = \\frac{I}{A_{\\mathrm{cc}} n_{electrodes parallel}}" ] }, "metadata": {}, @@ -1100,13 +1194,12 @@ }, { "data": { - "image/png": "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", + "image/png": 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"text/latex": [ - "$\\displaystyle (\\epsilon c)_{\\mathrm{e}} = \\begin{cases}(\\epsilon c)_{\\mathrm{e,n}}\\\\(\\epsilon c)_{\\mathrm{e,s}}\\\\(\\epsilon c)_{\\mathrm{e,p}}\\end{cases}$" + "$\\displaystyle A_{\\mathrm{cc}} = L_{y} L_{z}$" ], "text/plain": [ - "(\\epsilon c)_{\\mathrm{e}} = \\begin{cases}(\\epsilon c)_{\\mathrm{e,n}}\\\\(\\epsilo\n", - "n c)_{\\mathrm{e,s}}\\\\(\\epsilon c)_{\\mathrm{e,p}}\\end{cases}" + "A_{\\mathrm{cc}} = L_{y} L_{z}" ] }, "metadata": {}, @@ -1114,15 +1207,14 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle \\epsilon^{b_e} = \\begin{cases}\\left(\\epsilon_{\\mathrm{n}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,n}}}\\\\\\left(\\epsilon_{\\mathrm{s}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,s}}}\\\\\\left(\\epsilon_{\\mathrm{p}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,p}}}\\end{cases}$" + "$\\displaystyle c_{\\mathrm{e}} = \\frac{(\\epsilon c)_{\\mathrm{e}}}{\\begin{cases}\\epsilon_{\\mathrm{n}}^{\\mathrm{init}}\\\\\\epsilon_{\\mathrm{s}}^{\\mathrm{init}}\\\\\\epsilon_{\\mathrm{p}}^{\\mathrm{init}}\\end{cases}}$" ], "text/plain": [ - "\\epsilon_e}__{b = \\begin{cases}\\left(\\epsilon_{\\mathrm{n}}_{\\mathrm{e,n}}}\\\\\\l\n", - "eft(\\epsilon_{\\mathrm{s}}_{\\mathrm{e,s}}}\\\\\\left(\\epsilon_{\\mathrm{p}}_{\\mathr\n", - "m{e,p}}}\\end{cases}__{\\mathrm{init}}\\right)__{b__{\\mathrm{init}}\\right)__{b__{\n", - "\\mathrm{init}}\\right)__{b" + "c_{\\mathrm{e}} = \\frac{(\\epsilon c)_{\\mathrm{e}}}{\\begin{cases}\\epsilon_{\\math\n", + "rm{n}}_{\\mathrm{s}}_{\\mathrm{p}}__{\\mathrm{init}}\\\\\\epsilon__{\\mathrm{init}}\\\\\n", + "\\epsilon__{\\mathrm{init}}\\end{cases}}" ] }, "metadata": {}, @@ -1130,13 +1222,13 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle j_{\\mathrm{p}} = - \\frac{i_{\\mathrm{cell}}}{L_{\\mathrm{p}} \\overline{a}_{\\mathrm{p}}}$" + "$\\displaystyle (\\epsilon c)_{\\mathrm{e}} = \\begin{cases}(\\epsilon c)_{\\mathrm{e,n}}\\\\(\\epsilon c)_{\\mathrm{e,s}}\\\\(\\epsilon c)_{\\mathrm{e,p}}\\end{cases}$" ], "text/plain": [ - "j_{\\mathrm{p}} = - \\frac{i_{\\mathrm{cell}}}{L_{\\mathrm{p}} \\overline{a}_{\\math\n", - "rm{p}}}" + "(\\epsilon c)_{\\mathrm{e}} = \\begin{cases}(\\epsilon c)_{\\mathrm{e,n}}\\\\(\\epsilo\n", + "n c)_{\\mathrm{e,s}}\\\\(\\epsilon c)_{\\mathrm{e,p}}\\end{cases}" ] }, "metadata": {}, @@ -1144,12 +1236,15 @@ }, { "data": { - "image/png": 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+ "image/png": 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\n", "text/latex": [ - "$\\displaystyle \\overline{a}_{\\mathrm{p}} = \\int a_{\\mathrm{p}}\\, dxn$" + "$\\displaystyle \\mathcal{B} = \\begin{cases}\\left(\\epsilon_{\\mathrm{n}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,n}}}\\\\\\left(\\epsilon_{\\mathrm{s}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,s}}}\\\\\\left(\\epsilon_{\\mathrm{p}}^{\\mathrm{init}}\\right)^{b_{\\mathrm{e,p}}}\\end{cases}$" ], "text/plain": [ - "\\overline{a}_{\\mathrm{p}} = \\int a_{\\mathrm{p}}\\, dxn" + "\\mathcal{B} = \\begin{cases}\\left(\\epsilon_{\\mathrm{n}}_{\\mathrm{e,n}}}\\\\\\left(\n", + "\\epsilon_{\\mathrm{s}}_{\\mathrm{e,s}}}\\\\\\left(\\epsilon_{\\mathrm{p}}_{\\mathrm{e,\n", + "p}}}\\end{cases}__{\\mathrm{init}}\\right)__{b__{\\mathrm{init}}\\right)__{b__{\\mat\n", + "hrm{init}}\\right)__{b" ] }, "metadata": {}, @@ -1157,12 +1252,13 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle a_{\\mathrm{p}} = \\frac{3.0 \\epsilon_{\\mathrm{s,p}}}{R_{\\mathrm{p}}}$" + "$\\displaystyle aj = \\begin{cases}a_{\\mathrm{n}} j_{\\mathrm{n}}\\\\0.0\\\\a_{\\mathrm{p}} j_{\\mathrm{p}}\\end{cases}$" ], "text/plain": [ - "a_{\\mathrm{p}} = \\frac{3.0 \\epsilon_{\\mathrm{s,p}}}{R_{\\mathrm{p}}}" + "aj = \\begin{cases}a_{\\mathrm{n}} j_{\\mathrm{n}}\\\\0.0\\\\a_{\\mathrm{p}} j_{\\mathr\n", + "m{p}}\\end{cases}" ] }, "metadata": {}, @@ -1170,12 +1266,13 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle i_{\\mathrm{cell}} = \\frac{I}{A_{\\mathrm{cc}} n_{electrodes parallel}}$" + "$\\displaystyle j_{\\mathrm{p}} = - \\frac{i_{\\mathrm{cell}}}{L_{\\mathrm{p}} \\overline{a}_{\\mathrm{p}}}$" ], "text/plain": [ - "i_{\\mathrm{cell}} = \\frac{I}{A_{\\mathrm{cc}} n_{electrodes parallel}}" + "j_{\\mathrm{p}} = - \\frac{i_{\\mathrm{cell}}}{L_{\\mathrm{p}} \\overline{a}_{\\math\n", + "rm{p}}}" ] }, "metadata": {}, @@ -1183,12 +1280,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle A_{\\mathrm{cc}} = L_{y} L_{z}$" + "$\\displaystyle \\overline{a}_{\\mathrm{p}} = \\int a_{\\mathrm{p}}\\, dxn$" ], "text/plain": [ - "A_{\\mathrm{cc}} = L_{y} L_{z}" + "\\overline{a}_{\\mathrm{p}} = \\int a_{\\mathrm{p}}\\, dxn" ] }, "metadata": {}, @@ -1196,13 +1293,12 @@ }, { "data": { - "image/png": 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+ "image/png": "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\n", "text/latex": [ - "$\\displaystyle j_{\\mathrm{n}} = \\frac{i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}}}$" + "$\\displaystyle a_{\\mathrm{p}} = \\frac{3.0 \\epsilon_{\\mathrm{s,p}}}{R_{\\mathrm{p}}}$" ], "text/plain": [ - "j_{\\mathrm{n}} = \\frac{i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm\n", - "{n}}}" + "a_{\\mathrm{p}} = \\frac{3.0 \\epsilon_{\\mathrm{s,p}}}{R_{\\mathrm{p}}}" ] }, "metadata": {}, @@ -1210,12 +1306,13 @@ }, { "data": { - "image/png": "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", + "image/png": "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\n", "text/latex": [ - "$\\displaystyle \\overline{a}_{\\mathrm{n}} = \\int a_{\\mathrm{n}}\\, dxn$" + "$\\displaystyle j_{\\mathrm{n}} = \\frac{i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}}}$" ], "text/plain": [ - "\\overline{a}_{\\mathrm{n}} = \\int a_{\\mathrm{n}}\\, dxn" + "j_{\\mathrm{n}} = \\frac{i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm\n", + "{n}}}" ] }, "metadata": {}, @@ -1223,12 +1320,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle a_{\\mathrm{n}} = \\frac{3.0 \\epsilon_{\\mathrm{s,n}}}{R_{\\mathrm{n}}}$" + "$\\displaystyle \\overline{a}_{\\mathrm{n}} = \\int a_{\\mathrm{n}}\\, dxn$" ], "text/plain": [ - "a_{\\mathrm{n}} = \\frac{3.0 \\epsilon_{\\mathrm{s,n}}}{R_{\\mathrm{n}}}" + "\\overline{a}_{\\mathrm{n}} = \\int a_{\\mathrm{n}}\\, dxn" ] }, "metadata": {}, @@ -1236,13 +1333,12 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/latex": [ - "$\\displaystyle aj = \\begin{cases}a_{\\mathrm{n}} j_{\\mathrm{n}}\\\\0.0\\\\a_{\\mathrm{p}} j_{\\mathrm{p}}\\end{cases}$" + "$\\displaystyle a_{\\mathrm{n}} = \\frac{3.0 \\epsilon_{\\mathrm{s,n}}}{R_{\\mathrm{n}}}$" ], "text/plain": [ - "aj = \\begin{cases}a_{\\mathrm{n}} j_{\\mathrm{n}}\\\\0.0\\\\a_{\\mathrm{p}} j_{\\mathr\n", - "m{p}}\\end{cases}" + "a_{\\mathrm{n}} = \\frac{3.0 \\epsilon_{\\mathrm{s,n}}}{R_{\\mathrm{n}}}" ] }, "metadata": {}, @@ -1256,7 +1352,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1274,9 +1369,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", - "[2] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", - "[3] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", + "[1] Von DAG Bruggeman. Berechnung verschiedener physikalischer konstanten von heterogenen substanzen. i. dielektrizitätskonstanten und leitfähigkeiten der mischkörper aus isotropen substanzen. Annalen der physik, 416(7):636–664, 1935.\n", + "[2] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", + "[3] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", + "[4] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", "\n" ] } @@ -1288,7 +1384,7 @@ ], "metadata": { "kernelspec": { - "display_name": "pybamm", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1302,7 +1398,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.13" }, "vscode": { "interpreter": { diff --git a/docs/source/examples/notebooks/models/lead-acid.ipynb b/docs/source/examples/notebooks/models/lead-acid.ipynb index ccfa35b091..5e6342cfa4 100644 --- a/docs/source/examples/notebooks/models/lead-acid.ipynb +++ b/docs/source/examples/notebooks/models/lead-acid.ipynb @@ -25,9 +25,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.3.1 is available.\n", - "You should consider upgrading via the '/home/mrobins/git/PyBaMM/env/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n", - "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n" + "\u001B[33mWARNING: You are using pip version 22.0.4; however, version 22.3.1 is available.\n", + "You should consider upgrading via the '/home/mrobins/git/PyBaMM/env/bin/python -m pip install --upgrade pip' command.\u001B[0m\u001B[33m\n", + "\u001B[0mNote: you may need to restart the kernel to use updated packages.\n" ] } ], diff --git a/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb b/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb index 127763ec35..ad428e6791 100644 --- a/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb +++ b/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb @@ -22,13 +22,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[33mWARNING: pybamm 23.5 does not provide the extra 'cite'\u001b[0m\u001b[33m\n", - "\u001b[0m\u001b[33mWARNING: pybamm 23.5 does not provide the extra 'plot'\u001b[0m\u001b[33m\n", - "\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "At t = 57.3387, , mxstep steps taken before reaching tout.\n", + "At t = 57.3387 and h = 7.05477e-15, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "At t = 57.3387, , mxstep steps taken before reaching tout.\n", + "At t = 57.3387, , mxstep steps taken before reaching tout.\n" + ] } ], "source": [ @@ -46,7 +51,6 @@ "param = pybamm.ParameterValues(\"Ai2020\")\n", "param.update({\"Negative electrode LAM constant proportional term [s-1]\": 1e-4 / 3600})\n", "param.update({\"Positive electrode LAM constant proportional term [s-1]\": 1e-4 / 3600})\n", - "total_cycles = 2\n", "experiment = pybamm.Experiment(\n", " [\n", " \"Discharge at 1C until 3 V\",\n", @@ -54,13 +58,12 @@ " \"Charge at 1C until 4.2 V\",\n", " \"Hold at 4.199 V for 600 seconds\",\n", " ]\n", - " * total_cycles\n", ")\n", "sim = pybamm.Simulation(\n", " model,\n", " experiment=experiment,\n", " parameter_values=param,\n", - " solver=pybamm.CasadiSolver(\"fast with events\"),\n", + " discretisation_kwargs={\"remove_independent_variables_from_rhs\": True},\n", ")\n", "solution = sim.solve(calc_esoh=False)" ] @@ -80,12 +83,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3558ac2f7db145baadec9a28e236483e", + "model_id": "6b19474c3912495eb75217e009760637", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=4.5113500706445695, step=0.04511350070644569…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=2.329196798170269, step=0.02329196798170269)…" ] }, "metadata": {}, @@ -94,7 +97,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -129,15 +132,33 @@ "execution_count": 3, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "At t = 57.3387, , mxstep steps taken before reaching tout.\n", + "At t = 57.3387 and h = 7.05477e-15, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "At t = 57.3387, , mxstep steps taken before reaching tout.\n", + "At t = 57.3387, , mxstep steps taken before reaching tout.\n", + "At t = 57.3307, , mxstep steps taken before reaching tout.\n", + "At t = 57.3307, , mxstep steps taken before reaching tout.\n", + "At t = 57.3307, , mxstep steps taken before reaching tout.\n", + "At t = 57.3307, , mxstep steps taken before reaching tout.\n", + "At t = 57.2504, , mxstep steps taken before reaching tout.\n", + "At t = 57.2504, , mxstep steps taken before reaching tout.\n", + "At t = 57.2504, , mxstep steps taken before reaching tout.\n", + "At t = 57.2504, , mxstep steps taken before reaching tout.\n" + ] + }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9ff0e45a0c8c47b2b8d21da91b6110c1", + "model_id": "789a681c8c574bb8b3d3016a844dd9a2", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=4.5113500706445695, step=0.04511350070644569…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=2.329196798170269, step=0.02329196798170269)…" ] }, "metadata": {}, @@ -146,7 +167,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -166,7 +187,7 @@ " model,\n", " experiment=experiment,\n", " parameter_values=param,\n", - " solver=pybamm.CasadiSolver(\"fast with events\"),\n", + " discretisation_kwargs={\"remove_independent_variables_from_rhs\": True},\n", " )\n", " solution = sim.solve(calc_esoh=False)\n", " solutions.append(solution)\n", @@ -204,12 +225,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "53f5eacbb94b4ae29acee57db4bf7785", + "model_id": "ad36439975754b29bbbef1bd94379408", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=3.5075529064499813, step=0.03507552906449981…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=1.8531298311682403, step=0.01853129831168240…" ] }, "metadata": {}, @@ -218,7 +239,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -234,21 +255,7 @@ " }\n", ")\n", "param = pybamm.ParameterValues(\"Chen2020\")\n", - "param.update(\n", - " {\n", - " \"Negative electrode reaction-driven LAM factor [m3.mol-1]\": 1e-3,\n", - " }\n", - ")\n", - "total_cycles = 2\n", - "experiment = pybamm.Experiment(\n", - " [\n", - " \"Discharge at 1C until 3 V\",\n", - " \"Rest for 600 seconds\",\n", - " \"Charge at 1C until 4.2 V\",\n", - " \"Hold at 4.199 V for 600 seconds\",\n", - " ]\n", - " * total_cycles\n", - ")\n", + "param.update({\"Negative electrode reaction-driven LAM factor [m3.mol-1]\": 1e-3})\n", "sim = pybamm.Simulation(\n", " model,\n", " experiment=experiment,\n", @@ -293,12 +300,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "15acf6373f874463ba95de522d12fc89", + "model_id": "91ea043e10d342049929095e48e98c5e", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=3.4962610293431426, step=0.03496261029343142…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=1.8506629989989005, step=0.01850662998998900…" ] }, "metadata": {}, @@ -307,7 +314,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -333,16 +340,6 @@ " },\n", " check_already_exists=False,\n", ")\n", - "total_cycles = 2\n", - "experiment = pybamm.Experiment(\n", - " [\n", - " \"Discharge at 1C until 3 V\",\n", - " \"Rest for 600 seconds\",\n", - " \"Charge at 1C until 4.2 V\",\n", - " \"Hold at 4.199 V for 600 seconds\",\n", - " ]\n", - " * total_cycles\n", - ")\n", "sim = pybamm.Simulation(\n", " model,\n", " experiment=experiment,\n", @@ -395,6 +392,13 @@ "source": [ "pybamm.print_citations()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -413,7 +417,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.11.8" }, "toc": { "base_numbering": 1, diff --git a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb index a11046d967..fa394a3417 100644 --- a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb +++ b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb @@ -49,17 +49,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" + ] } ], "source": [ "%pip install \"pybamm[plot,cite]\" -q # install PyBaMM if it is not installed\n", "import pybamm\n", - "import pickle\n", + "import json\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import scipy.interpolate as interp" @@ -127,15 +131,16 @@ "param.update(\n", " {\n", " \"Current function [A]\": I_1C * 3,\n", - " \"Negative electrode diffusivity [m2.s-1]\": 3.9 * 10 ** (-14),\n", - " \"Positive electrode diffusivity [m2.s-1]\": 10 ** (-13),\n", + " \"Negative particle diffusivity [m2.s-1]\": 3.9 * 10 ** (-14),\n", + " \"Positive particle diffusivity [m2.s-1]\": 10 ** (-13),\n", " \"Negative current collector surface heat transfer coefficient [W.m-2.K-1]\": 10,\n", " \"Positive current collector surface heat transfer coefficient [W.m-2.K-1]\": 10,\n", " \"Negative tab heat transfer coefficient [W.m-2.K-1]\": 10,\n", " \"Positive tab heat transfer coefficient [W.m-2.K-1]\": 10,\n", " \"Edge heat transfer coefficient [W.m-2.K-1]\": 10,\n", " \"Total heat transfer coefficient [W.m-2.K-1]\": 10,\n", - " }\n", + " },\n", + " check_already_exists=False,\n", ")" ] }, @@ -176,12 +181,21 @@ "cell_type": "code", "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading file 'comsol_1plus1D_3C.json' from 'https://github.com/pybamm-team/pybamm-data/releases/download/v1.0.0/comsol_1plus1D_3C.json' to '/home/santa/.cache/pybamm'.\n" + ] + } + ], "source": [ + "data_loader = pybamm.DataLoader()\n", "comsol_results_path = pybamm.get_parameters_filepath(\n", - " \"input/comsol_results/comsol_1plus1D_3C.pickle\"\n", + " f\"{data_loader.get_data(\"comsol_1plus1D_3C.json\")}\"\n", ")\n", - "comsol_variables = pickle.load(open(comsol_results_path, \"rb\"))" + "comsol_variables = json.load(open(comsol_results_path))" ] }, { @@ -210,7 +224,7 @@ " solutions[name] = sim.solve(t_eval=t_eval)\n", " else:\n", " # solve at COMSOL times using Casadi solver in \"fast\" mode\n", - " t_eval = comsol_variables[\"time\"]\n", + " t_eval = np.array(comsol_variables[\"time\"])\n", " solutions[name] = sim.solve(\n", " solver=pybamm.CasadiSolver(mode=\"fast\"), t_eval=t_eval\n", " )" @@ -247,7 +261,7 @@ "outputs": [], "source": [ "# set up times\n", - "comsol_t = comsol_variables[\"time\"]\n", + "comsol_t = np.array(comsol_variables[\"time\"])\n", "pybamm_t = comsol_t\n", "# set up space\n", "mesh = simulations[\"1+1D DFN\"].mesh\n", @@ -262,8 +276,8 @@ " to match nodes, and then create function to interpolate in time)\n", " \"\"\"\n", "\n", - " comsol_z = comsol_variables[variable_name + \"_z\"]\n", - " variable = comsol_variables[variable_name]\n", + " comsol_z = np.array(comsol_variables[variable_name + \"_z\"])\n", + " variable = np.array(comsol_variables[variable_name])\n", " variable = interp.interp1d(comsol_z, variable, axis=0, kind=\"linear\")(z_interp)\n", "\n", " # Make sure to use dimensional time\n", @@ -293,7 +307,7 @@ "source": [ "comsol_voltage = pybamm.Interpolant(\n", " comsol_t,\n", - " comsol_variables[\"voltage\"],\n", + " np.array(comsol_variables[\"voltage\"]),\n", " pybamm.t,\n", " name=\"voltage_comsol\",\n", ")\n", @@ -568,7 +582,7 @@ "outputs": [ { "data": { - "image/png": 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", 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KlRg5cmSVLf9OEARBVB/79u0DAJx++una485+p1xFUpositJmXFx33XXIzs7G999/jzlz5uB3v/udr0wkEsHs2bPxww8/YMaMGWjZsiUef/xxdOnSRYq3rmV7EFXL448/Lg20V65ciVtvvVXaJ4oEFQXnvEqXrSZqBup1v++++7B+/Xp3u+GGG3znNG3aFPfeey+mTp0qTUTv1Ber3doAPeNEEEQg27dvx5EjR9C1a1cAwNdffw0AbkbP+vXr0aZNGzRq1KjafIyVrKwsdOzYEa+99hoefPBBvPbaa+Cc02NbBEEQJwgtWrQAAGzYsAG9e/f2Hd+wYYNUriJp3749GGPYsmVLYJnTTjsNALB582b07dvXd3zz5s3aFS8bN26MSy+9FGPGjMHx48dx8cUX4/Dhw1obLVu2xPXXX4/rr78ef/zjH3HaaafhxRdfxKOPPorTTjtN+uu7alv0kSg/9erVQ35+frXYrSxuvfVWXHXVVe77UaNGYcSIEbjiiivcfbpHYsrL5s2ba8XjozURpw+K/eK+++7DhAkTfNOhOBn84urW48aNw8033+z7o6mTaSOWvfHGGyvSdd91b9KkCdq1axf1vIkTJ+L555/H888/L+1v2rQpUlNTQz+nAe9zcMuWLejevXsZPK8aKKOHIIhAnCV2nfkEvvrqKzRp0gQZGRn4+eefsXz5clx22WXV6WKpGDVqFDZs2ICvv/4a8+bNQ/v27dGzZ8/qdosgCIKoAvr374+2bdvi8ccf983PYJompk+fjszMTPTv37/CbaelpWHQoEF47rnn3GxYkYMHD2LgwIFIS0vDX//6V9/xd955B9u2bcM111yjrf93v/sdli1bhhtuuCHmLNWTTjoJLVq0cP0ZOXIktm3bhnfffddX9q9//SsaN26Miy66KKa6iegwxlC/fv0q3yoz8yUtLQ3t2rVzt+TkZDRr1kzaV9FzqS5duhTffPMNRowYUaH1nijo+kVCQgLq16+PxMREbVnx0df4+HjUr18fSUlJMZWtKMpz3Rs0aIApU6Zg2rRpkihuGAZGjhyJuXPnYu/evb7z8vPzUVxcjDPPPBOdO3fGX//6V+1cPwcPHiy1T5UBCT0EQQTSsmVLAEB2djYAK6PHyea5++67YRgGJkyYUF3ulRone2fq1KlYv349ZfMQBEGcQEQiEfz1r3/FwoULMXz4cGRnZ+Pw4cPIzs7G8OHDsXDhQvzlL3+ptMd5n3vuOZSUlODss8/GW2+9hW3btmHz5s2YNWsW+vTpg/r16+Pvf/87/vvf/2Ls2LH4+uuvsXPnTrz88su48cYbceWVV0rZEiKDBw/GTz/9hMcee0x7/O9//ztuu+02fPjhh/juu++wceNGTJo0CRs3bnQnXx45ciQuv/xyjB49Gi+//DJ27tyJr7/+Grfccgveeecd/OMf/5Amki4pKZEek1i/fn1gRhBRe8nPz3evLwDs2LED69evr9BHsGK1UVBQgJycHPz444/44osv8Pjjj+Oyyy7DpZdeqn1Mh6gbVMZ1Hzt2LBo1auRbqGXatGlo1aoVevXqhVdffRWbNm3Ctm3b8Morr6B79+7Iz88HYwyzZ8/Gt99+i/79++P999/H999/j6+//hrTpk2rOX8Er/R1vQiCqNWcd955nDHG77vvPp6amsr79evHL730Uh6JRPi//vUvzjnnJSUl/I477uCNGzfmjRo14meddVbUZcm///57fskll/C0tDSenp7On3nmGfcYAP7888/ztm3b8saNG/PHH388qp9hy6uL9O3blwPgAPi2bdsCy9Hy6gRBEDWPilia9q233uJt27Z1fxcA4JmZmZW6tLrD3r17+bhx43ibNm14QkICb9myJR82bJi7DDrnnK9YsYIPGjSIp6Sk8ISEBN6lSxf+l7/8hRcXF0t1IWSp619//VVaXv2LL77g1113Hc/MzOSJiYm8cePG/Nxzz/UtpV5UVMSffPJJ3qVLF56QkMBTUlL4oEGD+CeffCKVmz17ttR+znbqqaeWu42ImsXHH3+svdajR4+Oem6sy6vHYmP06NHu/ri4ON60aVM+YMAA/sorr2iXuCbqBrFe91iWV1ePz5s3jwOQllfnnPODBw/yBx54gLdv354nJCTw5s2b8wEDBvD58+dz0zTdclu3buU33HADz8jI4AkJCbxNmzb8mmuu4V988UWgH1W5vDrjvJbMJkQQRLWQk5ODm2++GR999BGOHz+OhIQE9OzZE4899hguuOACAMCiRYvw8MMPY+nSpahfvz6++uornHbaaWjQoIG2zuLiYpxxxhm46qqr8MADD6CwsBDbtm1DVlYWACud+corr8Ts2bOxc+dOnHXWWdi4cSNOPfXUQD9vvPFGLFu2LOqqC88//zzGjRuHs88+G6tXrw4s98gjj+DRRx/FTz/9hCZNmkRpJYIgCKIqOH78OHbs2IHMzEzfowKloaSkBCtXrsS+ffvQokUL9O/fnybmJwiCICqVivodFgs0GTNBEKGkp6fj3XffxcKFCzF06FCsXr0aZ555plQmPj4ehw8fxpYtW9CzZ0/06NEjtM7Vq1fj8OHDmDp1KgzDQFJSkivyODzwwANo0KABTj/9dJxxxhn45ptvQoUewJpj4eeff0ZcXBxSU1O1ZW6//XbcfvvtgXUcP34c+fn5dXL5UYIgCMIiEongvPPOq243CIIgCKJSoDl6CIKIiS1btoAxhg4dOviOXXjhhbj11lsxduxYtGjRAvfeey+KiooC6/rhhx/Qpk0baYI2lebNm7uvY12ZYs+ePWjatCn69esXtWwQL774Ipo2bYonn3yyzHUQBEEQBEEQBEFUF5TRQxBETGzZsgWtW7eWlkkUufvuu3H33Xdjz549uOSSS3D66acHLqPYqlUr7Nq1C5zzClv94f7778d1110HAIGPjMXCiBEjcPrpp7vva8PS8QRBEARBEARBEA4k9BAEERNbtmxBx44dtcc+//xzcM7RvXt3NGzYEPHx8dJcB47gM2fOHADA2WefjYYNG+KPf/wj7r//ft8cPWWhc+fO6Ny5c5nPd2jVqhVatWpV7noIgiAIgiAIgiCqA3p0iyCImPjkk0+waNEi7bFDhw7hd7/7HVJTU9GhQwecc845uPbaa93jP/zwA8455xz3fVxcHBYuXIhVq1ahRYsW6NChg7uEO0EQBEEQBEEQBFF2aNUtgiAqFWeFra+++grx8fHV7Q5BEARRi3FWLGnbtm3go8QEQRAEURM5duwYdu7cWSWrblFGD0EQlUpcXBw2bdpEIg9BEARRbpzfJbQyIkEQBFHbKCwsBABpiovKguboIQiCIAiCIGoFkUgEqampOHDgAABrVcaKmtSfIAiCICoL0zTx008/oV69eoiLq3wZhoQegiAIgiAIotaQnp4OAK7YQxAEQRC1AcMw0Lp16yr5AwXN0UMQBEEQBEHUOkpKSlBUVFTdbhAEQRBETCQkJMAwqmb2HBJ6CIIgCIIgCIIgCIIg6gg0GTNBEARBEARBEARBEEQdgYQegiAIgiAIgiAIgiCIOgIJPQRBEARBEARBEARBEHUEEnoIgiAIgiAIgiAIgiDqCCT0EARBEARBEARBEARB1BFI6CEIgiAIgiAIgiAIgqgjkNBDEARBEARBEARBEARRRyChhyAIgiAIgiAIgiAIoo5AQk8Ubr31VjDGMHPmzAqp77nnnkPbtm2RlJSEXr16Yc2aNdpynHNcfPHFYIxhwYIFFWK7tsfyyCOPoGPHjqhfvz5OOukkDBgwAKtXry5zfSIUy4Iy26srsRQVFWHSpEno2rUr6tevj4yMDNxwww3Yu3dvOSLwqOrrUtfiefvttzFw4EA0btwYjDGsX7++zHWpvPnmm+jYsSOSkpLQtWtXvP/++4FlK+Jz9MYbbwRjTNoGDx5c5vpEol2X8847z2f71ltvrRDbBEEQBEEQRM2AhJ4Q5s+fj88++wwZGRkVUt+///1vTJw4EX/4wx/wxRdfoFu3bhg0aBAOHDjgKztz5kwwxirELlA3YjnttNPwt7/9Dd988w0++eQTtG3bFgMHDsRPP/1UrnoplvJRV2I5evQovvjiC0yZMgVffPEF3n77bWzduhXDhg0rV71A9VyXuhbPkSNH0K9fPzzxxBPlrktk1apVuOaaazBmzBh8+eWXGD58OIYPH44NGzb4ylbk5+jgwYOxb98+d3vttdfKXWes1+Xmm2+WbM+YMaPctgmCIAiCIIgaBCe0/PDDD7xly5Z8w4YNvE2bNvzpp5+Wju/evZv/9re/5Y0aNeInnXQSHzZsGN+xY0donWeffTYfN26c+76kpIRnZGTw6dOnS+W+/PJL3rJlS75v3z4OgM+fP59i0XDo0CEOgH/00UcUC8VSKbGsWbOGA+C7du2q9bFwXjfi2bFjBwfAv/zyS9+xX3/9lY8ZM4Y3adKEN2zYkJ9//vl8/fr1ofVdddVVfMiQIdK+Xr168VtuuUXaF+1ztDSMHj2aX3bZZaFlKuu6/OY3v+F33XVXmX0nCIIgCIIgaj6U0aPBNE1cf/31uO+++9ClSxff8aKiIgwaNAgNGzbEypUr8emnn6JBgwYYPHgwCgsLtXUWFhZi3bp1GDBggLvPMAwMGDAA2dnZ7r6jR4/i2muvxXPPPYf09HSKJYDCwkK89NJLaNSoEbp160axUCwVHgsAHDp0CIwxpKam1vpY6mI8Kr/97W9x4MABfPDBB1i3bh169OiBCy+8ELm5uYHnZGdnS7EAwKBBg6RYon2OloVly5ahWbNm6NChA2677Tb88ssv7rHKvC4AMHfuXDRp0gSnn346Jk+ejKNHj1ZITARBEARBEETNIK66HaiJPPHEE4iLi8Odd96pPf7vf/8bpmniH//4h/tIwuzZs5Gamoply5Zh4MCBvnN+/vlnlJSUoHnz5tL+5s2bY8uWLe77u+++G3379sVll11GsWhYuHAhRo4ciaNHj6JFixZYvHgxmjRpQrFQLBUWi8Px48cxadIkXHPNNUhJSanVsdTFeFQ++eQTrFmzBgcOHEBiYiIA4C9/+QsWLFiA//znPxg7dqz2vJycHG0sOTk57vton6OlZfDgwbjiiiuQmZmJ7777Dg8++CAuvvhiZGdnIxKJVOp1ufbaa9GmTRtkZGTg66+/xqRJk7B161a8/fbbFRIbQRAEQRAEUf2c8Bk9c+fORYMGDdxt+fLleOaZZzBnzpzAeSW++uorbN++HQ0bNnTPS0tLw/Hjx/Hdd99h5cqVUp1z586NyZd33nkHS5cuLfMkn3U5lpUrVwIAzj//fKxfvx6rVq3C4MGDcdVVV7nzT1AsFEtFxAJYGRVXXXUVOOd44YUX3P01NZa6Fk9YLEF89dVXyM/PR+PGjaVzd+zYge+++w67d++W9j/++OMx+bJu3bqon6OljWXkyJEYNmwYunbtiuHDh2PhwoVYu3Ytli1b5sZSGdcFAMaOHYtBgwaha9euGDVqFF599VXMnz8f3333XaljIwiCIAiCIGomJ3xGz7Bhw9CrVy/3/ZtvvokDBw6gdevW7r6SkhLcc889mDlzJnbu3In8/HxkZWVpv1w3bdoUCQkJ0oowzZs3R2JiIiKRCPbv3y+V379/v/tYw9KlS/Hdd9+5j1U4jBgxAv3793cHASdiLC1btgQA1K9fH+3atUO7du3Qu3dvtG/fHi+//DImT55MsVAsFRKLI4rs2rULS5cudbNfANTYWOpaPEGxhJGfn48WLVpo605NTUVqaqoUS1paGgAgPT09NJaVK1dG/RwtbyynnHIKmjRpgu3bt+PCCy+stOuiw/Ft+/btOPXUU0NjIQiCIAiCIGoJ1T1JUE3j559/5t988420ZWRk8EmTJvEtW7Zwzjl/6aWX+EknncQPHTpUqrrPPvtsPn78ePd9SUkJb9mypTtR5r59+3y2AfBnnnmGf//99yd0LEGccsop/A9/+APFQrFUSCyFhYV8+PDhvEuXLvzAgQO+47UplroYD+fBkzF/+OGHPBKJRJ2wWOWqq67il156qbSvT58+7mTMsXyOlpc9e/Zwxhj/73//yzmvvOui45NPPuEA+FdffVU25wmCIAiCIIgaBwk9MaCusHLkyBHevn17ft555/EVK1bw77//nn/88cf8jjvu4Hv27Ams5/XXX+eJiYl8zpw5fNOmTXzs2LE8NTWV5+TkBJ6DCl55p7bGkp+fzydPnsyzs7P5zp07+eeff85vuukmnpiYyDds2ECxUCzljqWwsJAPGzaMn3zyyXz9+vV837597lZQUFCrYqmL8fzyyy/8yy+/5O+99x4HwF9//XX+5Zdf8n379nHOOTdNk/fr149369aN/+9//+M7duzgn376KX/wwQf52rVrA+v99NNPeVxcHP/LX/7CN2/ezP/whz/w+Ph4/s033wSeU55Vtw4fPszvvfdenp2dzXfs2ME/+ugj3qNHD96+fXt+/PhxznnlXZft27fzxx57jH/++ed8x44d/L///S8/5ZRT+LnnnlumWAiCIAiCIIiaCQk9MaD7Ur9v3z5+ww038CZNmvDExER+yimn8JtvvjnqX2CfffZZ3rp1a56QkMDPPvts/tlnn4WWr2yhh/PaEcuxY8f45ZdfzjMyMnhCQgJv0aIFHzZsGF+zZg3FQrFUSCxOpohu+/jjj2tVLHUxntmzZ2tjcTLHOOc8Ly+P33HHHTwjI4PHx8fzVq1a8VGjRvHdu3eH1v3GG2/w0047jSckJPAuXbrw9957L7R8eYSeo0eP8oEDB/KmTZvy+Ph43qZNG37zzTf7RLLKuC67d+/m5557Lk9LS+OJiYm8Xbt2/L777it15hBBEARBEARRs2Gcc15pz4URBEEQBEEQRCVQUlKCoqKi6naDIAiCIGIiISEBhlE162Gd8JMxEwRBEARBELUHzjlycnJw8ODB6naFIAiCIGLGMAxkZmYiISGh0m1RRg9BEARBEARRa9i3bx8OHjyIZs2aoV69emCMVbdLBEEQBBGKaZrYu3cv4uPj0bp160r/3UUZPQRBEARBEEStoKSkxBV5GjduXN3uEARBEETMNG3aFHv37kVxcTHi4+Mr1VbVPCBGEARBEARBEOXEmZOnXr161ewJQRAEQZQO55GtkpKSSrdFQg9BEARBEARRq6DHtQiCIIjaRlX+7iKhhyAIgiAIgiAIgiAIoo5AQk8FU1BQgEceeQQFBQXV7UqFUJfioVhqJhRLzYRiqZnUpVgI4kRi+vTp6NmzJxo2bIhmzZph+PDh2Lp1q1Tm+PHjGDduHBo3bowGDRpgxIgR2L9/v1Rm9+7dGDJkCOrVq4dmzZrhvvvuQ3FxcVWGQtRhfvzxR1x33XVo3LgxkpOT0bVrV3z++efucc45pk6dihYtWiA5ORkDBgzAtm3bpDpyc3MxatQopKSkIDU1FWPGjEF+fn5Vh0LUMVasWIGhQ4ciIyMDjDEsWLDAV6ai+ufXX3+N/v37IykpCa1atcKMGTMqM7RKg4SeCqagoACPPvponfkSXpfioVhqJhRLzYRiqZnUpVgI4kRi+fLlGDduHD777DMsXrwYRUVFGDhwII4cOeKWufvuu/Huu+/izTffxPLly7F3715cccUV7vGSkhIMGTIEhYWFWLVqFf75z39izpw5mDp1anWERNQxfv31V5xzzjmIj4/HBx98gE2bNuGvf/0rTjrpJLfMjBkzMGvWLLz44otYvXo16tevj0GDBuH48eNumVGjRmHjxo1YvHgxFi5ciBUrVmDs2LHVERJRhzhy5Ai6deuG5557LrBMRfTPvLw8DBw4EG3atMG6devw5JNP4pFHHsFLL71UqfFVCpyoUA4dOsQB8EOHDlW3KxVCXYqHYqmZUCw1E4qlZlKXYiGIsnDs2DG+adMmfuzYsep2pVwcOHCAA+DLly/nnHN+8OBBHh8fz9988023zObNmzkAnp2dzTnn/P333+eGYfCcnBy3zAsvvMBTUlJ4QUGB1k5BQQEfN24cT09P54mJibx169b88ccfr8TIiNrKpEmTeL9+/QKPm6bJ09PT+ZNPPunuO3jwIE9MTOSvvfYa55zzTZs2cQB87dq1bpkPPviAM8b4jz/+GFjvH/7wB96qVSuekJDAW7Rowe+4444KioqoiwDg8+fPl/ZVVP98/vnn+UknnSR9pk6aNIl36NAh0J/c3Fx+7bXX8iZNmvCkpCTerl07/sorr2jLVuXvMFpenSAIgiAIgqi1cM5x9OjRKrdbr169Mk+seejQIQBAWloaAGDdunUoKirCgAED3DIdO3ZE69atkZ2djd69eyM7Oxtdu3ZF8+bN3TKDBg3Cbbfdho0bN6J79+4+O7NmzcI777yDN954A61bt8aePXuwZ8+eMvlMlA3OOYqPFVaL7bjkhJj76DvvvINBgwbht7/9LZYvX46WLVvi9ttvx8033wwA2LFjB3JycqQ+2qhRI/Tq1QvZ2dkYOXIksrOzkZqairPOOsstM2DAABiGgdWrV+Pyyy/32X3rrbfw9NNP4/XXX0eXLl2Qk5ODr776qpyRE7HCOQdKqv7zEwAQKftnqEpF9c/s7Gyce+657upYgPU5+8QTT+DXX3+VMtwcpkyZgk2bNuGDDz5AkyZNsH37dhw7dqxC4ioPJPSUk+PHj6Ow0PvwzsvLk37WdupSPBRLzYRiqZlQLDWTyo4lISEBSUlJlVI3QVQWR48eRYMGqVVuNz//IOrXr1/q80zTxIQJE3DOOefg9NNPBwDk5OQgISEBqampUtnmzZsjJyfHLSOKPM5x55iO3bt3o3379ujXrx8YY2jTpk2p/SXKR/GxQvy9+13VYvuWL59BfL3EmMp+//33eOGFFzBx4kQ8+OCDWLt2Le68804kJCRg9OjRbh/T9UGxjzZr1kw6HhcXh7S0tNA+mp6ejgEDBiA+Ph6tW7fG2WefXdpQibJSchTmG82il6sEjKsOAHGl/wzVUVH9MycnB5mZmb46nGM6oWf37t3o3r27KyC1bdu2/AFVACT0lIPjx48juUEqUOKfK6FVq1ZV71AlUpfioVhqJhRLzYRiqZlUVizp6enYsWMHiT0EUYmMGzcOGzZswCeffFLptm688UZcdNFF6NChAwYPHoxLL70UAwcOrHS7RO3DNE2cddZZePzxxwEA3bt3x4YNG/Diiy9i9OjRlWb3t7/9LWbOnIlTTjkFgwcPxiWXXIKhQ4ciLo6GqUTt4LbbbsOIESPwxRdfYODAgRg+fDj69u1b3W6R0FMeCgsLLZGn9UVAJN7ayQx4c1wzYZ/9001Pc/Yx73joPvGnUM796bx2vPPex9kvI+AArPcRd59XhWfRKmfYZQwmvHbLc3cfE+pw69Ici3WfU7+uXqaWF8poy8e4L+gYwsoH7mPR64VYPnq90BxzXpTGD0ApF1aHcEw9D2Dh5UPrF/3S+8gYC712UGMS21io09dfmNW3pHqlY/Ad0/UTNU5IPnJf+eB+y6PY1NQF+Ri0cWrqdcvojmnuN+FzQOcHwuJU/dfGwrXlwo55Mdu+SfXGUD/jmusjHGPyZ5/10arUD+67Bvo6/Pu05WEG1uH5aPrPtc8zGAdjah2mu8/QHnMD8H4aDHmHS9C6x5coLCwkoYeoVdSrVw/5+QerxW5pGT9+vDsB6Mknn+zuT09PR2FhIQ4ePChl9ezfvx/p6elumTVr1kj1OatyOWVUevTogR07duCDDz7ARx99hKuuugoDBgzAf/7zn1L7TpSNuOQE3PLlM9VmO1ZatGiBzp07S/s6deqEt956C4DXx/bv348WLVq4Zfbv348zzzzTLXPgwAGpjuLiYuTm5gb20VatWmHr1q346KOPsHjxYtx+++148sknsXz5csTHx8fsP1FGIvWszJpqsl1RVFT/TE9P9612GO1z9uKLL8auXbvw/vvvY/Hixbjwwgsxbtw4/OUvf6mQ2MoKCT0VgREHGILQw0oj9Ijlyyr0GIItxyndYJdr9tlWmCjmlE3oMeQxg89meYUeI2Dg7sZUjn1lKh+4j0WtQyfceHXoRBR/eedFafwAlHKxCj0x+agrr6tf9EvvY2mFHrmNdX1fKFeBQo/vejK5z4fVa70vv9Cjj7MMQo+mHBAs9OgEk0oVeiQ/qkro0Yg00Nfrr6NsQo+hLa8RelhphR7np3fvShfMoEU4idoLY6xMj1BVJZxz3HHHHZg/fz6WLVvmezQgKysL8fHxWLJkCUaMGAEA2Lp1K3bv3o0+ffoAAPr06YNp06bhwIED7uMHixcvRkpKim+ALpKSkoKrr74aV199Na688koMHjwYubm57vxAROXCGIv58anq5JxzzsHWrVulfd9++637uF9mZibS09OxZMkSd+Ccl5eH1atX47bbbgNg9dGDBw9i3bp1yMrKAgAsXboUpmmiV69egbaTk5MxdOhQDB06FOPGjUPHjh3xzTffoEePHpUQKSHCGKuwx6eqk4rqn3369MFDDz2EoqIiV2hcvHgxOnTooH1sy6Fp06YYPXo0Ro8ejf79++O+++4joYcgCIIgCIIg6jLjxo3DvHnz8N///hcNGzZ054No1KgRkpOT0ahRI4wZMwYTJ05EWloaUlJScMcdd6BPnz7o3bs3AGDgwIHo3Lkzrr/+esyYMQM5OTl4+OGHMW7cOCQm6oWEp556Ci1atED37t1hGAbefPNNpKen++YCIoi7774bffv2xeOPP46rrroKa9aswUsvveQuK80Yw4QJE/CnP/0J7du3R2ZmJqZMmYKMjAwMHz4cgJUBNHjwYNx888148cUXUVRUhPHjx2PkyJHIyMjQ2p0zZw5KSkrQq1cv1KtXD//617+QnJxM80kREvn5+di+fbv7fseOHVi/fj3S0tLQunXrCuuf1157LR599FGMGTMGkyZNwoYNG/DMM8/g6aefDvRt6tSpyMrKQpcuXVBQUICFCxeiU6dOldoesUBCD0EQBEEQBEFUIi+88AIA4LzzzpP2z549GzfeeCMA4Omnn4ZhGBgxYgQKCgowaNAgPP/8827ZSCSChQsX4rbbbkOfPn1Qv359jB49Go899lig3YYNG2LGjBnYtm0bIpEIevbsiffffx8GZfERCj179sT8+fMxefJkPPbYY8jMzMTMmTMxatQot8z999+PI0eOYOzYsTh48CD69euHRYsWSY/7zp07F+PHj8eFF17o9udZs2YF2k1NTcWf//xnTJw4ESUlJejatSveffddNG7cuFLjJWoXn3/+Oc4//3z3/cSJEwEAo0ePxpw5cwBUTP9s1KgRPvzwQ4wbNw5ZWVlo0qQJpk6dirFjxwb6lpCQgMmTJ2Pnzp1ITk5G//798frrr1dwC5Qexjnn1e1EbSUvLw+NGjUC2l4MROxnYGvgo1uxzNFDj26VoXzgPha1Dnp0y3mv95Ee3fKOB8dJj27Ro1tleXQraI4eA3mHi5Ha/nMcOnQIKSkpIIiayPHjx7Fjxw5kZmbSXFIEQRBEraIqf4eRnE8QBEEQBEEQBEEQBFFHIKGHIAiCIAiCIAiCIAiijkBCD0EQBEEQBEEQBEEQRB2BhB6CIAiCIAiCIAiCIIg6Agk9BEEQBEEQBEEQBEEQdQQSegiCIAiCIAiCIAiCIOoIJPQQBEEQBEEQBEEQBEHUEUjoIQiCIAiCIAiCIAiCqCOQ0EMQBEEQBEEQBEEQBFFHIKGHIAiCIAiCIAiCIAiijkBCD0EQBEEQBEFUEX/+85/BGMOECROk/cePH8e4cePQuHFjNGjQACNGjMD+/fulMrt378aQIUNQr149NGvWDPfddx+Ki4ur0HuirlJSUoIpU6YgMzMTycnJOPXUU/HHP/4RnHO3DOccU6dORYsWLZCcnIwBAwZg27ZtUj25ubkYNWoUUlJSkJqaijFjxiA/P7+qwyGIEx4SegiCIAiCIAiiCli7di3+/ve/44wzzvAdu/vuu/Huu+/izTffxPLly7F3715cccUV7vGSkhIMGTIEhYWFWLVqFf75z39izpw5mDp1alWGQNRRnnjiCbzwwgv429/+hs2bN+OJJ57AjBkz8Oyzz7plZsyYgVmzZuHFF1/E6tWrUb9+fQwaNAjHjx93y4waNQobN27E4sWLsXDhQqxYsQJjx46tjpAI4oSGhB6CIAiCIAiCqGTy8/MxatQo/N///R9OOukk6dihQ4fw8ssv46mnnsIFF1yArKwszJ49G6tWrcJnn30GAPjwww+xadMm/Otf/8KZZ56Jiy++GH/84x/x3HPPobCwUGuzsLAQ48ePR4sWLZCUlIQ2bdpg+vTplR4rUftYtWoVLrvsMgwZMgRt27bFlVdeiYEDB2LNmjUArGyemTNn4uGHH8Zll12GM844A6+++ir27t2LBQsWAAA2b96MRYsW4R//+Ad69eqFfv364dlnn8Xrr7+OvXv3au1yzvHII4+gdevWSExMREZGBu68886qCpsg6iwk9BAEQRAEQRC1Fs45jh0pqPJNfKQlFsaNG4chQ4ZgwIABvmPr1q1DUVGRdKxjx45o3bo1srOzAQDZ2dno2rUrmjdv7pYZNGgQ8vLysHHjRq3NWbNm4Z133sEbb7yBrVu3Yu7cuWjbtm2p/CbKB+cc5vFj1bKVpo/27dsXS5YswbfffgsA+Oqrr/DJJ5/g4osvBgDs2LEDOTk5Uh9t1KgRevXqJfXR1NRUnHXWWW6ZAQMGwDAMrF69Wmv3rbfewtNPP42///3v2LZtGxYsWICuXbuWup0JgpCJq24HCIIgCIIgCKKsHD9aiEubTahyuwsPzERy/cSYyr7++uv44osvsHbtWu3xnJwcJCQkIDU1VdrfvHlz5OTkuGVEkcc57hzTsXv3brRv3x79+vUDYwxt2rSJyV+i4uAFx7HzWr+4VxW0nfcRWFJyTGUfeOAB5OXloWPHjohEIigpKcG0adMwatQoAF4f0/VBsY82a9ZMOh4XF4e0tLTQPpqeno4BAwYgPj4erVu3xtlnn12qOAmC8ENCT0VgFgOMWa+ZAS9RStxn/3TKOWUY846H7hN/CuXcn85rxynrBWfWBgAcXLPPDsE9T9jr7BOqd2tn3GeSC+5w5Vhp9jn1q/tMJhyH/zx/HbHvCzqGsPKB+1j0eiGWj14vNMecF6XxA1DKhdUhHFPPA1h4+dD6Rb/0PjLGQq8d1JjENhbq9PUXZvddBB2D75iun6hxQvKR+8oH91sexaamLsjHoI1TU69bRnfMf785cRi6NmMAwuJU/dfGwrXlwo55Mdu+SfXGUD/jmusjHGNezG4ZtX5w3zXQ1+Hfpy0PM7AOz0fTf659nsE4GFPrMN19hvaYG4D30zCRd7gEBEFUPHv27MFdd92FxYsXIykpqUpt33jjjbjooovQoUMHDB48GJdeeikGDhxYpT4QtYM33ngDc+fOxbx589ClSxesX78eEyZMQEZGBkaPHl1pdn/7299i5syZOOWUUzB48GBccsklGDp0KOLiaJhKEOWB7qBykJCQgPT0dOTsXlzdroRSrPwsqC5HCIIgiBpNeno6EhISqtsNgigVSfUSsPDAzGqxGwvr1q3DgQMH0KNHD3dfSUkJVqxYgb/97W8oKChAeno6CgsLcfDgQSmrZ//+/UhPTwdg3Z/OfCniceeYjh49emDHjh344IMP8NFHH+Gqq67CgAED8J///Kc0oRLlgCUmoe28j6rNdqzcd999eOCBBzBy5EgAQNeuXbFr1y5Mnz4do0ePdvvY/v370aJFC/e8/fv348wzzwRg9cMDBw5I9RYXFyM3Nzewj7Zq1Qpbt27FRx99hMWLF+P222/Hk08+ieXLlyM+Pr404RIEIUBCTzlISkrCjh07AifAIwiCIIjaREJCQpVnHBBEeWGMxfwIVXVw4YUX4ptvvpH23XTTTejYsSMmTZqESCSCrKwsxMfHY8mSJRgxYgQAYOvWrdi9ezf69OkDAOjTpw+mTZuGAwcOuI/HLF68GCkpKejcuXOg/ZSUFFx99dW4+uqrceWVV2Lw4MHIzc1FWlpaJUVMiDDGYn58qjo5evQoDEOevjUSicA0rczQzMxMpKenY8mSJa6wk5eXh9WrV+O2224DYPXRgwcPYt26dcjKygIALF26FKZpolevXoG2k5OTMXToUAwdOhTjxo1Dx44d8c0330jiKEEQpYOEnnKSlJREX4oJgiAIgiAILQ0bNsTpp58u7atfvz4aN27s7m/UqBHGjBmDiRMnIi0tDSkpKbjjjjvQp08f9O7dGwAwcOBAdO7cGddffz1mzJiBnJwcPPzwwxg3bhwSE/VC11NPPYUWLVqge/fuMAwDb775JtLT031zARHE0KFDMW3aNLRu3RpdunTBl19+iaeeegq/+93vAFiC1YQJE/CnP/0J7du3R2ZmJqZMmYKMjAwMHz4cANCpUycMHjwYN998M1588UUUFRVh/PjxGDlyJDIyMrR258yZg5KSEvTq1Qv16tXDv/71LyQnJ9N8UgRRTkjoIQiCIAiCIIhq5umnn4ZhGBgxYgQKCgowaNAgPP/88+7xSCSChQsX4rbbbkOfPn1Qv359jB49Go899lhgnQ0bNsSMGTOwbds2RCIR9OzZE++//74vc4Mgnn32WUyZMgW33347Dhw4gIyMDNxyyy2YOnWqW+b+++/HkSNHMHbsWBw8eBD9+vXDokWLpD96z507F+PHj8eFF17o9udZs2YF2k1NTcWf//xnTJw4ESUlJejatSveffddNG7cuFLjJYi6DuOlXRuSIAiCIAiCIKqB48ePY8eOHcjMzKSMaoIgCKJWUZW/w0jOJwiCIAiCIAiCIAiCqCOQ0EMQBEEQBEEQBEEQBFFHIKGHIAiCIAiCIAiCIAiijkBCD0EQBEEQBEEQBEEQRB2BhB6CIAiCIAiCIAiCIIg6Agk9BEEQBEEQRK2CFo0lCIIgahtV+buLhB6CIAiCIAiiVhAfHw8AOHr0aDV7QhAEQRClo7CwEAAQiUQq3VZcpVsgCIIgCIIgiAogEokgNTUVBw4cAADUq1cPjLFq9oogCIIgwjFNEz/99BPq1auHuLjKl2FI6CEIgiAIgiBqDenp6QDgij0EQRAEURswDAOtW7eukj9QME4PORMEQRAEQRC1jJKSEhQVFVW3GwRBEAQREwkJCTCMqpk9h4QegiAIgiAIgiAIgiCIOgJNxkwQBEEQBEEQBEEQBFFHIKGHIAiCIAiCIAiCIAiijkBCD0EQBEEQBEEQBEEQRB2BhB6CIAiCIAiCIAiCIIg6Agk9BEEQBEEQBEEQBEEQdQQSegiCIAiCIAiCIAiCIOoIJPQQBEEQBEEQBEEQBEHUEUjoIQiCIAiCIAiCIAiCqCOQ0EMQBEEQBEEQBEEQBFFHqJFCz4oVKzB06FBkZGSAMYYFCxa4x4qKijBp0iR07doV9evXR0ZGBm644Qbs3btXqiM3NxejRo1CSkoKUlNTMWbMGOTn50tlvv76a/Tv3x9JSUlo1aoVZsyYURXhEQRBEARBEARBEARBVAo1Uug5cuQIunXrhueee8537OjRo/jiiy8wZcoUfPHFF3j77bexdetWDBs2TCo3atQobNy4EYsXL8bChQuxYsUKjB071j2el5eHgQMHok2bNli3bh2efPJJPPLII3jppZcqPT6CIAiCIAiCIAiCIIjKgHHOeXU7EQZjDPPnz8fw4cMDy6xduxZnn302du3ahdatW2Pz5s3o3Lkz1q5di7POOgsAsGjRIlxyySX44YcfkJGRgRdeeAEPPfQQcnJykJCQAAB44IEHsGDBAmzZsqUqQiMIgiAIgiAIgiAIgqhQamRGT2k5dOgQGGNITU0FAGRnZyM1NdUVeQBgwIABMAwDq1evdsuce+65rsgDAIMGDcLWrVvx66+/Vqn/BEEQBEEQBEEQBEEQFUFcdTtQXo4fP45JkybhmmuuQUpKCgAgJycHzZo1k8rFxcUhLS0NOTk5bpnMzEypTPPmzd1jJ510ks9WQUEBCgoK3PemaSI3NxeNGzcGY6xC4yIIgiCIyoZzjsOHDyMjIwOGUSf+9kPUcUzTxN69e9GwYUP67kUQBEHUKqrye1etFnqKiopw1VVXgXOOF154odLtTZ8+HY8++mil2yEIgiCIqmTPnj04+eSTq9sNgojK3r170apVq+p2gyAIgiDKTFV876q1Qo8j8uzatQtLly51s3kAID09HQcOHJDKFxcXIzc3F+np6W6Z/fv3S2Wc904ZlcmTJ2PixInu+0OHDqF169bYs2ePZJ8gCIIgagN5eXlo1aoVGjZsWN2uEERMOH2VvnsRBEEQtY2q/N5VK4UeR+TZtm0bPv74YzRu3Fg63qdPHxw8eBDr1q1DVlYWAGDp0qUwTRO9evVyyzz00EMoKipCfHw8AGDx4sXo0KGD9rEtAEhMTERiYqJvf0pKCn3ZIAiCIGot9AgMUVtw+ip99yIIgiBqK1XxvatGPpCfn5+P9evXY/369QCAHTt2YP369di9ezeKiopw5ZVX4vPPP8fcuXNRUlKCnJwc5OTkoLCwEADQqVMnDB48GDfffDPWrFmDTz/9FOPHj8fIkSORkZEBALj22muRkJCAMWPGYOPGjfj3v/+NZ555RsrYIQiCIAiCIAiCIAjixONY/mEse2gMPpkwGMseGoNj+Yer26WYqZHLqy9btgznn3++b//o0aPxyCOP+CZRdvj4449x3nnnAQByc3Mxfvx4vPvuuzAMAyNGjMCsWbPQoEEDt/zXX3+NcePGYe3atWjSpAnuuOMOTJo0KWY/8/Ly0KhRIxw6dIj+qkQQBEHUOuj3GFHboD5LEARBVAUrJg5Dz44rkdCg0N1XmJ+AtVv649yn3ilTnVX5O6xGCj21BfqyQRAEQdRm6PcYUdugPksQBEFUNismDkPfrCXI3ZOGzYWXo/2Im7Htrf9Dp4T5SGuVi1XrLiyT2FOVv8Nq5Rw9NY0nX3obScn1wMAA+3E7xgBw+z3jYJyBw3oej4EDjIFzWPsg/HR1Nw5wBg7uvLN+mhyFxcVISIi37Nl1ggPMfRDPssu47QjnYAxgMGC6NQqVWoZdT0xun2Za+4pLSmACiI+Ls/0X4nT/sV6IsQAAMwyrPmc/Y1a9zg41Pm5tRUXFMOLiEBeJgIPDYIZ1wIAXt9vEzHWfccCIGCgxQ/RLLtuD3e6FxcVISoi32tOuy31+0m1PwGAMnHMrFrutDHD3OnjtKdsQHeCcg8GaJDwpIc6+fgzMqdDpJ060wmOcznvnctuXWCwCDqEvCdcWAErMYiTGRexuIsTn/vRqEkMCBwxDuI5qYBxweyyH9cp2zjRLkBAXEepj3mvmXU/RFw7AsF9ZZawzObjQfQKus20zPj5ix+HcK54lL14ILW3bYs71tKwxKNeTyW/cu8osQVx8nHv/M2b1UYOLBrl3TGgLqfs4P8UX3iUUfOFgMBEXFxGe9WVSbE7g4ucOs9uEGU4fES5iIJYzpmkiPmLCiESUzwIm23S9sf7hAAzmXAO7PrXzcuU8WP3INEuQGM9hGBGxdax+CAbGuNBvnVa16meMw2Biyzr1i7EK19kuxnkREhMMNyYGBs64r59KDttmIoZpX1duh6fcoPaHkHfNuV1FIeIThH7q2hM/h8S6nP4ERCIlQjtwyR+AC/HKP4uPNwJBEARBEARhcSz/MHp2XIncPWlIHPM5Uh4fgvz9Q/GbqbNQeOzPyH2mA3p2+ATH8g8juUHNXcyChJ4K4E/PvQEY8WU4Ux0SxYhRjqmVXDWolLaNsnQVZtljpfHX8csAjEgpyou7IjGeq9TBHJtcPeIrre5PYECcNIgNP98hOcJ954Sd6+5nQLzBEWHhZdVzACApLngw76tH2ZEY548xqq8AEkJujzCbDEBiwLnhNjkS4sNbRRXQ3P0MSIhj+jJRrm9iQnBfZ74XHhEDiI8z7MMBRph+V0KCvq8HxmdvRpwXZ5iP6j6DAYnxftVJZ48px+IiHAlxghMBfqs24yIm4hWbqj3deYxxJMSVIF7p80wn0rgvrbLJicUwDM+mbM/aZyiGnb6XlFCI+DhTEFkUvxRfnPoa1DsuHWVMvVc5DEONxXpfr95RRCIlbjldW7ixCA4UHj0OgiAIgiAIwmL19Ano36UQm7aehW7/70yc0TMPv6y5CcX9dyAhuR42FwxHv4avYOX0CThv2svV7W4gJPRUK2F/PQ8ZpHKzlOKJeG5IvWHjYhNlm7qbO4OLWNUMp01KAG7oVQX9ibJNbkI7qtKf4P4w7BySICtB41PDSj+Rrqg4LNWM1QEAxSWWeKLaCMOtS2jWaD1JSJ5CSQkQp9EG3HrDmrYEVj8QBqfiK/VUp7uZJUAkoq84WryBSTtRbALhwlvUye6Zpl25XI/fNW5nvWmqixqoY1O5XzTxy7GbVsab0t2DhB5nN+P6TDCNAelymwBYPJf1ZlU40ZlmcDOcguKS7hnhTYnJkWiU+MUc5nspx8gAA6bdtoJBbZ+SRdeiYqBeUoG7TycsuQ1pY9j1GIYJbtv1/OVCcGJH8oSkoiIgOem4Is4INhlgXQHvvVOGsSJwbmp8FTP77P3cOcf5rCQIgiAIgiAAIO7IjwCAvqcvQSSxBGaRgW05nXCOvVJ3u8vHABteccvVVEjoqZFEVSbKQYDoEpMYUsF+xaRoBEkYQcP8shb1RpXSkznKqTpvnAElU5ooFsGIwxm2eYO9MHed47GKQbr9JtfHoY5B1RMZ4D6NGC4t+Svl3LKr8y803pAGCWwD7sWpapOSEMf14ktYwlyQYOcKbu6jXP5y7jhbU7+hFhZqiCbYWI+XwXdJOIT4lH7JYcUZ2EeF83Qxc8haarRO697KAdqrT0RR7yPOreulnhMgdjqPRHG7ERgzJcNiH2bKfjfbx3kMSvlAkMpLz/EJbc64J8Ao6qBlQ85Mcs4xAbuDCNdfuiE5mNSrufv4qFXWOUtuLAbbH1dA8s7hsd7LBEEQBEEQdZyfd+5Ej45rAACRxBIc/aU+NiU+hHNm3uWW2T7/ZaS3B4rrt6wuN2OChJ4aSSlEjDLXz/y7Qk2Wx5eAc0NtMmWkWkZfShWP4TsSJtaIcE2TSsclC3Kdfqvh9tzBfRR7QcJJWGaJI0CFwX3KQ/hA0bGprZtFsRdSdbTuYwTE6rgfZDdsenr3kK5eBjDGYASJMjH0D13ljoijjcVWCVTxyG3rEIGIl/JjxulTbvKQTigKaW+3Evj7pyhUaeM0mNvfHeGE2/VKGTdQXkuBaj2D2vqcW308zmlFUWhhcsaRMFuU0L/tChyRTikjmXSzAC1RKSJNiuX4I5zPnIwvIW6lUtGm1/ZyrNxWwZhfYSQIgiAIgjghWf3SM+hcMA2JjQsAAMUFERy7eBV6tWvnlik8dhSdEheg8HAiek2eWU2exgYJPSccZf1SX9qMHnUYV1q7zl+fg3JPYhSAeNBhXb0lAORnmsSxsG5cLFUZJUSxDrF+UzgulogWpa4+pvxUkebB1sChf0JPX1+YPCGU4iF2uUYA8g1ggwm1aeqzcxxRQSfIRBPCXJuaBrQmEucwA7xyM4iUw775YjSdNsgn0x6w656+4YBPjJFsasQXz6LGT6e48ySUCb/YaHda3d3vxB4kzEkmBWc4AJimL9PMzfZy/A9qVzFQJaUrzBfvsVPpFK8MF4UUtYD603rt3dNc0pA8k6YsLEGdw4vJbSRNMs2E10IR0Q9BMHQnj6eFNwmCIAiCOMH55M4L0CtrLYwGJkoKDBzYk4b0U38G/nM+lhcMR7vLx2D7/JfRKXGBt+pWDZ6IGSChp4ZT2Zk9pTFZBqEmVFKIZtP5y3aY3XIoAVKB4EJhwkm0qnVZCwyyRQZVWGGaV+JRYRDHg6dN0olETllDM9gOFK/UerkwtvTnZGj8tf5xbGoFnUBjpe91YncxNFM8sYCyktnSjHsVYYgxphFuBNu6A04CiLZdg4Ua8Y0oIEWLUdIxlJ2h7S0IcAzQZhHpKnD2OwKbVpzkQhnBd0+UMNwVr9wMGKXT6uK2dAzfrEfean1OFoxQgAmVyGt5CR1SCNZdMUt6HEu6oKJRIVjuJP5IcQqpS/Y1EuvlVjsI10JqNXVFLjt+g0k1SG3EglLQCIIgCIIg6jgb3puPU/aMQZ/eVhZPXk4K9rR9CmfcdA1WTByGnh1Xol+DV4ANryC9PVB4OLHMS6tXNST01Hgq+kt4DOKR9lBpsnKiyAYxVROW0aPzKWjUHs0m9xWKNs4Pqoorg8XAcoINA/bktr4SwcbdiDUqT6hIE1K9LnMJ8Ge9+DNe/JX5fOByzNKpuu5Rii7vG8ALRnQZPWJ5b24TVTgphU3u2WSAb44eqUqlcWWBSKlUcMQZ64vlVZuiHiDqRU6fdPuMIKZI11YnqilahbvCO/c2EfVaSHqDqHKqZYLEOFEU4iaYMr+6vMy9fLL4qcCYEgjz51xxpS0sA6ZUt7x8O9eeK4lCkmplBt43wmJY4Ny09W2/eGSdxew5nYXzRbckI16cXl/nXl+yLyA3lQtJEARBEARxAvDJkw8hK/U5JKQUgXNg16aWyJi8DmfYmTrnPvUOjuUfxsrpExB35EcU12+JXpNn1vhMHgcSemo8NSWrp6z2mbdFrUItEPDcSdTzym5TV1MsFt0mEwfGAeeL410GXVZOwKpN4uBRkzAQZlNaIInJ9oMI8l3OMNCfo4o1BjxhIdY2DToQs00loyc4HsSUeKa1rYgXTkZPmJ86R0wTwlODmj6pikKSD15GT6BepRMjNEKN75ZRhDApBlVUiUW004lDITeKlOkizsbM/MuHiy9F8YXbwoxrhjNXTPF94ojx2jYl9xRnvYmWnTwjMU4uTF7OJGFR9FfKxrILcC5Owi02mjypslVWDp672U5M0H68Qu5KXbYtyughCIIgCOJEorioCF/e9xv0yvoaRoSjpNDAV192Qc9nPvOVTW7QsEYvoR4GCT21gor+Ih5Dfb4isWb0qBJCaf5arJQttchTlnaS/uyuya6JrWZnTtNYWsn5A76j1ehscnBfZO5QLUSlCbItzgMk5iOoml6gthdgkytFfFlDdnmT+XuErqfEklkTs00lo0d7bRj8YkTIBVR9d6Zx8d57Vnx6qRCoTpTyW3HeMekcUaBhkDN6xD7iCDcApBXGxIwesU18moLTuMyz6fZbMQvFjT1A7BFeaB+l0/Qpw4lHyK6Rspa4IJII2U2uNuzsY5BkU08E8QszTIyT28uy25NLifeMtaKVdX+aUERUwQ/5A0EWm6SVrpw+xABTSJXisLJt1EcDTc6lVdrcLDvpBvLucDcTy63E7ijcEo0oo4cgCIIgiBOFz/7+DLqxR9Dj7EIAwC+705DX55/oeeMF1exZxUNCT60gdDheNSZjtilKGEDwLDJBSH+Kj8GsWKis2U9eed2qWMGlPaSEjADvhD/cS/Xo9/ulH+a9jDlEtd4gm1p0TesOJoOzegzNAYNHt+mOh2PQCIPikmwyZ6AfhTKOc3VzxESdo0fx0SEso0e05XvcC95AP2w+IvGNKFKJZdymF8sGrKwV5o/PpHhL624U4Zq74o1Ypy3WuL7ZIotOJHKqk2zaqhSTS0iIQptbr2EAhneet2Q5c99bb736HEHIilO2Iy7V7sYp9BVn1TYw5tbLBOHGW65djsOZBFyKzRW6GJwsJCZk+jgZPQyMMnoIgiAIgjghWDb1VpzT7l8w4qw/hK3//AycOWMFmsXHV7drlQIJPTWG0nzZLsVIvyLMxWQzaGhb2lG0kq4QSqwyTKw2gzN6HAzIEcUinKjSF5T3agvJcpU30PTJWJpLou7S1W1wb0AKBLRiQNN6+70I1Hrc7CHBGSejpyw2dYj1SLGINrkXa1BVulW5Yu1C4jw5TjaNOkePTmyJ7of/wjrZMmI2jzRnjuCPakcVT1x3mGxJ6lf2DmeFLbG8k2nDmSwWiXPmONlCgHxNmCqoKDhxMHUn41C1DLUSpw2Ze44nvsH21xKJ/HeIMx2PGCNgWtkuvobxXjOxg8FrZ0NKs5EsWQk1jPvisAQb0xNtnJvVOZ9bAg1TlFTmNLC6LBm8WBx9ys0ksjOHOACTMnoIgiAIgqjDHMs/jG1/OBf9enxrZVCXMHy2ri/6z/ywul2rVEjoqTGU5st2abNkBHTrO8d0Xqzz5UCpX1IHSnduVD1LN5xWTopZE7MKhWX0+AbEitWA8ackAmkH+QHn6T2MjhOyWl7KHIhWX0DTyo/n+IW8oHoZj6HXBl3OEELblemXUNfaLAVujBrhhjEWaJOphQViyegJOp8xT0DR9dcgAjN67JPVrB2xrNS2unYIcELM6JEut6NVMLU9nZ9OForXDkHtIWb0WEIYd40wSZ0TbDAldgAwDBiu0GILRFI5f11OG5rcsqW2g1OH2HZyuxowhA7kPGYmfYRKHzQcnHNbBGTuymTevamu0MXk1wwwtGonQRAEQRBE7Wf59AfQo8HL6JJ1FACwd1tzmJe+hv7X96pmzyofEnpqFKUdyleAjZirUWejKFMlpSseWi7KkLbUzWO1abSMniBPdEuWizXrrhgTjkPz2pBKaexq/oKv2tVh8hCfNIP20tpUSzBY2RSB8x+VQ90KS/IwbWdCbZbhNuLKC7EdOecwETCZdsh4OtpY25urRqnTPsbtxlXbwQhR9AIzegA3Q8m17Ry3C5nihVaOi9k84PL11S7JrtiU2tOJEQY4L5HKuhUogp5PvDLgqlpcEGvcujl3p61xYrCqN61sFwaI66g5mTmGRuR0XogTgfvi4aL45By33pvcdDNspLmEbDFHmrjZK+SW9+zYgo8w8bRk0xWhOLhpgiAIgiAIoq6x+s5zcE6v9VYWTxHD2i97oe/MJdXtVpVBQk+NItb0gvL8BVaT8RILoaNx3dC+FMXDymmrUndq7HPm2xW9HsBQ9oXpAeI+E9xXhqHsc/7EUiba5LdBvouilO90VbxQCnCuy1jx2j9IfpMWTAo+3V/AnzThIzTOKN1AXXkKiNLVVZtq/SFz9IQ9Q2YGTfIUk02NACCaCbiPTG7/AhAEBwfx6SAWYFPtB77+orGpztEjCU2C4766neXJJZFDFmZEMUU0z00Iv+m4m4hjQBGefPUbMAxvGnMpPkc0EqVN97ozmKYnvIgCkJUYKTw+xqTawRCBYXiClif22LKM8ywWPDHHEV85t1f54rDn5rHjZfamyGeuqEsZPQRBEARB1CEO7d+H/TMvwFm9dwMAzGKGTzYNx/kz/1XNnlUtJPTUOGIZ9pcno6cM5spcAdMfj9VmjOJMYIVliE3NOuEInpdHSSrQmlX/Vq5rEZ3uFTr0Ugb0KmKiBYN/4CtOySHFFVavcMwZZkZDylQK0BZjsRmbNX8Zk/vmw7WzG/z2g+xGs+VbotzkYAbz+RL6xKRG2IjVJrPfc+FCO9c9WtaSu+qWLktItStmRtkF1KldwrLa3FNDCohxmUqnNcCELBTB74AbRbofGeAqIGDe41VMjN1K6REXy+IGtwUb+YaxVl03PTtOH3Z9tiZRdn0V7x3TfQWnUa3MKueuKhEmVnYidcQ8rthkSl+2JlyGUx8XrqUbv1Oft+4XZfQQBEEQBFFX+HjK7chq+iZO7Wo9qrV7cwuwy17H+TecVc2eVT0k9NQ4woa0FSTuxGouJpO6ClQppIw2tXpWLEP+GEacAQRl4IRVxyEPUp2XTl1BIlCU5AefDekN0zeP7r1qJ2iw7VuGWaxQCMovY/hR2y3oia9YbMZyKXVtGRZnedD1CXEuGfWYZTSk53KAR0uq0Ahi0qpbuuKq0ueZA2Bn9CgnhrWjJMppxKlYblVt2yux6TN3uNSZS/OxxCU1y+tUUtaSEjiDJegYwjrmnsDCwaVUIA4Gb+l0cGZNquxU55h0pjkT59GRXgPgBpx5fKxsHO9m4K5ixAF3Xh7Pd85Nd5Jn9xoZcJQuIQqnXltooowegiAIgiBqEcfyD2P19AmIO/Ijiuu3RK/JM5HcoCG2P9gB/bv8AMaAkoIIPtswAOf+9e3qdrfaIKGnxlIJok65TOoOlnYIXobTYzpBsz/kMZkw1IwenWSlFVM0g95oegKH9xSLWn9QzlY0cSgWm9J8JE5dpRhA88C0DP8KYeLPstqMRRf050BYQkbQ5L7SycrLWPzx2XSyabg1iHa7n6b+6Aqf5urbWTviEc+mnDElzg2j0zzFOHUTMrvHpBfeT8eWmNFjsOA+6zOqohOi7GvH3R2WI1I2k3DcE2E8H923hlefu5fL8XlxCw9kmdaKVHLWDHfP966nPyXKy+jhrm/O6mjy/S00MOdAxHSzccRsHve1aERuMYhL0FvCGBfmqeLg4FJdbuyU0UMQBEEQRC1hxcRh6NlxJfp3KXT3Fc57C7/k1UPm6YcAACWFEWxMfhbn/nV0dblZI6iRf8pbsWIFhg4dioyMDDDGsGDBAuk45xxTp05FixYtkJycjAEDBmDbtm1SmdzcXIwaNQopKSlITU3FmDFjkJ+fL5X5+uuv0b9/fyQlJaFVq1aYMWNGZYdWCnjIVhF1l8Jk4Dmx+BcyiCh1iGEnaE4ug1ZmwEsc0E3fEagTRLksTn1i3eIjYUH1q4Q1EwvYgo5J9VpJAtZEzeKm2mPO+Vyz6f0R4y6TzZDYQm1qRA6u2BINuf4gGK1NBnfOGlG8csfUTv3OS9EHE/BnuWh6AZNtO+KiY1NqD2dwb29qg7q2nfaxT2TCBrVeJsepxh3gtYfQBmrbOUa4xg83RsMTPZjGN9W4FLcwUTUXG4bZ/ZZxGPYmtYEBGAaDtZIas+fz8ZzkdkEmXhn7Api2sKLep1ZJ5tbv2jMAZjA4GT2uTbGBhLo88cmr3YmPc0cQs63Z9TC7szBmKO1Xhg9KgiAIgiCIKmbFxGHom7UEh3Mb4JPtY7C/22dY9dnZiEssQWrGIXAO/LQzDYcv/AbdR57YIg9QQ4WeI0eOoFu3bnjuuee0x2fMmIFZs2bhxRdfxOrVq1G/fn0MGjQIx48fd8uMGjUKGzduxOLFi7Fw4UKsWLECY8eOdY/n5eVh4MCBaNOmDdatW4cnn3wSjzzyCF566aVKjy+YsOFsrFJArHZKYVI9J+rJIpouVqYQY22bWEadwZjQCym6fZIJxR5XdjvjTXHsaSrHoxGtmcKkLykuQXRQB/KucKAOph17HNIANkx4CipTaptRYguyKQoLqnjgEzREYSHkGmhtqoKVU0YJRIrbELZY+qpGAPOJZIpNrjakGi/32kkSgHSim/DeyeRxxTKhbXxo2kBtu0AhyhRiNJnPpnidVftciN95Sslyh3kHObOWJOcMpr1Zr20bdkYP5xymqgwKjeTeDcw5ymDAFmxsGcj1kdn3ohAf58x6bXKAmZ5YI31SyK0rtbsbG1yRyIvZ89+xYQlBTLiesXz6EARBEARBVB/H8g+jZ8eVyN2ThtS7tqLXvX8G3h6KPr3WwIg3YZYwFB+PQ6M7N6FxqzbV7W6NoEY+unXxxRfj4osv1h7jnGPmzJl4+OGHcdlllwEAXn31VTRv3hwLFizAyJEjsXnzZixatAhr167FWWdZEy89++yzuOSSS/CXv/wFGRkZmDt3LgoLC/HKK68gISEBXbp0wfr16/HUU09JglDVURploqxij0YeiPU7PnOG6LGcLJbTLMses82ynGSfGKjIhFPWBeTVCXKD6gjSsVjAa8mG5r1TNsym+lrMdHHrVAbLTD1Jes/sl/prEhRfmW1GqTtwvxhnQPcRNACXUvU0TfuIophUr6paOQ5A9V93r2kuBfN+Ml/jKj4IJ7u7DLlO3eOHzgtdPxMfEWPQtIXij/pW6Eo+0VS6T5jX15jqi7b9AGX+ZOEkxSHFljSBtZ1lw9xaWOB5ngnr4nJeosm+sY7LcwNxoX1to2DCe1nSlK414M2141lWPjatyg3XWWfCZq+cQRk9BEEQBEHUcFZPn4D+XQqxOedyNFm6BM023Irmpx4EABzNrYfPd12Ac7svxMrpE3DetJer19kaQo3M6Aljx44dyMnJwYABA9x9jRo1Qq9evZCdnQ0AyM7ORmpqqivyAMCAAQNgGAZWr17tljn33HORkJDglhk0aBC2bt2KX3/9VWu7oKAAeXl50lYupLx9FvsG55zSbuqwRPiTdyw2/QEEbCIhy+JE28p0UtBoNTaCHtuRh1vBg24o5aQsA3szhS3IllR3wBbkX9DmZvVwYXDubPZlNpiSYaOU44JFnY1ovpTFZrQ4A/dzYZBfiturNONeXRYMBJvSbabELcbqUzp0tpTNzTxxZBBdJ3GeEdTc7m77BMRicruPKvu40OiBtyo8W2obSPFo2s/LdBE3Jp2naw8pFmZrHI5NR1S0s3bcAiYDN+2MHljZPO5mGlaWDUy7hYWNO5kxduYN8x7VAgDGIl7QbnqR5ZhbG4eVRWTCy+hx7lJmgkl5eP5r5PkgtgqTRU3brJXRw8FN08pSMp22oIwegiAIgiBqPnFHfgQAGD9vRrv91yGt1UFw03pUK/53u3Da9ZOlckQtFHpycnIAAM2bN5f2N2/e3D2Wk5ODZs2aScfj4uKQlpYmldHVIdpQmT59Oho1auRurVq1Kn9AAPRDlcpGsBeLSqD1LbCwQICcEe00t1As9kIqKmWzymKGvF8c1IYKGJryEF6L4+8gN6UBPfyPlHHoBYlYJDDnPFFwcgbw4kA3aL4c0ZI4Q0kstsNshs3RU5oYVfGEB8Wms81j7y5a4YaFCBgaO/rbSx+5TiRjtorBxI5idxZXNBGEE1G0UYUXXSyGYFQnhInt5XsMy/TvUzUIBs2je44mLYphwmpS4n0kzkPExGaz3zuxevU4G3dPZs4GK9vGYNYk6YZhWtku3JmBytvceW+YfQfYYg5zFSbTrZcLttx5gWxfDNgTJBvcngPIsLN0DHA5UveRLPWuc+bxceKy+qDc8M68QIbBrHmHbPGPGcy3ehpBEARBEERNo6TEGlP27b0KcUnFOJZbD6t/uh/pD+5BUr162D7fyuIprt+yOt2sUdQ6oac6mTx5Mg4dOuRue/bsqcDaVVmgKmD+t9FGzTGdIBKliwWdpvMvqpMBFcXcrCywuDiG1FmMRXoSf4oZParn4nsD2qQMMLFCjZ+iSKQTiNS6ncmLDaZk2ITalCecDdu0NplsM5aMHl2MQZtPQFBsGYbGNou9u7i2JEGBae1qk/fEGH1G/V5I8YmCjaPY2A3rDuIde4JNUbBRhRedQOVm8Cj7HKHCbS+hftGPqBk90Ahwtjgl2uRggKH4yyDNxaO7iRz/OGdKfN7J3N08AbKEwxZ4TOhmtObC/DyceZsr6HDDnz0k2rN9MWHPlWNnFln3mNAAqk0A1lTPpnDneXP4eIKe3QB2JpFrz3TmPIKQRRTzByRRS4hlMQqV48ePY9y4cWjcuDEaNGiAESNGYP/+/VKZ3bt3Y8iQIahXrx6aNWuG++67D8XFxVKZZcuWoUePHkhMTES7du0wZ84c6Xi0xTYIgiAIQuXj+69D356rAFjfZ7aub4OS336Fc+7+AwCg8NhRdEpcgMLDieg1eWY1elqzqHVCT3p6OgD4voDs37/fPZaeno4DBw5Ix4uLi5GbmyuV0dUh2lBJTExESkqKtFUcoiRQVSg2Q5UKnW9hsoZYJgYXdKdqxx9BEookK+hPiYozkBIGV4qUEfSf426sEpQzHgYc0ceryxvGea/Ffe5/AeOzaD64k7Cqmzrg5pqWddUef4OW2iaPbjPaGDTMpi9TR7Fnah8RcmIspU1bVLAmvtVnCrk2oWlXnxWvjaP2KfvxHW/wDntpcPgyetQ41T6pxqMTiNysJbFe0Y7ih5hN5BhUYxCFP0egEsUacO5bRYxxgAk6jPxYk9fmYMzO2FFjsipxM3ocvYwBEfu4m63jOGNn7TgZNVYVzN1ccQdivabnqGTPy+4xGAcz7E8SSSGz5+xhTuYOwGC4GyDaZEJ8HF6DwbJj2BuzsoeMCMAM5n5+EXWHaItR6Lj77rvx7rvv4s0338Ty5cuxd+9eXHHFFe7xkpISDBkyBIWFhVi1ahX++c9/Ys6cOZg6dapbZseOHRgyZAjOP/98rF+/HhMmTMDvf/97/O9//3PLRFtsgyAIgiAcfty0Ed891AHnnjkfRhwHL7H2N0k7jPUvTsePX6/H8kfvwMFnOiCtVS7Wbu2H5AYNq9fpGkSNnIw5jMzMTKSnp2PJkiU488wzAVgraK1evRq33XYbAKBPnz44ePAg1q1bh6ysLADA0qVLYZomevXq5ZZ56KGHUFRUhPj4eADA4sWL0aFDB5x00klVHxiAqv3LaohKUKoD0XyO4bi2CAsYAesKq/s0A5eY6jHsI0xbKrgFog+UnFDCrMbiobc/2KZ4RKqDCYP4gPqjiRyc6yZv5W79ukvmy17R+BRs0C2mJ6gbM3m5c98pIXajTVkSJEBZ9gIehYkSp2ySaY8FXi/mLP0t1+DEqcPWToIrDsGdxNmQTw+yJ7W3bVPtJ9z+R63DrdtgrrDp9H/nOjAml5VeS4FqPRM9cF9xAHG2DMPECZyZcD0YbJlGFuS8ievlSZWlphE+EMTLYDgqmnLjOtk83mNZ3jleH/f8kGw6/3DHN0cM88Qtou4Qy2IUKocOHcLLL7+MefPm4YILLgAAzJ49G506dcJnn32G3r1748MPP8SmTZvw0UcfoXnz5jjzzDPxxz/+EZMmTcIjjzyChIQEvPjii8jMzMRf//pXAECnTp3wySef4Omnn8agQYMAhC+2QRAEQRAOn93ZH927fY24Llbm6M5NLZF8zVvY/vJD6NlxJfo1eAXY8ArS2wOFhxOxat2FOPepd6rZ65pFjczoyc/Px/r167F+/XoA1l+J1q9fj927d4MxhgkTJuBPf/oT3nnnHXzzzTe44YYbkJGRgeHDhwOwvlwMHjwYN998M9asWYNPP/0U48ePx8iRI90vOddeey0SEhIwZswYbNy4Ef/+97/xzDPPYOLEidUQcdCwuzKR8iY8N0LLR6tHrM+JKcqo2fdYhFCv1h9dNom6T3Oizw2dX+p8QnJGj+5BJXc1IJ2rId6LmzetivyfL4PHsa+uHKRB7FFuaSHLQbSrmzdHza4Rsys83+UWcV1TLqfzSIzWJo9uM6jdeNABx2ZAJktYNg8QXexyBQtls+rkvkwlx55rU3XXN9j23w9BPpn2gF3MoIGQ0RNTnIGNq4fbWUnORZTq1s3Lo34sQHO/OIKN0mndakwTTOkkDJDmJvLF6LSrEfT56ty//mCta+zd3+5+u4+7xzmXP72d+Nx71KvfuWfUxmF2tpL11lTubyHbyG07aeF2xWunkGDTvVG8H5ZAZFUaTdgkahexLEahsm7dOhQVFUmLXHTs2BGtW7eWFrno2rWrNLfhoEGDkJeXh40bN7plxDqcMk4dBEEQBBGNgz/uxc4pp+KsXl8gLrkYxccj+OSbK3Dqn75FRpeulphz7W6s3DgS2Wv6Y+XGkcCoXSTyaKiRGT2ff/45zj//fPe9I76MHj0ac+bMwf33348jR45g7NixOHjwIPr164dFixYhKSnJPWfu3LkYP348LrzwQhiGgREjRmDWrFnu8UaNGuHDDz/EuHHjkJWVhSZNmmDq1KllW1pdnaE09hPLVqZCNKEQMURbf0CaQiBOZVG0RP1IF95foMviR2lHLkFxeEOp4Ei58K//iJozwCANx6y/4gf4ERgp556GFkNfUGMwmEZaDBqAi847poUBp1qAQ74VnGQCcY6h0tjUZmmoxZimHLPm4FFvS6lcQIWOGBPTbeYO7J2fLDBLggnlZYNQHsOL7Qb35sdhskgivtT44mopXN4ZLWbn2roZWpososCPKyYIc5DvF0c4k9rAseG2qwHuLgmu6Wu6PuCUNDm44dkBnD7jPbak7WfMzugRbUr2mN1msjADwH18S6rZddjeb9/HzupgTpxeh7ZrFy6Um/CjFXsFYUqqQ8SyL7YdK9PvLqKmEstiFLpzEhISkJqaKu1XF7mItoBFUJm8vDwcO3YMycnJZYqpoKAABQUF7vtyr3hKEARB1EhWzJiKHvVeQKtORwFYmTqbGs7Ab6b/XiqX3KAhLaEeAzVS6DnvvPOkL7cqjDE89thjeOyxxwLLpKWlYd68eaF2zjjjDKxcubLMflY8VfmFO1TVCSgfq6Ciriel4DPpG/bBN8qPajNEEogaIo/5nVhdWGv4RA2lnCiDmb6yIUKVOKCNUQNUa5OyZpRCvtiEQahqQJW4mHzAemmPa03maVM6m6rO4dj0D6FldGNZ16ZpiT0ak5YvXI4tSDRQke4EQQCzfvqH/q6fSgNobfu89OpXx+PudeRcElEko1wWwhyhAID8iFlQ42rUSjUzSKxCL5gIB3W3pm6fKghxE8y+UUQ73nHZqKOnMACcKYEwM/DedfqF9cIUDqqNxd0a3UfKBPGFgSsfCGbANQYM4Wbk3FTEI+XTiFuPkgVln1nHPR+1NjlgGFx4Syk9tYEHHngATzzxRGiZzZs3V5E3Vcv06dPx6KOPVrcbBEEQRCXx884dKJl3Lvq2ygWLAMUFEaz5uj/6P/0esqrbuVpMjRR6TlzCvnBXtAgUUp9WA9KVD5MyQspow2TSD7dcmKoRaIPrd8dwrnqKOMuGvF8vBwW6r9gQBRV/zpP/oQzXpjjA0xiIySaT7YsvwrQ1L8lAI+4o79UBqIFy2AywEdWmktGjrSsWZSzMtiJeRM3o0VTiigsBVlQxxJelJGT0+ISWIDGQ+YUaQBaFdDbdOpnOD40t1SGdOBTSaaVMF0NUyYRMHF0/YmL9sjADe9UrNSvGqcuL0QCYqXwMyQqL++imUpcr/LntaQiPkylSldCHnOwazu18IaY2Gndt+a8vs+N0Mp+8x7K8uvxtRBk9tYN77rkHN954Y2iZU045JabFKFTS09NRWFiIgwcPSlk96iIXa9askc5TF7AIWuQiJSWlzNk8gLXiqfhYfV5eHlq1alXm+giCIIiaQ/bfZyIrYSoiba2Zln/elYaDZ/0d/W+6pJo9q/2Q0FPjqKov3VHEGN+uWDN6fMN5Pdr6lQMBfwGPXpk4ygt3Q63Xn13jLxM2iI/FJBfK8QCb3mDNq99tIaZJMgjxTWdTzRfQPQnClEvJ4R8rqr0oKMPCyegpi02VmG0qGT2BekKQchRi243FbkzvvT+jxyecKGNuDiW7RjmbK+eIAg2DnNHj6yMChtim3GobsU1cIU9FEIVcYUaX0aPREtx6ndeGZr+mTxlOPEJ2jWRPSsGRjRnCPmeyavc05fEvqT77PO4e5oAptLtk0KrHhGVPdEcy6alLkk3xkTG3DzHA5F7dAHyZrYwxmCa3bDLvPC+Lx1Fv1DtcvD5cmIyZ20usEzWdpk2bomnTplHLxbIYhUpWVhbi4+OxZMkSjBgxAgCwdetW7N69G3369HHrnTZtGg4cOOA+GrZ48WKkpKSgc+fObpn3339fqnvx4sVuHWUlMTERiYmJ5aqDIAiCqFkUHjuG9ZMG4Kysr2HEW9/z9mxtgVZTNqO5vVASUT5q5GTMJzY8ZKtImLCFuCGVjwVxgBHicyxhBp7OlC2gkpg1M68uA/7ameadLhwd6oBf3YL2G2BQJ111zylFV5Dr9G548b12/hzRjtCsXKMwqee69THBNo9uU4otSrePyaY9ADaEzbdsuGqrFLeaVI8Be1VsFmxT8FHFlOYB99+bXmaLshnea8PwNtcHeEuXq7U7Uz05cLWA/Vq0Z9g2ofHHMDRllVC4KdvzRBXFtP2COe1q2HcCY+4+wxCP29fAFl24LYRYeoYjnniTHqswwVenXdUL6MXm3ZUGAFGx5PAmGRc7lPPJwYSl2g3mLZsubl7wEPZbj1sxxn2ioDXxOPeUH9u0a1O4MVz/md2mzKCMnjpGLItR/Pjjj+jYsaObodOoUSOMGTMGEydOxMcff4x169bhpptuQp8+fdC7d28AwMCBA9G5c2dcf/31+Oqrr/C///0PDz/8MMaNG+eKMLfeeiu+//573H///diyZQuef/55vPHGG7j77rtd/8IW2yAIgiBODJY+cheO/r0tsnqvhxFv4tC+FHy2/15kPrYdcSTyVBiU0VOjqMIv3KU2FZQPEVZhQPlYbQeWU/MqAgpGc9lXX1BGj1fOgDxOFD0IOk9IFohi3X+O9Nd9xaYuPnWXWjeHJbqI413tlQu4nN5+LyK1HmcsLz6So87RUxqbOmKyyb1Yg6pS5/AJs+nzQdQTnTZVMnqkqphmX5gfii3G/Fk0jvjGBX9UO6q4xIV9XCnnFnB2cEe08Mq7NpksFnGhMkMoy4TKtRk9cjjuC7e/cADCvDTaGxBeGzL3HCdOyxlLY/Eea5Iq40Ibu7tNK9vF1zDCa0PSeQRBzrlZuHKmLTaJ8bhHuDVHj9vIkB5Xc+bnkSfBdpZ/Z1CzeLxsKLtHMrt/MvugnQlm0rJbdY5oi1EUFRVh69atOHr0qLvv6aefdssWFBRg0KBBeP75593jkUgECxcuxG233YY+ffqgfv36GD16tDRXYmZmJt577z3cfffdeOaZZ3DyySfjH//4h7u0OhB9sQ2CIAii7lJcVIQtD/TAuT2+tzKZi4GvvjwDZzy+BP2S61W3e3UOxsNmPSZCycvLQ6NGjYDMSwCjLOpjWYQdZ/RS1mQsMacjdpNApBT5X0x+zSKx2xJHqSw+wM1ovhuyzZjqsN5HInFRSnFtdXEMiAQMYkURSD03Ao6kOLmsH+4/xiybQQKBTnhy3scZHHGR6K0oDtAd4gy163GpvM8u886Li4RbZL4XFvEBcnSgyGaP9BPiWFQBJahrxMex0AYyhJPFYgkJVqZGoK2QOhPig+9Nd94f5t+fEA8YRsR3pu46iC+ZE6eumCDqMTVOBiTGy/0dLMC24m+EccQpH5WqTake14di1EuEJ9Ro2kMXrwEgYpQgLs4WO4RsP7EdJP/t1xHDRHJSiSy4uuXsTB3GfTEycMTHlSASMb2+KbYhAwzm9wMAEuILkZRUJMTPhZ/O54CoLDl+AAkJhYgYzqTP3D3utKEhKFJMsH84vxAtun6GQ4cOISUlBQRR03G+e1GfJQiCqD1s+t/7OGntbWh2ys8AALPYwCfbrsL5fzyxVs+qyt9hlNFToyiL8FNWhD/Tx1w+qGwp/Y61eGA51RdNwag29LkvYRk91h/HNYNwBAtATs1hHmvyBIR6vdL+KxCu0QYdNbneJ9duiMahX9ZZtqlLuOBMyLoJsKk3GG6Lhdg05XG93qb2QLBN0baQDOLFyTlM6aoJ1YaIToZWPPNqELMzVEc4h/VIFPO3gxEUIxCc0ePUK9p2jjPPpql0XE/EUDJzRDFGtyS7YlNqTwjXWPybhNJERoDIyh2bTkaPraxIZbiVZePadf23MnqsCZW5VCfALeFE1FyEF+4E2bp4uJBZ4+yz7yuTm9ZjWIAk2DixG+Ks5oD02JU8l5BdzJ142pRtcm8pdpPm6CEIgiAIogI4ln8Yq6dPQNyRH1FcvyV6TZ6J5AYNsfbOXjij2xbEnVIMbgLfb2iFxrcvwvk3tK1ul+s0JPTUKGL9wl0RgpAyKiiXSbWSKP7FajNQW1J3auyX0iUHdSyuGwT7BvCaA+J5QXU69pw5N0qrOTCE63RKwoW3n+njclEGvdIhzpQ5QuwTlPp8bcSj29Q6GyLURLPpzhETUDUA3+MzpdUgg1bACo1Rc9A0AbhJaHovgm3KopWvPQLuI5NbWWGqe4DQd5jXD6R7gKkTSGv6i8Yml+JUhCYlBkkg0sUo+KaKKaJ5bgKI8x5rYkIBQ+evW69hLUNu186UMlyRGTmc+pg1Qbbd56XPB2aJOkwKxK0dDBEYRokUt3oBRcGLc277KD6WZQtA9rnMzupRZxdj9qNlRrS0N4IgCIIgiCismDgMPTuuRP8uhe6+wnn/wXED6NG7GABw/GAyvjx6C/r9eVp1uXlCQUJPjaIiBJyqNhtViii7vZhEnpAKyxCXLs8nSFZSx4i6ZaI5vEyWaC4FaR2BOOPMgMJidpJUtyYxAvAG1tGanftUND++xJMAQSF2m9FRy5gciHB/GVVACLMbzZZvnhWTgxnM50tg9pBtzy+exWaT2e+VeXgBAO6TcgF1Gwy+CZndOlW7Yjm7gJoIIgqI7omqGBSiKTirT3lZJ95rwxZPxP7MOAIfJ1VFKSstx6rQ6QMcQIkbl6VuOo/kcQDcsLJrmHLDMANg9qzSovjjaTLcmmhZqEs8zt2LZRm03jliUYmy0paX3eM9xiXGxpTX3LbJ3WvGXevcPc6Eu5jLM4ETBEEQBEGUihUTh6Fv1hLk7knD5pzL0X7Ezdjz4i3o0fNrGHHWH78O728IdtXn6HfyydXt7gkDCT01iqrM6Cml2UCTupN18kIp7TnlfHZjdLaMTaQb9Or2qR5xyGIG4K0EpTtPK8Bo/AmLlocMdJ36dDaDRAUuDQz1jjIjetNqkySCukIMNtU6Y7YZEmd50Okn0iS80HfbQLMc4NGSKhRBzJftoivu3D8asQuwM3q0zuptSaKcRpzSVuMTwiBl9IgnSvbEmGxhQuzQosCiRdRmXFXIOyDdG0xoT6F+ZjhzPAnX1G5YLilWpqy5cgaTm+41YG59Th3ChNDq5NDcWm/Py8bxbgbOvdfWMUhL2nNugjNP4PFWZWOuj27IjLltQhk9BEEQBEGUlWP5h9Gz40rk7klD6l1b0WzVShybdwWyeu91J1w2iyNIGrsNyQ0aVre7JxT0Da9GwqJs1WFSZzcW/0L8LXWY0U4QTvSeqCgVXNl0VanvXatc9kKtS7eJ43Cdy2FRGtCcoPhpCvWazqYRHXzLdjubYtMSLFjA5m9DU/GhTDajtGGQzVjiFA25y4IHN6lry2fTznBxsjEk/wThwh2AO5tvcutgo+L1dOxZNvX2nCXG1QYV25lrGpcLPzngLNAEblqbkwAi+iBWoyXsvtbZ5apd60RxPwT/fIKSGrd7b1r/cW5v9mvT3rgp+GBa89dYj2Fx+9p6BZjriD9Wxgw4S7A7H5/OvEbiT3UDM2GtvsXtffLFkddzkxvUXfbdmR+IWfWYJhdigRCP7Qtl9BAEQRAEUUZWT5+AhAaF2Fx4Ob64fwDa7x2Btl0skWfPlgx8snkE4pJKsHr6hOp29YSDMnpqJGEKRSUJPVFNOrJELCeJ5UwEDp1LHWasyg0LqSOmM93XQfJWEFxTJkz+Els1aAwcZivMF9UH1xemuZLCYF06pnGcRfVMY1NTVWBmjSaoWC9lrHHq6nUFnBBj2usodDfto2hO2zL5vfuaeeeFGfb1ExYQJ9f7qhx2HxsKul0ke6J95v3U9bEwozrhTcWXQeR+/sjtK7aDrw5NGzsSnJQNZf/DlEAYABiAYactMbdSzwHnnXeqJwFyXqIXg2GJTd7qYcwTbxggZvRYWUxycBxc6mdii1qP8HnqpiE0llWnN004Y564wyijhyAIgiCIMhJ35EcAQGbJe2jZJweA9Z3ks8/7o9/TixD/9Xpgw1tuOaLqIKGnRlFJIk6FmCyt5OGgGUSUKcxYJY3y2IhNewoUWAIGzM7f41VRQUg28NkIeu87GFJAM9YFB2AImS5uFTFqY9Hm59EJJ87rstpU64pm021TDnvFpBAhReNDaYQ8Lux0s0sUocJXb5iyF2LYEYpcE2LGjV2faJNrfJDM2+cxwf/AwkLgrihon2+ofunO17/VzhHExRfMKWNdVXcFMCa4JAg5rrvidWVeXQxMCpQrP5n4zs6CcWwZ4P72gCWuOHZsOQqMRcDgzOPDvevHvDb3/LUd5ByIWBk91v/cblv/lZEf2RIFME8kcoQtpy7m/BQbhVFGD0EQBEEQZeP4sWNomrgDANCygyXy/PpjI/zQejr6PT0aALB9/stIbw8U129ZbX6eqJDQU6MoZcZKlZqswIyeMoUZ60n2iWHqSQwmg8biUbMWAgrIf9EPt6lWGWovik3teyYfU8UKtaxcBxNeB3uns11Wm1F2Bx8T4gzKHtKM26P2tKB2FPfpxue+jB45QSRqipavTQVBiWkCCarTbW8jOBbpFOGYVEYReLRimtoGqg2mvBfsye+9jB4W7XzAPycUCzig2JLm6jEAZjg9XsmuYdIPYbd1U5owLWEIartweUUzJmYY2Uadu8yZXNmxr/ponyNOFu1k/Lin2IUNIUjrWnnnGIEqKEEQBEEQhJ5lTzyCPk2eRrtu1opaZpGB7O+vxbmP/h1N7DKFx46iU+ICFB5ORK/JM6vN1xMVEnpqHLF86a7gL+YxVacrFMuJMSyLExOlHuaXyY6UERJwPJY5XMIkMfF1JEqd5bnSagxqJohkM2Dg6qvTHe/qc3t0won4XhUQYrGp1qGzqT3mZPSE2CnLGFdtR+mYq3ro/Qx2JGC/Yle/g4lj+gCjml08OBahaikDTDLP5QmZxcwS59wwoVQU+8RrqL+e3l7dPcqE/YCc0cOcHcyb0FhtA3cybC6ISqYBbhbbghhXDAg/BLHGyegxYMCdAJm7FQNg9mp4TqYNA3NXE7MyeqyygmCjtIab1SS0ByBO6iw0qn3M5M6y8tztK84qXWZ5ZycnCIIgCOKE4eihQ9jw8GCcc9Y3MOKtbOv8nxqgQdN8dEx+H8sfvQPtLh+D7fNfRqfEBUhrlYtV6y7EuTQRc5VDQk+NI2z4LJYpr9gjDKti+Z5fkRk9Qaf6QtKMrGJCHH0G1R18ZtjgVEdQYk208bzaShWqfYUdZp5NF64fPAcHJQ6Yuf+w5jQOpf5SZvQEHY4WJ1fsiOeECVDR0PnrPlIU0z0lvJTK6zutzy3mtT/zNa7ihyB8aDN6oL/emiQW6acjbElZJMJrzuQqddfAJ1AxjS1nNSnVHzWDyXVMuMYcXgaL2w76PmuIZQzTznax8mSktlLFQ6l6E5ybrk33s8FZHt3ZmLfPE3YMW4CxxTvhk8ERWJ3H2JyGErOQPJtcmVKI2dlDTGo4xljgKnwEQRAEQRAiSydcgay2nyCrzxEAwNHcZHy56zc498m3sGLiMPTsuBL9GrwCbHgF6e2BwsOJlsjz1DvV7PmJCQk9NZaq+PatjupKUTb6CTZR1v8OJVabIRWVshl1T3yEaUXSoFcZ/AVJY6INZwn2MIEk1NmQ5g3MSuIam+rANaxSYXQbq/xWLpsaF2LBEAfBsknrdTlvMe57ET5w1gpnOsf0O0IzesTGlUQSjT1RnPBJqYoRMUGJq/sUlUi9ttGa163Pn4BivWeiTQYYXLoXuSZm0b7zwxKkGCBMVAxBuPFC5jCFQJnJwJVlyd2SXPTBE3Dc2hnzxyXaY858Q/ZkzM6HhwFYS385JwrecadNhImU1Qhcm8y7aIy719YnzDEO0ySlhyAIgiCIYPZv/w7x/+2Hc3vmWUumFxlY/1U3dJv2P5ybXB8AcO5T7+BY/mGsnD4BcUd+RHH9lug1eSZl8lQjJPTUWHSSQ2XZYHqTkvmyZvSEVBotq0cbcpgU4yCoH7oUghDEgVOQK0rSgjugDnNdPSZulvijzzAIFZnKqG85A2hdEokvBlUoUQafqs1AHaOMNqOJMWE2Te7XwXwZRRpBItoiRFqbzi3EZWlA65tGfPL3HfnKhz6WpaQscV0ZQfhwjkd0GT3Oa+E8rdABW49Q1RK/eiLVEfQYnU+QE/uAXSETsnRg6zaiUqXe6m5/Y8IqU5J94T539guaGTPs7BlFDZPKi7Nvqz6oS3+J4pNwzH3vGYbcoOI8PhxM6tVcmH+KyatxSW3IrWrdTB6njRmYEat0ShAEQRDEicaS+29E78x3kdTiOACg8EgCdmT8Az2fGuErm9ygIc6b9nJVu0gEUCvXVS0pKcGUKVOQmZmJ5ORknHrqqfjjH/8ILsw1wDnH1KlT0aJFCyQnJ2PAgAHYtm2bVE9ubi5GjRqFlJQUpKamYsyYMcjPz6/qcEIQ5YAqqltVIdwigUN4XWHleBQfGBNHHt4x7fhDZ0snnURzQ+eXIQz9GHhAY/j/g0bF8FsSPRaLO7Ua9ib+p9vn2OQhNsMkOGczdBvzNmnQa2/OCkiiH1IX0VwesZ7S2hTFoMBuGWDTrU+zGYa9aY5Fm7KEw0rAkDbbFyejx1DsG4Y1fmeG4ofQ7T33RYcQeCswOOdbA3xm+DcxTjVWLndAaRO7ltrN3Kd/DMVV9VZ2bm8hJK0YBcsXZ1NjBABmMHBmbU6DOW2v+xhhEPxQxBixdiZ2GtsxDudRSvszgEmnSNebM+a/l11RXL5LpDuGe05bdVjvDTGNSTAqtg0X/nOvh3CzONY8f+x/hHo5B7hpVSo/lEYQBEEQBAFkvzgLPz7aFued+SaSGh1H8bEItnx5CpJ+n4vOQ/wiD1HzqJUZPU888QReeOEF/POf/0SXLl3w+eef46abbkKjRo1w5513AgBmzJiBWbNm4Z///CcyMzMxZcoUDBo0CJs2bUJSUhIAYNSoUdi3bx8WL16MoqIi3HTTTRg7dizmzZtXneHZVPaXb02qS6jJoMFSWFoOCzgunOurkoUcU+2xgH2aE33u6+IxYU2PrLMVvJf5XujRZefIA2q55qDqJHsBhYLO5YC7LLb2yqnNLggnzntBTvWdq3OEc29wXCabAedFs6nL6HHr49A+YuXTG/XVax2y2tbL6JGusyNYaRagE5cnd1+5AhfzxBJNDNx+wXU+OWKO5mTpsTadLqqco8bCAThJMtKy747f6rXjduwBsfhs2n67rpmmbsEsyX8eEKf1j66jBH82WdeYQ11ZzvXBrpIpn1Nec4symfiKy8eFRB7LpKl0aq65FmpDiV47jnHvKHfEJ89BS/txLgZl9BAEQRAEYVFcXIwtk3qgZ4/vwFKsfbu3ZCBhxGvoMuas6nWOKBW1UuhZtWoVLrvsMgwZMgQA0LZtW7z22mtYs2YNAGuwNXPmTDz88MO47LLLAACvvvoqmjdvjgULFmDkyJHYvHkzFi1ahLVr1+Kss6xO++yzz+KSSy7BX/7yF2RkZFRPcC46WaAiCRBDAk2GqAqhPoadJ9oUxSHnmKE5Ncb2KNUELMIAKQZb8l4uv1QG7Qz6ZhWj9VrIb0/vQUBaRBScugymuSrRtD5x3OqW9Q841cl3mf2PHGfsNnVt5isWYLOsc/SUahEiRZTSztEj+hfmj26vKKKIZcXY7IwezWmh9riiEYg2VPnAzVFhQjwxTOYM8Ty74qC7TFy1y7mmrhbBDHAmiyRcrFd9LcTETe59lAiiEIO3bpU2DjvTRrIp3LjMPlOa2DlwXXfRYUfFsjq4m9wDDsYMoUNb5cRMVScxx2/HiUMKEPrWFuc6ChLwCYIgCII40Vgz52W03fcIOmXlAgB4CUP2Nxeh/4z51ewZURZq5aNbffv2xZIlS/Dtt98CAL766it88sknuPjiiwEAO3bsQE5ODgYMGOCe06hRI/Tq1QvZ2dkAgOzsbKSmproiDwAMGDAAhmFg9erVVRhNdcEDttKW57AyYcwY6rBhmk1nRzv+KK3fscJd1+S9wf85D1zoznP2aUMUPFZb0NqCbUqx6rQppX51n2PLZ5PLm/sojRKImqkhP0QC32XhsOoxWdlscp89/1VnQTZNSI8EccWWelxq1xC4+oYD3LQ3zuVY7GOuLVMsK9TFxBDEYHQOev6btk21bp89fxX+DBpN4/qKcK8+aNpXcleoR7quKkL/UvuRc824aYLZDSvWrbazFCe3+gaTRGRHUuXCJuPVb7r1+J/V45YAw4W7UhRfnHvTPWgCzAST7j7rIOPcis3k4Ny020D8hBB9Y+5PvyDJ7OOyXCfN22PfIMwWlqz/fRURBEEQBHEC8cuPP2DL/Z3QnU1A4za54Cbww9Z05HRbSSJPLaZWZvQ88MADyMvLQ8eOHRGJRFBSUoJp06Zh1KhRAICcnBwAQPPmzaXzmjdv7h7LyclBs2bNpONxcXFIS0tzy6gUFBSgoKDAfZ+Xl1dhMQXjDmcruN6Q+rQmdXJFLHVq9vGAY2qKBdedXnXt4AwJ9VkX3gCJwZ7Tg/lr0+kHan3+/drcDqk2BntAqpFqo10FrfjEgs8VB/3SUtWa8mImiXjM4GW3qRJ0PXw2DU2Wh1qXbmfIuFdbh3ANfBk9uhh1PvHgq67GJWXV2DbFjJ6othyTapxMueWE91LftgU/9TG00AmjXaOaMtr7XG1Xw+pE7g7v3guL2xKnPPnMlUCcVbiUU2RNyACYqbSJ0zE9wUgxaWEyIE60aQihSp4I1465ohTndqaQrl00sYtwV7xy6rJtCheICZPrM+2FIwiCIAjiRGDxA2NxXqfXkHqm9Qeu/J8aYFu9e3HWo/dVs2dEeamVQs8bb7yBuXPnYt68eejSpQvWr1+PCRMmICMjA6NHj640u9OnT8ejjz5aafVbVNWX7igpDL5dsf7VN5aRnVNM9UFNOdCdqKuvPG0mjzK59gj37VP1lSC9QLdfXaxI97d7dW4Q1QZHwMBaqEctL77myr6AMaUkIOhsBl1BdyVru7zJ/FdY9/SJ+gRfkC7gO88+INk05RW0gq6Hrx1DupO2bYUFnTj3htk+Z4WLrj6i5Igq4knuYJ3Lbe9knLj9Rpmjx2kDXxiq6KPqSUG3OBMycuxyugwh8dErjUlXIPId0/QpcZVxq2FN2Z54IZQ2FqfkscQb4f5WHv+S6mNW9pD70cS4lYgj9UWncqse515mhifqWG3LZR+d9CO3LqE2DncFOEeU8gQp2U3GrH7N4DQShEmn4TnrXkyhV7nXh4ObjtjDrUmZCYIgCIKosxzLP4zVyrLnh37MQe5LV+D87t+DGdb3hN1bWqD1lA04KyGpul0mKoBaKfTcd999eOCBBzBy5EgAQNeuXbFr1y5Mnz4do0ePRnp6OgBg//79aNGihXve/v37ceaZZwIA0tPTceDAAane4uJi5ObmuuerTJ48GRMnTnTf5+XloVWrVhUZGsIFlYoUgaLU5RvRBpWPJuwEPB0YlNUjjlCDRubBlcV6QmB5pn2tZvVwn0VnXBhkXtBLov4U3+klKGFXlDbSaRg6X0LPFUxb5uTRtVqHoTmgzejR2XPiiTL2DNRmRJvOClfhVcWuYSom1EXiXFFBY1MVVtS25RxgEd9RqW7VpjcvkCzIaeNVfHLbWsDtSmpcOoGIaerUiTyqiCPMdy4KR0EilDj3kWfbWw9P01SS2Ac44ol13wYlSIqrdbl2nWXR3FO4ZNR97EvINHJFVFF5c89n0v1qKG3o2nSD54JIZQtAvr5lP7YnPq9m/2COr8y7qZx91nLu9jpgulnJCYIgCIKoE6yYOAw9O65E/y6F7r7iN95EqsnQNKsYAPDrj42wBaPR74/Tq8tNohKolULP0aNHYRiygBCJRGCa1p+AMzMzkZ6ejiVLlrjCTl5eHlavXo3bbrsNANCnTx8cPHgQ69atQ1ZWFgBg6dKlME0TvXr10tpNTExEYmJiJUUFVKyQU9GmghQFcTCjE3V0aRu640x5H2AuuKIyEpzRoysbJneFaS5BmSmGcMx/vpjZUTZUuxzWuDTYpkVMj+EoNpzDbiKGULma0ROTzZDAY7JpZ/SE2TR0XTZKg7vDZiFrxNEQOFen59VUz/zSJDP0fUSsW1oSXbDvrb5lv9cIPjqbOpHEDdDZIWbuMM+OOJ+Se+cKb5hwviTciI9jaeJ1zXNF73Pm5hH9UypxrqUoGLriG+f20uziY02ygin6zzlgwLSyXcSG4aIBOx7xI0sQpjiYtOy58y+zVWFfRhRgzdHjNjLkx9UcsUbqs8x7DItZ2WTMvhvceDjcx724k1UkzC9kUkYPQRAEQdRJVkwchr5ZS5C7Jw2bcy5HcmZ3nH7kHiSmWFORlBRGsHbzhej757fRjx7lrnPUSqFn6NChmDZtGlq3bo0uXbrgyy+/xFNPPYXf/e53AKwv2RMmTMCf/vQntG/f3l1ePSMjA8OHDwcAdOrUCYMHD8bNN9+MF198EUVFRRg/fjxGjhxZjStuxfKFu4JuwtJ8t9eNCuWDIZVqRtK+YpoRfjTlJNBe+dqHKT/l/crgMIonQfXq6tZ7La/4E1pxKQ45WQExNW3g5dW3RVC9jMcw83uI5qcjrE0BK2MiarJCgI1oXU8SL8T9znw5IeWlfY49O9NFa1MVTyR7kObLiRauVmMV3jKloG65eWefOheR8lb7RpvRIxiXsmokP5x5iLi7P+hjSczosTQNbpcXVCvhPHXOI/e1YYAZ3qNe8lL3guokxifa1GQQOXe0IQQphmAwA4ZR4p7gCUdOrhDk68asSaEtLYvBYCbcjDsnZruhDGleHs+4QRk9BEEQBFHnOJZ/GD07rkTunjQ0uH0DMPlqdGv5KuJSigAAJUUGSgoM9JjyKs3XV0eplULPs88+iylTpuD222/HgQMHkJGRgVtuuQVTp051y9x///04cuQIxo4di4MHD6Jfv35YtGgRkpK8Zw7nzp2L8ePH48ILL4RhGBgxYgRmzZpVHSHZVOVNxqFP2wgpr/VP3B90XMFXTK3bPxiLoZIKQZw/R28xSJIJzvYBhAFgwLHAzJoQm3KGgb5eHSaPYpPF2rr+iLiyx9VsmJB1E2Az0EQAYpvqbJrCgUCbAY0ekz4kjLvdONU5elR7AThCjZj74dbqJGFoOpC44hSYJlMoqloV8NYOimsaV1zBjAnlHYHF0PihxhnggpQlpCQWScuMq01kBAhd3LHJnUwn5ru+nHN3Sh2niFW9ldHDHcFEMW2I8/CItpks+En9w4lHaDwOuBk3JjfdDBspG4gLcUoiFRNee73HOdXNX2Kml83jxOxESRk9BEEQBFHnWD19Avp3KcQPG5vg1Ffao985RwAABXkJWP9tDxQ16ox+7V/ByukTcN60l6vZW6IyYJyrCeRErOTl5aFRo0ZA5iWAEV+GGsooVuiWvIkZ57xS2maRgJSMaPUYECYfKcV5zGrTUjeRPXo3dDbDzrGIROK0R5i2tPc+jnFEIvI+p1zYxM0RcCTG6esN2ucQx3homKJdySbjiItEr59pCsRFdEIQl88RT7NfxBlAJBIuW6nnOMSHtE+wTY6EOBZVWNDWxYD4uHC1x72LlDIJCQyGRl1R21JXdXy8YR/zFxT7k98mYDD/BZX6rE7UceLUnCf6q/M9MQGI6NpWc6P4+p7yUSnFJcYrCRrFqJfIfT75/BQqdd7HGSWIi/MycER7uhXSnHMjzES95BK4j2lJZZx9shLm1BcfV4xIxJSum9NpDCZIgYrv8XFFSE72nqGXMnncNjHhwWHYj3clJBQgYr/2+j13y6j3rCMkHT5SjBZds3Ho0CGkpKSAIGo6zncv6rMEQRDBrLrjXPTqs85djIGXAFs3tEObe/6HBs3S8ePX65G+4Rxkr+mPfjMXVbe7JwxV+TusVmb01F3KKPxUidmoz1HFWpH35+bQc3lIdVHliTITkFsUdZ8B+CaVdcqJwzKdd0HZPuXtDTzgtZi94LOpGzT7HOK+88JsM1jZFDqbofaUA6W1aXIgombACDahvNbZjGZLlMkZrEwNrnkUJlSXZbIfksua1BjdalfOvDxShokgXOjuXqbp1FI1zhcDpV7OvIweJQxZONEYDWsHN3tIjNN+bYDZWShC1RzgRnSBlDkd0L5BnT7AAZS4HzNWVo8j5nAA3LCya5xMGTEmw76rfUKT7ZgrhjKv6ZxquJOapVsZCyW2TfiOOYKO3H+Z8toKwpuA2vbBTQfjgqO2BVP8hCIIgiAIojZz9NAhfPngUPQ8az0A63tD0bE4fJE3AX2feNQtt33+y0hvDxTXb1lNnhKVDQk9NYpYk6sqWBCKxWzgMyDRTtacx0OOSeV4gN0y2IwR32A4YJ+KCb+rzlMWYQNRcSxclkijoUtYMFhIC3Hph0/hYiz8ETXxFMl2gG7nzg0b5DT374rZZsBJ5c1hVG2pA++gOANxOkFQlhXzv1TFKm0fDVLznF0aIUbXH0XBSNyn6lmx3KriHD1iETWzxj//D5duJkfAiOVjiTuqkHiAC/eBGLBQP2PMfiSMSTYBDi4pVp5QYh1m0gTZTrXMEaXECaGZPfePG2cEBitx7YirbnFJzXPm5fHagHMT3FZxLf9tm4wBMJV7g7ltoi5sQBAEQRBE7WTV+H7o0W0Dep9jzcPDTeBobjKSbt6Nvsn13HKFx46iU+ICFB5ORK/JM6vJW6KyIaGnRlLBQk6lmIz2d/RYqtCkK1S0jVKiy6pRM3h0GT3OwFqXmaErr9avsxN0TizHHLviwF3MLFDH+FEzaxxMBh44LuRSHGpGUZltwn9dRIJsmtwvgoSt7BUmuKm+SDYlUcFe3UmxGSRI6NrAF6sjhol9SxEyuFCJk60iiRg621pjSnxK44rXUMzoMZiXgaM1GdK4TIjHrVssKjjiy6ASq1XuPVGg8u5Nr5CbCSUEzJyJje3dcnaNUweX202NmTmCn1WpY1adf8jXFBzgkRIrX457O8W5euTopQaS5gVyhDFur1Zmt562LpMyegiCIAiiVrPu9deQsX0Szu7zCxgDzGKGDd90weGixujbczlyn+mAzQXD0e7yMdg+/2V0SlyAtFa5WLXuQpzboGF1u09UEvSnvBoJD9lqismwE2L0l8MaaXEWcJru/HLYiwEWsqlldKheOGXF7B5xUzMo1HqjRhsSslif5IdmMC5OsMvFTbXHnLq4ZtPbD8ps4qqtIJsBbRd0XWKJ09lEQ24bBDep3qYt5BjuAF/xjwPcDLBpejEHxmm/YIE2vVjdcXwMG2eyUSZsUOoU7bkTHzvvmVSNHsem2p6agF27jk0DYMJjS9Im1qFcFyle9yOGw0ovszYGDoN5mxg7DGtFKsaseZe87BrZCX8OF7eyayxr7j3O4GQRMeF05mXeGAC4YR9nbn+S7yKh3ZRPG6t/cVfcsTz1fGd2LIwZ8rWljJ46R25uLkaNGoWUlBSkpqZizJgxyM/PDz3n+PHjGDduHBo3bowGDRpgxIgR2L9/v1Rm9+7dGDJkCOrVq4dmzZrhvvvuQ3FxsVRm2bJl6NGjBxITE9GuXTvMmTNHOj59+nT07NkTDRs2RLNmzTB8+HBs3bq1QuImCII40fhp5x6su6Mnuh6/Bc1OsUSegrxErMt/GN2fXI1zZ76PVesuRMO0fPRr/wrSN5yDfu1fQcOTjlgiz1PvVHcIRCVSpoyed94pfae46KKLkJycXBZzJxCBw6QaZjLopBgrk4px3c7S1VeB7aaKNLq/o2v/Gi8MNsVjXPip7ndO4QiuNzSy0JF1sExmcL/ApPNdX2e4mKaL3XldVptqXUE21TJOxoZrM6SLqeJNND8km9x7LWb06B55kkwLO4NicHYyeMIMB7z5XiAIRUwTY0gwmqeZpNOcOqWDDF6GiG3XEP3yGQl3R1gIytsnvnDtWR65Aoa933FUjZuL11W4uZij+Agm1LjdPaaX0WPFqZzo2hKyduxaDOERPub4LZiWs+qcYDgQsQQi639ut62/V1iPbHlt4glfzI6T2246beasLCZnKIFRRk9dZNSoUdi3bx8WL16MoqIi3HTTTRg7dizmzZsXeM7dd9+N9957D2+++SYaNWqE8ePH44orrsCnn34KACgpKcGQIUOQnp6OVatWYd++fbjhhhsQHx+Pxx9/HACwY8cODBkyBLfeeivmzp2LJUuW4Pe//z1atGiBQYMGAQCWL1+OcePGoWfPniguLsaDDz6IgQMHYtOmTahfv37lNw5BEEQdoLi4GBvvPQude2xHWh/rd/6RX+pj4/Hr0ee+v6K3UPbcp97BsfzDWDl9AuKO/Iji+i3Ra/JMyuQ5ASjTqlulfaafMYZt27bhlFNOKa2pGs2JteqW4Z+Uw19Ib0+76lYM5xrxAaPzaPWwcq+6xXxH9O+dfXGMW6sQKYN6tR5Vm4m26lYY8Yz7llsO89V5H2Ec8VGah/leWCREvH1MMwANOj9eWHWr1DZjbB/5fI74OIYoJgO7V0J8gDqj2FGLJMYDRtC9qavSaUsGJMQF3JuqaKGUShRX3dLVECCoGREgLuIdDBK7dI+XJSb6lzQPfDxNssm9VdSCRCAm3Ceu7WIkJwmTHDuHDM35yuu4uBLERZyMIC6X0dmyfxpGCeollbjijyqQeObd9Bk4q3BZq25Z+9WPTVcwYpY/Yr1xkSIkJxV5vrmrbnl+O5Myu67Y9SUkHIdhmHImm+OnO0mWJUqJq3kdzi9CizM+oxWM6gibN29G586dsXbtWpx11lkAgEWLFuGSSy7BDz/8gIyMDN85hw4dQtOmTTFv3jxceeWVAIAtW7agU6dOyM7ORu/evfHBBx/g0ksvxd69e9G8eXMAwIsvvohJkybhp59+QkJCAiZNmoT33nsPGzZscOseOXIkDh48iEWL9Cu6/PTTT2jWrBmWL1+Oc889N6YYadUtgiBOZFbMehodDj+FJpm5AKzHtL74pjeypr2HuITEavaOiEZV/g4rc852Tk4OTNOMaatXr170CgkbFsNWHSZVqSLWrSJslsb58hHtiZegv32LloMyaZyfprBFozy9QOe7k4EiZhRZAz9Ij+KojwO5NoV0C137BNkWM2FKbTOkzjCbTpxORb7HfsL2R2lX50XQo2eu84a1WZPi2vYMYRNiDux33Isl7HE3sdG4aF+IVdqUxhMfLZMeo7Nfi4/1Af5sHN+fDDR21fI6m5J995E35p0jXAexD0jhCG3AnB2w6uHu46LMmnPKZDDBYHJ7M+1YTcNaRU10CrKDnNsPaNnCifWv9XgUYD/W5TyaareJ6chBHLY9ZsVpencptzxSIpTbzfMB1opkgZ+ZHCbnViwmh2lymKbTFoBZ3tnJiRpFdnY2UlNTXZEHAAYMGADDMLB69WrtOevWrUNRUREGDBjg7uvYsSNat26N7Oxst96uXbu6Ig8ADBo0CHl5edi4caNbRqzDKePUoePQoUMAgLS0tMAyBQUFyMvLkzaCIIgTjS8XvI0NE7ugT+oUNMnMBTeBI7/Uw9bUf6DXkx+RyEP4KNOjW6NHjy7VY1jXXXcd/dUlZnRfuitB3BEftIjle767dnhpBwUBvscUJtftjFJR+doqSEQJq5UHlAmqhynHyzzMihJq4GH7gLTsuzN4V4sFBsWEf7n/sOY0dciq2lSqDiSWdhYP8KDYYjUQVizAGXfszIW7RjjmO1cSQZR+zxS3lEPuAkpKgI7YBUB6hM1Fkfm1GTaa88T+65uAWRWPVHFHV4/GgHqPMPtZJ0PN4OHKe039Vttzoc3l+aRE+8IiW2CGCcNtXH/jSJ8VXLRpWkKN3cHds+33ovbl7nMzjaw5esDsZeCFaamlzCrBJ3myaMemEiOYnT3E5Daw5x8i6g45OTlo1qyZtC8uLg5paWnIyckJPCchIQGpqanS/ubNm7vn5OTkSCKPc9w5FlYmLy8Px44d831vNE0TEyZMwDnnnIPTTz89MKbp06fj0UcfDTxOEARRlyk8dgy7HumOrl33gNka/qF9KdjV+D50v2Migj89iROdMgk9s2fPLlX5F154oSxmTmCq4ou3blQXrXxYoTL4HJPNclVQaoJkoyAxRyqjDMh1MpU6Hg9bgj3InlRZSE5eoIDENTZVMSGsUmF0G6tI5ZxWJpuaumLByRLSmLRel7P7iEKKVycLFy4CbIZKqI5YFKgMssBHmHRCjViv9FYRTcRMGZ2Ioj7KFLNdxby64pYrmjDRRQYYXLoPudChfLaYd3tYugZzlx53BRLRvi1+mELgzGS2YKK5MmJVgoDj9XMDnJta3xzhyek/lmBmz+kTAQDTq0hIm3Lm4uFudpFzDTz/nLaUxRs768g5jXmr4zFmzUNEVB6ffvopdu7ciZKSEnffDTfcUOp6HnjgATzxxBOhZTZv3lzqequTcePGYcOGDfjkk09Cy02ePBkTJ0503+fl5aFVq1aV7R5BEES189EjD6BHg1dxSrdDAACzhGHtlkHo86f/II3+UENEodzLqx87dgycc/fxrF27dmH+/Pno3LkzBg4cWG4HT0yqIqtH9+f/EFi0AkHZNyF+Rw0zWkZPUAVlbys1k0Drls4LZWAbTRITN0f80Z0TJjCFGQltAaZ5IEQdbDuvlYqsDA19X4gmWJXVZhhhNk3u18F8GUUaQSLa702tTXcwruZSyEbFla7C+5h85cMmklaXCuPica7vnwB803xp2yFAsOEQBBrBjijWuBEIFejm+1HL+M4RKpT2h3wkyZoYA2OmdIBJR4X9TLg3De6KRPD5A69ttFlFJpjkIJdsMzsATwD0jnsXhkt+Ofu8dd0c45465ZRTJ112Mnosny3HHf/KPN0bEZVrrrkGOTk56N69OyIRax4tVsYv5vfccw9uvPHG0DKnnHIK0tPTceDAAWl/cXExcnNzkZ6erj0vPT0dhYWFOHjwoJTVs3//fvec9PR0rFmzRjrPWZVLLKOu1LV//36kpKT4snnGjx+PhQsXYsWKFTj55JND40pMTERiIj2SQBBE3eNY/mGsViZJTm7QECtmPok2P7+I8zrmgBmAWQIcyknF0QvfQt/re0evmCBQAULPZZddhiuuuAK33norDh48iF69eiE+Ph4///wznnrqKdx2220V4ecJSmUqtQF1B5oMGlKXxUd1VFcae7HYLOtfqJkybIotOuaMagPUGp145BR35jYufaThNsMkMlFk8h2LoWkZUyPxKg6T+4IEktjauHQHxKXHtafEaFeHkBTis6nN6BFsBnV9pu7VaEU6YYg5/wTUHXY9eZROHnQvuE//6GKJ1pG5XLfPF8VnT/tgSv/iXgZd1JtHjcQr4J3qOeZMqRMHZskqiqIkZRXpzDEnl8j0i5hSLcwT/uxdhpPGxORW50IGj5hHJwpW8j7nX64Y9jKJ3KygSv09c2Lz1VdfYdOmTRVSV9OmTdG0adOo5fr06YODBw9i3bp1yMrKAgAsXboUpmmiV69e2nOysrIQHx+PJUuWYMSIEQCArVu3Yvfu3ejTp49b77Rp03DgwAH30bDFixcjJSUFnTt3dsu8//77Ut2LFy926wCsvnzHHXdg/vz5WLZsGTIzM0vZEgRBEHWDFROHoWfHlejfpdDdVzjvLfyUWx99Wx0Es5/CPbCzMQ51m4GO14+sJk+J2kq5/5b3xRdfoH///gCA//znP2jevDl27dqFV199FbNmzSq3gyculf3lmwtbLCZ5wOZMK6w7FkNdvslFhDKB/gT5Ut5HEJS/wAds6n+AkhUQxWsHBrUVubRx4adsT0iFKa1eB7gT2pq6jcuT7rrWBKVGbgWhjHg5lWDV3hJoT2cT+isRdkCdPFjcTA57Mlr/BoSLI65LGrvOpLi6mKLZlKu0JwAWZh7WueTU607IayqbbdPxR/RLilMTi9q1VFGGA+5FlOKxbavt4t7Oek3K9UfySazGNMGUTsKc/maK7S9vTArU14LCpvhiB6pmrzk2nT7AOPfl14ivxPuVS++9xmGcu3VybgJKJpCzMpeXDeX95/faKSRYc25OtSRjYAYD18RPVAxnn302tm7dWqU2O3XqhMGDB+Pmm2/GmjVr8Omnn2L8+PEYOXKku+LWjz/+iI4dO7oZOo0aNcKYMWMwceJEfPzxx1i3bh1uuukm9OnTB717W385HjhwIDp37ozrr78eX331Ff73v//h4Ycfxrhx49xsm1tvvRXff/897r//fmzZsgXPP/883njjDdx9992uf+PGjcO//vUvzJs3Dw0bNkROTg5ycnJw7NixKm0ngiCI6mTFxGHom7UEh3Mb4JPtY7C/22dY/Ul3RBJMpLU+aD1aXcLw2aZL0eLB3eg4hEQeovSUO6Pn6NGjaNiwIQDgww8/xBVXXAHDMNC7d2/s2rWr3A6euDhfvitL8AlIAwk0Wba8k6jltTaF0b0uBaRSCPz7vLakBxf+hfRaHPypmRviT69l/Tb1XnDpR6w4dRlBg+2wLBcu/GS6LAC7HZQ61MG97jKH2QzOiBCKBdgs6xw9YYsQSV1SzDxxs0uYbzltqV3D/FFfKbeJ2nauHcaCH8PSxOj2S6W7izZEIU2cK8dgQjyGvt219oSK1eaV/LHrFq+pZdMAd+eWkeeYcSrR+WKJRAB3H8MSY2Xu3SvOt+z6y+yMHqFF1EfvuPAvACH7x9kvNwgTP2c4AAOuqMnArdW63OAtp7hwobxrwX39l4vClFCHnIDH4M4PBNtmpX2mEuvXr0e3bt3QoUMHJCYm2qujMd8jUBXN3LlzMX78eFx44YUwDAMjRoyQ/vBWVFSErVu34ujRo+6+p59+2i1bUFCAQYMG4fnnn3ePRyIRLFy4ELfddhv69OmD+vXrY/To0XjsscfcMpmZmXjvvfdw991345lnnsHJJ5+Mf/zjHxg0aJBbxpmz8bzzzpN8nj17dtRH0wiCIOoCx/IPo2fHlcjdk4bUu7ai4I8PIOn9gejVz1pRkJuAWWzgl77ZOOd6mmqZKDuM87BhTXTOOOMM/P73v8fll1+O008/HYsWLUKfPn2wbt06DBkyJHCVh7pAXl4eGjVqBGReAhjxZaghli/YASOnMk+sEO28oNG+Af8IVnQoaB8DWCSqSW0dRkL4eYE+MMCIYjPg3EhE1T65rnbLPWFfPAMihlxQK6Yo++LAkeCTW7nYesHnGt7AW0fQ1YpjHHEBzePrHYpYEq/EKA7btT4zz9dIRO9RVJshlzJIsAI44iNMWqVJKqLp5p5YA8RFgu4DwYSGxASAMUN/3TXOuk9dMSAu4jmrP19vNTEBMALuMZ0A5FQT0VwT9drpHqMCgKQEfd8Tb2XpnrH9iET8fS/wHGl/Eeonacqp/tnvDaFcXJyJSMS5p+z7mXFfrGodcUYJkpOK5X7tvuG+fV6dHAnxJYgYsk0Y4n3tpWqJ9cfHFyIpqUjoi5psI3efk+ljvU9MLIDBTG+CZknoEeO199n+5h0uREa31Th06BCtjlnBBP2xq02bNlXsSd3C+e5FfZYgiNrIsofGoH+X1/HxuivQNn4N2nb9wf29v+fbFth1/Cz0O+NdrNw4EudNe7l6nSUqnKr8HVbuR7emTp2Ke++9F23btkWvXr3cZ7E//PBDdO/evdwOEvpHC8oPC9iCTAaVV4dmun1R7DuTYqj1asOO5kNpCT7XqpnZCx4z32uxmTggz7cBrxnV5tR5b0gbC/xPrMNbLUjnt35foE3mZcBImxAI47CzC7xNvQLiAFycOsYoh82g+HwDddWmIddrCJvrr3DcIUz61ratIWx2Ro9br73f9QWyP25dXIxLuVKMSSIPY1Z9hlsv89kyDH/8UeO0b0XdLcjUmMSfzPNJ3Oe6L/6WifJRJt0nYv2GIXcWuxOJcetsc+b1WfERJc6Z9v4Ur5Fbkdo5hYso7ZICYUJ72udyA9x3JzD3ESoYzMroCf1ccx7lkh/nkkvInzbc9wHE3L4KxzZRKTz33HNo1KgR2rRpgzZt2iAlJYVWISUIgjjBieTtBAD07/QuMs+wRB6zyMBn3wxC20e2o921DwAA4o78WI1eEnWBcgs9V155JXbv3o3PP/8cixYtcvdfeOGFePrpp8tb/QmIbkheGV/Eg+encN0ILB+26c7T1c+VTWNDG3YsNksD971zNm+qEe8/Z84cJogcQOmukG5GI3XuGv/MPHKs0mMrmojUVtG1mjpXjm5eGzc4cdAaUC+En3DqcGyxctgMicW1yTU2lfljtPMB6bpPyMXUtq0zL41piQq6OWu4Ke8TfXVsenEp11vx0517x3RilOfocebl0cWpjPflsB1hTXN7Se0XMNeQbg4i6bbWtC3T7HOvq9Rmph2nvYltqsxNJNpk3BY0XNXJecm1RuVrZErtYB1j7lxMsOdjsgRXZaYbQ+iIvjvO+SSxXws3AZc6B/e1J8CsdjY9f+Ug1N8ZwmeG44vdfpZbHJyWV680Fi9eLK1iddJJJ+HDDz+sPocIgiCIauNgTg62P9ABvXuuBgDE1StCYX4C1n15Nkp+uw/nTH8bALB9vpXFU1y/ZbX5StQNyj1HD2Atqaku23n22WdXRNUnIGFfuitS8IlSl29gFlRe3a/6H6AliiNctS5xjBJTyNLwNZYTAstrvAHAlP3q38xtV0P8FQUhVRzSi0VM2afpF874UWNTH4d/GBjWWu4xn2km7dTVoQ7gDR7dppwREeKYpg5dN3UyWsJsxmIryDZTGtbSwZj72neOTuQQBAbv6St/QZ1NcV6g/8/emcdJUZz//1M9M3ux7K6LwHIqnoAiICCgoqhENN4aExOixBj9akBB1CDfxCuJoDEaNfGK8UwkJvlFjfFKEFTQgCLIVxRBjAqILKAce7Cwu9P1+6O7qquqq3tml733eftqd7q7qp6nqruHrs88VaUOC7PW1fCJISxyyVvJrBfTdrWbSEsaeSMouPCWmjNtGsnVYXTeX6bY1kVWGP5x41gwYTq3fK8FdVKH83nt6Xg3rkzHtQYSYq+aRhWpkOCGHSYrrNqR9gPD0rGgTb2HPTzkkAfXkTNALCMv68zkF5NYU1AO92LeUYroaT5c10VlZaWcx7CiogJ1dXWt7BVBEATRktTX1+PVaRfi6EPmY8AR1QC8d7DdO/KAb7+Po7oHgk5tzS4Myn0OtZW5GD3r7lbymOgoNCqi5/3334frupkT+nz44Yeor69vjKlINm7ciO9///vo1q0b8vPzMWTIELz77rvyPOccN954I3r16oX8/HxMmDABa9eu1crYtm0bJk2ahKKiIpSUlOCSSy5BVVVVk/rZMFiGrQVNhUxG9YbVn/5tBVjyaUnEz+7KZ/lzfGMq0lB4aE/9/V0/rg//MAWTOL2AK3/N8sO/95v2Gv6LOzc+qza0qJeQTaWjHKmmhG2Z9kJ2mOFDNjZjLqlpUwlQCOwoESFRK3CxjPe93TYQlCEqxV0R6WG3ZUYQqXVgTkSb+AllRI5STmREj6W+sJVrE55Mx0S5SjuKdjXraUb0CH/V8tRhXLKpLW0SLt9/GvxoFNdFKKJHfGWEvlZ8gYWDBUPTlMpzWz1dAK4L7gZRRK7wg4voPv8ZVusHf0AWAww5Sj7T4skG1AglDteP6JH1dTlcaR/g3I/m0dxncpieZ0P/JpJtKb+51HZ04bouXIroaTamTZuGY489FrNnz8bs2bMxbtw4bQUqgiAIomMz7/IzsePX++MbxzyPLt2r4aaBHZuK8M7SUcgr2Y1dj4zBG7dciY3vr8Abt1yJHfccitJ+27B0zbHIL+za2u4T7ZxGTcacSCRQXl6O7t27Z5W+qKgIK1aswAEHHNBgB21s374dw4cPxwknnIArrrgC3bt3x9q1a3HggQfiwAMPBADcfvvtmDNnDp544gkMGDAAN9xwA1auXIlVq1YhLy8PAHDqqadi06ZNeOihh1BXV4eLL74Yo0aNwty5c7Pyo2UmY7bkEb82NwqRrwG2GQAkImTBTOUksvDVoigwACzViCbyO1d7ORlztMbBreeSDEgkovOpf9U0CXDkJqOryXybNpIZqmm2urCRYDx2gmMtveFYMmFGGehlh3QSfyfp+BMcZ7JnsZmKiDuMt8mRk4yejDnClDyYSoZCJzQcI7P4mJPD4FhCW2xtaaZKpcQkzvaEtsALxoCcFOAoN0JUndRzzM+bTDJbsiB9xMpauSl/8nHlRCidZSfBOJLGV6VqU4uqUf1g9SjIBcC4HqVluQbqjgMg4aSRTALBUulc81dOxWPUJeG4yM9Ly2dQ88eXaZkcdqqa5kgl00gk3ODe1NqfB/el0W45/mTMwk99UmYuyw+McZkuJ6cWCcf1y+Qh/xxFVVOPV1bVoteQJTSxbTPxwQcf4LXXXgMAnHjiiTjssMNa2aP2D03GTBBEW+ffs3+BcfvehVSXWu/HHxdY93Ff5JzxEPqNGg/AW2J91MBFyCmslflqK3OxdM2xOO6u51vJc6K5acl/wxol9DiOg8suuwwFBQVZpb///vuxatWqJhN6rr/+erz11ltYtGiR9TznHL1798Y111yDa6+9FgCwc+dO9OzZE48//jguuOACfPTRRxg8eDCWLl2KkSNHAgBeeeUVfPOb38QXX3yB3r17Z/Sj6YWeLFWNJhF6APtYi5h8GVfdsp1nCK26FdcT1UzGrboV53vjhR6WSFqDOtRjtnMpxkOrbplpbfmTvtAT5VXcHZJkHMxiMyqvIMG4FE8irljEjr/Sl3brxX99iOzJBJBwrDKGdyxKV2GZV92ylQXoq25F2oxogFQDV90KhB7AsTyborMftYoVc/RVt0wLQpixIVfdstx7Mo9N6DHEt1DxzHLc38nL8YfGWfKYXxOq38kERyKhJbfumKILWB265Cr7CNoUQPg6K2WlEi4Sjh/io5QZJOHWMpNOPfLyAuFEF5Y4HGaKP6IQjtyUCyeRttbTK4trToiVs1LJWuTl1vvHguPSpqMfkytsAcjN3Q1HDslSxSCRx9VEWuFDRWUd+tCqW0Q7goQegiDaKm/PfRqlK27B/kM2wEl4/xbv2paPjf3vwsAzLwqlr6mqxNtzpiNZvRH1Xfpg9Ky7KZKng9OS/4Y1ao6e4447DmvWrMk6/dixY5Gfn98YU1aef/55TJw4Eeeffz7eeOMN9OnTBz/+8Y9x6aWXAgA+++wzlJeXY8KECTJPcXExRo8ejcWLF+OCCy7A4sWLUVJSIkUeAJgwYQIcx8Hbb7+Nc845p8n8zZ5sNbeGCDQxZTRE4osUhdTjtgIt+Wwz7NrycXv2aFsR9hpAVBRMJjj0aqkdyUxlZtOykX7E6BFR+TMJSBrcSBBqdoaoyB4tFY8+Fyo34pbKVE/zvFikKS6/KXNne72lzZCowayPii4ShO1wZe4aZkmoXrOwzbCgY7MRsmlcW3U3JGCY97ZN0MnCqFpPYdMmspjClteuPOST+CvKsd1GnCsiD3hwL5p1UIUXAGAOHEdE0rBw/ZiYrUsMihLlMbicy6l7ZBZHZguusfzD/L8JOI4iEClCjqihPjE39330hnZ5S+NBqVgQ8cOMi+FNUs3hZAp7IwiCIAgilrf/+AQOKp+F4d0rkRjmRf1WbC7ER5uPwtG3/RMDI/LlF3alJdSJZqNRQs/rr7/exG40jE8//RQPPPAAZsyYgf/93//F0qVLcdVVVyEnJweTJ09GeXk5AKBnz55avp49e8pz5eXl6NGjh3Y+mUyitLRUpjHZs2cP9uzZI/crKiqaslpoGgGnuczGqi4NKQjWNbPD3dB4dWNvfYjAhS7MCE/NY7b+rDkZs8jrGulMspGsQjYtdszTeixA+JxNT4myIXZZrMfa1C66vajOuN7fDZOhnjbbIpvL9UmgZR01ASHeZiZbaqebAd6cLpbIt9gAPEOQ0uppUS5CkyhzRWRU/YlV1pTzFjFNzFvDWPBZ2mOQ8/UY1YgO+hNpYtpBzuej1tP/7ICBc675x/x6Rwprqk1ZFtPmuAnq4AtAiujFHW/+Gi2qxv/oOG7wFcUUe75jjmgIptx3XNTLfwqMaB3v/2nfpmEQkIKOPnQtqKl6vUWgrhTClHpCE4/gr/RFNIRFixZh3LhxeOutt3DMMcdknW/Tpk0oLS1Fbm5uM3pHEARBtBSbPv4U6+76PkYc8z6cPn4Ez/Z8fFT5LYya+QCObtDoCYJoWppk1a2WxnVdjBw5ErNnzwYADB8+HB988AEefPBBTJ48udnszpkzB7fcckuzld+yET0NNBv5RZUpsyUfjzmnpeMRdhthM0ts0TeZIiOER2agEvPLy6QlZNOypkDi/Wofnz/Kjq1ONpumAxzQVhjKZFOzHaHbiSW4tVItClS2EqMRcBFpsymQNjXhyIybyMKmECEsczxphiJtRtyjNmVNMWlTLEMRNqotph+LG6KlG1J2jYgekcSMrDH3Q0OdhL0sbmTuQvmXjss/ZsSXKD+ooxj6x+Rp0bBcU6xc2W7eaQbXdb0Rq6rbol5iyBeY8RleRA9LSztMUeO4oeZxfziZFKe4C+5H9Kjt44lOrvFsMCk02YYaEvG8/PLLSCaTePHFFxsk9Fx44YX473//i/POOw+//vWvm9FDgiAIojnZ9uUmrLjp+xgzfAVGHbcbgPe+8d9V++Ggn/8fjjInJCSIVqBdvuH16tULgwcP1o4NGjQI69evBwC51PvmzZu1NJs3b5bnysrKsGXLFu18fX09tm3bFloqXjBr1izs3LlTbhs2bGiS+oRhGbbWMBkVN7IXvsqlcXhE9iiJpPnax6wljzlmGyZieuECSCNY3cpcacsszywzqlmUfloov1quusKVusKWSSZbDryN88xtbtqLtMkiNuNcVB1tbajZtBiNtCk2ZUn2TEibXF3piof9czLY87fIevofeKRNSx7j8piri8m2tRjUVrzy7YvVrcRKX+AWH7hFsLPZNK+JasPfhB25yper3wjCH+uDY9Rf1tNwygVDmjO4YnMBnoZcxcuzoayM5W8AB+NusMF4AhgHcxwwBKKfiGhzuVcXuUKZulKZy8GRhlwVy7+fRAUZ8zeItlQtczAmbHoPkSfm+St3uUyu3CVX9ZJtTRE9DeGWW25BfX09TjzxRKTTafz85z/POu+rr76Kzz77DD/60Y+a0UOCIAhib6ipqsTrP70Eb04/Ba//9BLUVFXKc/V1dVh+5Qh0ffUQHH/CEuSW7Eb9ngRW/N8wVI3/AIfOXoUEiTxEG6FdRvQcc8wxoTmCPv74Y+y3334AgAEDBqCsrAzz58/HsGHDAHjDrN5++21cccUVALx5g3bs2IFly5ZhxIgRAIAFCxbAdV2MHj3aajc3N7eFQq5t3WJBMwk9GU3a7MZl0jLH2Iw4L3p/oZ7hXtpsYE6bEhrZEhwyqkekiZpeN+ieZSjTP8ctJ1wGJHhEZuWwabdBV1KISUzJywH78Luw7VibUUWEKpzdFQ3ZtGTKGM0T0542P8SjIdvaFtFjLImtVU98iIvOMu6psE1LPlP4MMrmahrlhK1+6uMvbbGw3Wx8cLke0BOab0g5pv/lmhHt+trEI8U21yofDIuSz6YpQIl73QGYElrEZMEMXB0yZVGauOuC+5MwanqX4jPzKycnTmYM4I4v1HB5XloRoo9sd9UmCyJ6fDe9FdNY0H7GPSNz0hw9DeKmm27Cww8/jF/84hcoKSmJFW3mzZuHsWPHorCwEPfffz9WrFiBa665BgMHRs3WQBAEQbQmYiWscYcpK2HNfQYLV49DVWJ/HNXtWQwduw2A936x5oOD0HfK3zHi4oNay2WCiKRdvuFdffXVWLJkCWbPno1PPvkEc+fOxe9//3tMmTIFgNfZmj59On75y1/i+eefx8qVK3HRRRehd+/eOPvsswF4EUCnnHIKLr30Urzzzjt46623MHXqVFxwwQVZrbjV/LCIramwhTtkazIqYSP8jYrokXaywczY+HaKihQxj9k8ECfNDrVtE8EIzEhnlmkTTNRNRApE+anakp+NyIu4q+com9rfznS9bXUN2WQRm6XEqHZUywy1sVlPiy1hRD0W96VotcntET1qIzLHYlOJ5tHraUQF+R/Ma2iNpmnAo6nes1qkjLqv+CCjevx73Jynx6qhmTaNR5NrFdV9UKN7RESP9A9BnW3iHWeQUT1yjh54woc4yZVNRPUEES9+XV01kke5kxUHuVJzT+hlWnSNeG5ECer106JsXA4w14/oAVzZOOG4OK5c7KD+vj1lQnAODldEI8n66PcMRfQ0nPr6elx77bVIp9Ox6a699loUFhZiyZIleOqppzBhwgT88Ic/bCEvCYIgiIawcMaZOHrEfFRuK8Sbn1yCzUOX4M1PLsHu6hwcPWI+Th72CPbpvw3cBaq/KsD76Tk4bM7/obgviTxE26RdRvSMGjUKzz77LGbNmoWf//znGDBgAO6++25MmjRJpvnJT36C6upqXHbZZdixYweOPfZYvPLKK8jLy5NpnnrqKUydOhUnnXQSHMfBeeedh3vvvbc1qmTB2m3yaQrBx1JGrElTltgLO42ymW0hLEOh8ZhSkWk9yhueIUEmGcp2jBvnzFpxxIsSUb6bkRhxrWX6oOs50TlttkM2ufLZtJFFmRltmvW0uGtr20x3jya+GUY1m4riJvrlIX+UmyI4ZxZqHGVhH2wTK8v9iIbjRriZKSpqny33TKPm6IlJb9aPKQdF1EtoIu2oe0UVoNQ5esxINJstUaYf0cOEfc244bMsyxNnXO7CsfgMDm8JdJGeqRMv+6ogmLJKVvjOtN1zQQrvZpPD1fyTalsxedgrz6GJIhuMiAz+n//5n6zSP/fcc7j88svx7W9/G3PmzGlO1wiCIIhGUFNViVEDF2HbhlKUTFuD4/MLsPjJp3B43v9D1x5V8t/azZ/vi6qht+KQ738fR7auywSREca5rftDZENFRQWKi4uBAd8EnMaMx8y2y29J0ugJNEW+cKclnoSfNS5xRH1Ywno4Y16Wk4VvETYdi80synASSe2IrXTbhM0pxr2JW40OYFTnWZACR27Sfj5T3iTj1qaNSi+OJcCRSgXHoprY7MR6NgGWaPhXRsoBEgmm27N1lm15M8jRtluJgSOV8CbTjZqUOUhrsxmXKfpUbgpwHCc0rChOHBLnk0nLs6keiRBGcnMAhyVCwoculIRxGJBMRthSdmyiT14uMk/IbGnChMORtFxPW/vowk8aBfmuPK/dQ8q+7d5KJdJIOJAij/XZ1obOeSFLCcZRkFfrC2k8+hpoYo1HbioNx1Hn7+HawxYM1xKmPYEolapFfl49AO5P3CzOqe3DgzIRpMnL26PN46OeA/Q8wWgtjoqqPegz9B3s3LkTRUVFIJqOU089Ffvuuy8WLlyIFStWoKCgAKNGjcL777/f2q61a8S7F92zBEE0Fa//9BKMO+xpvPnJJcg/ZDwKltyMQ4Z9jkSOF7mZrnWQyHGx6MMLaDl0Yq9oyX/DGqwWLFq0CADw1ltvNbkzBBAegNJcOpwx5iLTpoYnNGiLMZ8xaTZ1b9q2Cn5TD3dwo0QRYdlMEyW2qMOhbOWY+1GbmJBZtWvatx5THFMHhphz25r7crRIbGvY/REptYEoPBjCYg5BiruiUeWHvPI/qDY0O1GbtVYRPrDwJmzKCYPVyX25GIoUthn4z/X6KGKGahOq3VDjBvZDtrgRTSTqpJSrlh36rLRBqL2Mi2cOzeJK3qg2hK1+/mpSjmGfwddvePA35AO8REFbcm1jDgcDhwMOh3kCi8MA5rjKMCjjDmNiiJQ/OIsHG+NeRA/zy+fyIRWCCw++BxgHHN8HB/AWkveGfnFNlDfsKQ0njnvXkPuXQX+CxFw9jsPgJJjWtrTqVvPx//7f/8M555yDV199Ffvssw+2bdtGq20RBEG0QZLVGwEAw4ufxvDaizDoqP8ikZPGru35ePf9Y7B1+EItHUG0Bxo8dKuxy4oSDcHegW5WGxlNRnfssyygEVkyJWj6dnJhn0CZG/umFwzQlldnCDrvKqZ4kYgoU7UVdZ5zREq1wn5UPrPMTPtB5sid2KOqkGEaaehVzFrO4wgFvqm29mbECg99CAoNlauKExE29etlJOLG/cXNPEy7D8w2zu5aQhvaFnf/yDRaYzbArlk2N54VUT+GQGNmDJxx7Tnkyg0VsqUcFyIYl5XSJ8wWopdYrlz4wOBNgMzM4V6Kz4GAo/uQYA44d62+CeFJ1M3TsHyfEoCcvVsoaUzx088rdrSJqpU0+nAsfx4hKfDx4PuJMZqjp4l4//33sWjRIuTk5ODoo4/GYYcdhi5duuDcc8+VaXr16oVevXq1opcEQRCEycKH/oCD9lkNAOjSvRoAULMzFx9tPhEjbngaoxNJvHHLlSg7GKjv0qc1XSWIBtEgoUddVvSqq67Cz3/+c9x4443N5VsnJiqOoblssCx6dZkS2GQNIDZoLGM1o8rMVEDj2ypq8EwmLzKuQGScU7dsWtbqU0xnOta+5XIbd0Nky3urb9k9zlRnU+RSl4fX8mdx+bJqa6aLF6otad/ShpkEIOsdJjvjmlwQMirFGptdu5d2n0ylQumrc/W8IkiEiogSwVTxJOYeE20r5oMxxRp5XVUB1BBDVTvqifD15aF2j/tK0jUxFog1sm6GrCaOM+XZdPzhU1x3SG0nQ2cJ7DI3mItHeGKKT8oQK8a40iBOOI/SsHIAJFOfHK6l4+BGWzNpQ0QEiTZxzPF3RIO55557cPXVV6OoqAiJRALbt2/HkCFD8MQTT8iVPwmCIIi2xctXX4KhJfMxdv+v4RzsvUhxF1jzwYE45Ja3MSo3HwBQW7MLg3KfQ21lLkbPursVPSaIhtGgmO2bbroJBx98MH7xi1/g4IMPJpGn2VHlgBYq21Qhgl5JzKauy5SNjKGcN5daCqXJ5HvI0Qib2RB0/0xRIj6XMkQl4rzNQzfifFzN1PRxs2vFtjqzl+8Ym9UHDiWUQr8+cW1msyF8kUNlWLRv1rbwd7j1pFGmsjnMm6PEcexpMiEHxTBl8/0RQ32sm7rKluUR0FrLaIyo6+moF9RoXJahnnL4VkT7RT0LUmA00mvHzPLkTRvRnly5ny3CCfMroMo90q9MD4ttnJqfkFkqLZ5Nb+FzFsoq7XLlXjCL5kH5DOHPIiFn/gZvxS9HC2MK/BQrcwHMj8/hyvOvN6qwJv+vVVHk5eCut4XbhsiGRx99FMuXL8eePXtw66234rbbbsP27dvx9ddf49NPP8Wpp56KcePG4T//+U9ru0oQBEEozLv9V9gypy++MfJp9Dx4K5yUi+qvu+DjlQMABnQv2Y7/3PYTbHx/Bd645UrsuOdQlPbbhqVrjkV+YdfWdp8gsqbBQ7fEsqIPPfRQc/hDSJr75Ts05iSDyTjZI6qzEFVgNhFLPMafOF/2pt14xOd467YuZFw+te+rCj4iakFNH1WW6FSrfVizfxtlP2oeGr1rabcZdBrDJcS1vLVD7J9Q28MsyBZ9JM/pQRohR1xuV7JFPzoqkMGJkb+1LJZoIc45XD+VeS3BYY+iYWZxXL/9GQvXUREZwINhQGbh1rl4oIhLUeKkJY80LaJ3XMSKUIqr2r1jbXbzODfKdl2oQS/SR25Jq5TpTXsTVVEe+83FuTefjnbceIiZMQ7SK48r1zR4prn/f6YWxJXnjXlLocOIBGJGwxgDz7zRXeqNpJQv7zsj/Mm7Hsxvt8YK452bX//611i7di0Ab4n6pUuX4p577sHw4cMxbNgw3HbbbejXrx+uvfZaEnsIgiDaAC/+7AYczp/GCQM3yYVF0rUO3vvsZBx18//DIMawcMaZGDVwEY4tfBT44FGUHQzUVubiP8tOwnF3Pd/KNSCIhkGrbu0FzbPqVhZpGMI9xqyJXtknzqS36las7BB9LrQ0VESdzB2Wiu7Fx8IauOoW4LULh5NIWgWTaBHF67ilGJAwTGZTThLBqlsm0a3tPbLJDNW0xjAwbzWhZMxCaFEdcQBIWG1yNUk4G/Pax0lYBIsM+iID5CpNkemYpa0ZR07SMl+OUk5c9E7cqltRghQA5KZYeLlqZs+j+QIgZa66FSO2gAWPY06KeRPqMkvamPprq27FXAe1OsJmXo73FRRu9wh7fvmOsepW5P3AdD/B0sjPE9MTK8OumJHP4kvKSSOZ1MOEbM+wepw5QMJxkZ9bL+fB0eqnXSJjCBY4clIukglj7humpvf9Ub7GHcaRk6xHXm6tn5BL20F9g7AnuTqZfyw3dzcSjmLTKEMTmIRNh6Oysg69jlhCKxg1gt27d2PlypUYN24czjrrLKxbtw4rV67E7t27sd9+++GAAw7AokWL8Mwzz+Cwww7D/vvv39oudwho1S2CIBrCy/9zPsYfOR+pLntk92nn5q5Yu+NYHHXz3+SCBoKaqkq8PWc6ktUbUd+lD0bPupsieYgmoyX/DWtwRA/RkmQjajSWiN/hI002VA+M890oK9RDjArViPNjb9qKIUqsiPu1mymbdM3oo4fLC46JYITwVKiBLzaxRnb1WHxrmOfkr/uwR3rwiHxRc6yovqr5bTZdZi8btqgbFtjJFGPFEOq7ykl3XdcencNFPczbkOl/o5D1VPILuVxE9DAjg7RpqYC4/fWW1OtlvoSAexFLwqYZ0SMDT2IiiIz5esPXjxnXHmo9AeZCm4CcC5+NZ0AestzQ2jn1tBI543KAcVezx4xktnA5JiJdGFPqxqWjZl3Vz95QTB6kN5GVhbx2uujEdZu6t8FnrtSfA5y70Gd50o1zLuYbEhNFm5Uwv11Mvz1DcvUx7t0/ROPIy8vDqFGjcMwxx2Do0KH4y1/+Atd1sXr1aqxYsQILFy7EggULcNFFF2H79u0oLCxERUVFa7tNEATR4Umn03h55nUY3uU5fGPcZkXgKcT6gh9h6PRfYnTEC19+YVdaQp3oEOy10HP00UfjlVdeoV9VmgWz99KURJQZadImO2RTZlQeo4cIhHvC1nEVTd0W8eWJWTXsObjWhVPnbI0bzmJGHoRbllk+BTYzeZ3pKkg/ePhgnLbmdRG5th/ngHretmR9tjYtWbKzKebCiclr+zdeXe48wox2QBfCmB74Zquj7Zg2AijstOYP04Pr5BLgMbZsu6H+PQvXW+ybdRJzDoXKtjSaJtpl0BTUrx+1fOY4gGOIikyPstFcVGxyX7DRRFbOYCqETMkX7DDlGnD9vH+CyWcyEG8499Qi7ztBFMCUycyDbwrt2jFH+8YJizn6KmChr0x/KJnwDNrQriATcwIfGE3GvNfceeedGD9+PD799FNcfvnlGDp0KPr164fly5ejd+/e+OKLL/DFF1/ggw8+aG1XCYIgOjTpdBqLrjgJwwetxinDKuW7xO6dufhgw9EYPfsFlLauiwTRYuy10LNkyRLs3r07JPRUVFTg1ltvxe233763JjoZLfXSbenlZ50+DrOsiHyhJXNYOG2cApA5YZboXVu7t+GjZqBIQwbSuVC7eeHf3vUoAPN4lEfhc1HpbTZlpI+RT+2EBhITt/og83MlvZ/XGtGTtU17PTLaNCJ64mTT2KXCLfaZckCuPgURXcPCGdRMmpoRaAahiB5AD5lR8rtKuWZET5RuatYtNMIssnF1e2J1b9uKZqZJbXqckNIXLQ4BAFcflLQbXv5dGDT8cJzgmKfXmEbty4lz+NFDqgNa2Uy587lmmjm6kMTUG5KFo2u0uXs4ZCSUjOhR2kyrN2NwXc8T5sAQcTiCbyIOcyk2IY6Be1UTQ7q4azQg0WCGDRuGZcuW4fLLL8eYMWNklFQymcSjjz4KAOjbty/69u3bmm4SBEG0a+KGVNXX1+Olq/4Hxx34EsaNDyInt24oxbZ+V2HwFddhdGs5ThCtRKOFnm9961sYOXIkGGPYsmULevTooZ2vrq7Gr3/9axJ6GkzcS3dTikAZygr1aKPSZxJ2IiQQmczoYauHosIqogvLNkNkejO3+GVc95KHLLoAEjH+aoEClmNRnsRKUKL/aIuiiPgc5YMNrf8YOsPD6cy8ygmHZ7ZpBC7EYtbPdpuqET0Z69mAvq5VKGGBqBA5L5BN5BB6AIcylVU4oc1mMNQsJqLHUoZIYxVqLDdLKCqKBZva7lF2NVwAyhxPIZFP/GXmX6bY5pHXXN6ZqtbGzYgbpqlaqk3NPvOiiISPnn0lEkaINUakESAmAueak1H1ZOYBeSG5IlL54hLz66E8nMFqZUwRsXhgU94X/jHmCz7+SYroaRoOPPBAzJs3D5s3b8aSJUtQW1uLsWPHkrhDEATRBIhJkscdViuP1c59Bm98MAa7duVg5AHLcdqx24JzVTlYWfF9HDXztyhrDYcJog3QaKGnf//+eOGFF8A5x9ChQ9GtWzcMHToUQ4cOxbBhw7BmzRr06tWrKX3tBLTgC3eDTUWpGGpvMXKNoxjbqqKUTURPbGGNID6iJ+hmWTqGDfDCFnwgfn/nyr5eVryYko09vRR/n8fZ9PftilfsHaB3SbU+sYzoMX3LxqYN866RZao2lYieKJvWFbYyNLa8lkrUiIiYCEX02Ipn4Towx641qWXLJdGFH1IkMiJ6LNfKXL0sk/AkD4jIHdVncUy5j1QfNZu61qEPx1LywmgTNdpLHJB15ErsipJItKNakCdqeI5wxhDMzRNkFPeqVk8OOHBliE9wj4mCAwFI6jwsEIQcptsI2o7714LbxTY/ikgMkeRMHbrFZDs4yjHHCXzy0qjr+Pk24Q8jY1weEw3qUkRPk9KzZ0+cddZZre0GQRBEh2HhjDNx9Ij52LahFB+Vn4ODz7sUa/7yIEZ0+yuOGb1Qe/fY9FkP7DhgOg6/bBqOaj2XCaJN0Nilm3DXXXfhzTffRDKZxJIlS/Dwww/jmGOOwSeffIKbbroJf/rTn/CrX/2qKX3tBPAstqYyxbMzJ01GdV6Vn/etmS35QslYeF/0rjJXxOZso5EdNegPB5P/hS2Lz5kEAmbZxHFH2YLzYZvWQhtoU0QR2G0qHX5TlRKdYYthW120MnnMuQw2M1U9qkyHeXV1WPDZ3Br7mGlliM8OZHRN3Kb6L8UvV/XdErESUZaIWmJOsNnqa7Np1lPb9RPJctW2dCy+OPpxRxE9VKNcH02kCTsiqf1a+c+Cb99x9DqLTftKEV8r/tgoBn9IFA9fZJFf3KTMgW+IyWvqCD8YC54dcWOzoBouxFA3rlVSPNNiT5Ql64Zg9TTmML9NFfsOl34E5XpCjetyb14gQzVjEHMA6d8ojDHfhgOHIno6HNu2bcOkSZNQVFSEkpISXHLJJaiqqorNs3v3bkyZMgXdunVDYWEhzjvvPGzevFlLs379epx22mkoKChAjx49cN1116G+vl5L8/rrr+PII49Ebm4uDjroIDz++OPa+QceeABHHHEEioqKUFRUhLFjx+Lll19uknoTBNHxqKmqxKiBi7BtQylKpq3BqGmzseKuWzCs8Dl06b5L/qi0dcM+WN/jEfT96Wc4/LvTWtttgmgT7PUcPdXV1UilvKXF6VesvaWFX7gbZC5CtNHjEmLOx9k1y+YR6WILaRJU4ca0EGdRiBlRiUQXLMpmVNZMNjNhs+nyDDZZdNkRCxTIsswutBQyWGabdoMRx5WyzBlXRBaXe9EWsTYb2ujCtq1hOTJG9Kj1VP2KiugBoM8/ZPGDc0VAYfoTGWfTdFPb9ROqwows27cpAkGYOObvMKMwbSUuW0SPYVZEAam+cnAlgilcB1Gu/Tp7jnN1uJJmMain8N076nrz11iUMQ5j/iHo9Waakqjn9tpDqQ/ENeJwuQtwrkQXKfm5H4Gmtq1ycW3PEPcr4wk+bvD9xjmY35AU0dPxmDRpEjZt2oR58+ahrq4OF198MS677DLMnTs3Ms/VV1+NF198EX/7299QXFyMqVOn4txzz8Vbb70FwJvg9LTTTkNZWRn+85//YNOmTbjooouQSqUwe/ZsAMBnn32G0047DZdffjmeeuopzJ8/Hz/60Y/Qq1cvTJw4EYA3V9Ftt92Ggw8+GJxzPPHEEzjrrLPw3nvv4bDDDmv+xiEIol3x9pzpGHdYLVZ8NA4H3jIMfQ7cgokn1QHw/u2u+roLunavxuqKiRg/4YJW9pYg2haM09qqjaaiogLFxcXAgG8CTqoRJTRSrLAteZM1Il8DbbNERPxXpnIcKJOPNMA289q0wU3k994d02amPB6JRDLijNJxtuROMo5EQj8m0pnNpuZPgCM3aS836pggyXhsNfWIJMUm40gmMpdvdtgBICmiJ+QRHs6jZmNBvkSCxdqMEiBSWbaPnp8jJ8kyCgvW8hiQSkarXZo+ZCTLyWFwLD3ukPhhKTeVcvxz4YSqTbP4nBzAYYlQetOBUFtF1JNB9zc0lzGA3Bwgkc2qW0bxCcaRTIWSBHbU+mqCRj0KcrleP+OzZlrZTzppJJOBhKvWL7RCmlJmgrkoyE9D3ON6/bgUZ9RnQJSXStYjmXC1+okH0lGXdjeuV06qDnm5tUrb6+Wb+wCXQ7dycvYg4X8O2pLLNPqlCYaEVVbXodeQxdi5cyetntkB+OijjzB48GAsXboUI0eOBAC88sor+OY3v4kvvvgCvXv3DuXZuXMnunfvjrlz5+Jb3/oWAGD16tUYNGgQFi9ejDFjxuDll1/G6aefji+//BI9e/YEADz44IOYOXMmtm7dipycHMycORMvvviitrrYBRdcgB07duCVV16J9Lm0tBR33HEHLrnkkqzqKN696J4liI7PW1ccizHj3kNtdQo5XQKBZ/0nfZF/2u/g5nZH2QfHYPE743Ds3dHfMwTRVmjJf8MaPXSLaA5YlltrmMxGD8zSV8Ytm5mVx1S1+drHjA6xRaHYWsLmgcjrKptthBA30mc5eihjTdWyVB9ERI/Vpl8Ba7SLOO7FV/j/hcuw1plltmm9fMp+lJ0om64feSIScGWTxTN9y+b20XzwI1uELe5yzY5tdSobqiBlli83pV6uaddIrwVpRD2G5o3qP4qyrfyG5K6+uVyvt7o1pJ4mwqasg6vWkYWupbzQUUKVapMHZzkYOPP+ulxsfn3TXrmeH64/JCq8MT8R456AIod6cu9/MtKGQQ4jk767zC9fLdP17PE03JA9aKtr6cPoggdHswkvr6i3135+eeZ/rn0VMqJ9snjxYpSUlEiRBwAmTJgAx3Hw9ttvW/MsW7YMdXV1mDBhgjw2cOBA9O/fH4sXL5blDhkyRIo8ADBx4kRUVFTgww8/lGnUMkQaUYZJOp3G008/jerqaowdOzayTnv27EFFRYW2EQTRcUmn0/jXj07H1jn9cNSY/wMA5HSpg5tm2LmpCB8X3IsDbl6DXqO+gU+efQQAUN+lT2u6TBBtkr0eukU0Jdl074EmFXuyNhllM1MBlnw85pyWjkfYzWSz8fqlLfomRnvQPDK9YtAWGLKWofZTG1JT0W/MdCeYAQtAMH+KFa790RKKISfZ2tRsx+l2GWxmc7dbbcbUM1Z8yfLxkjZVf5llTiUm+//R+KtRWU2z8Edz0mNrPnGDWOxy9bwli9UW04+ZU7tk86hyY9UtkcSMrDH35UpTgZahCI+WOih2uQvlX7rgZnPM/ELklPVh/mTdTLMJcHBNsXJlu3mnmb9MeuCHyCuib6RZppUOhgQclpZ21FW3uFRFuTji2RQ+cdebvFnYZMImA+CCKXmZPzE1ADjWGcmJ9kp5eXloFdRkMonS0lKUl5dH5snJyUFJSYl2vGfPnjJPeXm5JvKI8+JcXJqKigrU1NQgPz8fALBy5UqMHTsWu3fvRmFhIZ599lkMHjw4sk5z5szBLbfckqHmBEG0d3Zs3YY3Z/4Pjhn8JiacGAi63AVqdubD+c57KN23H0r947U1uzAo9znUVuZi9Ky7W8VngmjL0Btem8QMcYgLeWgpk7ZeaqZMGXyNjehBRM8xG5vZqldhzJw2AYdbjkfVPA0ZJCA3tQy1hlFiUWPvAG5sakSPSeTkwVAuBfMjA2DbAt9D9iz1apBNS13Ma2C12ZB6ik1Zkj3b9tWja8JRTshkT0z+G1VHEQkSadPSJsYNwyz7cgqZiE2WKz67fpSNf0GjIouCxo7ww7wmqg1/E3Zc/zN3mdZAWkSP5YHkpk1NRfQ2FwxprkT1+BE9MnqJw4/Q0jeAg3E32GA8k4yDMUcTc0REm+uyIKJHRvaIjYMjLaNxxDFROcb8DeL66ZY9m6LS4tr5EzW7zC+PBbZkW1NET3vg+uuv1yfwtmyrV69ubTez4tBDD8WKFSvw9ttv44orrsDkyZOxatWqyPSzZs3Czp075bZhw4YW9JYgiOZmydy/4b0fD4fz1CCcOuElFPX2RJ763UksX/sN/Gf5icgvqUH1H47GG7dciY3vr8Abt1yJHfccitJ+27B0zbHIL+zayrUgiLYHRfS0SeKEimYSejKatNnNRlCJ8ZfHnBe9v1DPcC9tNjBntqtvyw4509NEzYgk9k2xyFZuFFz8L6YJzXKj9LPICBfDUa8bmfkahGxa0mRr0ywvk035tyH1lAnijVmvI1NtWiJ6XP1W13QH8cGJMWvcU2Gblrw8vGuKijKNcsJWP/Xxl7ZY2G4mHwBPuAlFuZn1Y7a/PJRWFVJCRaqij1b5oBz5bJsClBAbHYApoUVMFuwN/QqOhyVaEdEjskgBU7mIzG9IGa0EBnBfrBGRc0oGKfrIdte/PUREj7TpeKXL9jPuGekHRfS0C6655hr84Ac/iE1zwAEHoKysDFu2bNGO19fXY9u2bSgrK7PmKysrQ21tLXbs2KFF9WzevFnmKSsrwzvvvKPlE6tyqWnMlbo2b96MoqIiGc0DADk5OTjooIMAACNGjMDSpUtxzz334KGHHrL6l5ubi9zc3Ni6EwTR/nj1nt/j4C23Y+TgcrBjvWM8zfDFZ72we9CVGPi9qzDKT7twxpkYNXARji18FPjgUZQdDNRW5uI/y07CcXc932p1IIi2TId4w7vtttvAGMP06dPlsaZaKrT1sPwc3qQijy3coSEmoxI30N+oiB5pI1uapn2iIkVsx0zr4qTZoY4KPhD9T7Vck7iWzfTw2my7/glTYMoYXSPqyBER0SMWBddtRwRcNMxmTHlqmWZ9XbOeFlvCiHosrl2tNnl0RI+4UOoS5dKmujS4VicjKkhEsRjHoiJ6sn00Ne1D2OCBPc2mjP7wbDLFB3X1KCtGO0c1Kjd8UG2KiB5u2JPtYxbJoEX1eDu+8AHvJFc2EdWj2XahzdFjbSg5501gF9CXTA+eG3FP8qBeii1vhS83OC9thuPigmFcTNY/WIKeyXbm4MGcP7I+ZvtSRE97oHv37hg4cGDslpOTg7Fjx2LHjh1YtmyZzLtgwQK4rovRo0dbyx4xYgRSqRTmz58vj61Zswbr16+Xc+eMHTsWK1eu1ESkefPmoaioSA67Gjt2rFaGSBM3/w7g3YN79uxpWIMQBNEuqd2zB89d9gOs+9+DcXzJNeh7WDkY8/49WrvqANQc/zb2v3EtBp5/lZbvuLueB763Hos+vACL3xmHRR9eAExaRyIPQcTQ7iN6li5dioceeghHHHGEdrwplgptXSK7TWgawcdSRqxJIUtkkziDnZDNiDRx65XbC2pA+giTxmeztNjSo6oR8RcIxJ6o7HGt7AJIxDgU6TuzXEml88ysmfR9lsE7W7ZG24w/HG3TrKfFXVVIguVzRjuGjVB0jSKYhNpdvWW1c0ZNmHFUSSsFM9Om+jmq4dQVyewmg8+qTeYV6xh+RV4fbv0YbVMR+2TdRaQKM9LHfXWoRuXENcbVtdryz/kRPcF5pTHNayKLE1KdEtETV08ZueP/j3vrp3vXVMh/qgxolhesnsU5vIgejmC4mn/SXH1OjZKyrRJHtF8GDRqEU045BZdeeikefPBB1NXVYerUqbjgggvkilsbN27ESSedhCeffBJHHXUUiouLcckll2DGjBkoLS1FUVERrrzySowdOxZjxowBAJx88skYPHgwLrzwQvzqV79CeXk5fvazn2HKlCky2ubyyy/H7373O/zkJz/BD3/4QyxYsAB//etf8eKLL0r/Zs2ahVNPPRX9+/dHZWUl5s6di9dffx3/+te/Wr6xCIJoEmqqd+HF6+9A7aZy5PQqw2m3XYf8LgVams2ffoF1t5+DYWPW4PTj0/Lfrl3b8/HZlwMw4OrnMHBS/ITK+YVdMf7WR5qrGgTR4WjXQk9VVRUmTZqEhx9+GL/85S/l8Z07d+KRRx7B3LlzceKJJwIAHnvsMQwaNAhLlizBmDFj8O9//xurVq3Cq6++ip49e2LYsGH4xS9+gZkzZ+Lmm29GTk5OK9WqFV66szKZsTuXbUGWpFG90WzKa9r2Uvvetg4/R3aRNHGSmLovFgSKqsXetHakYMGDc+rwlWzKFJ3mqLJtwoneTW2ETUuZ2dgEl4EcoYmD1YQNvYO068V1u1yxqU6hYhOhDDdirxdDUK6wKdtV7Mcok9Y6qgKM8lnzxbTJlIgarrdrNoJdlPCm2tTsy4p6R9XRnFw5r0oigHINIEQP5t233Bha59eL+w8iA7zoHQYwEWUDAA7XdWdFpIQi1vhSDJj/ZAd6CpeKkjctMvdtM3+1Lj+qJuH69eTKPaLfGb578rj4PxO2TTEL3jw9Ig8HoI7WcrNZFo5oVzz11FOYOnUqTjrpJDiOg/POOw/33nuvPF9XV4c1a9Zg165d8thvfvMbmXbPnj2YOHEi7r//fnk+kUjghRdewBVXXIGxY8eiS5cumDx5Mn7+85/LNAMGDMCLL76Iq6++Gvfccw/69u2LP/zhD5g4caJMs2XLFlx00UXYtGkTiouLccQRR+Bf//oXvvGNbzRzqxAE0Rz8+XvTcFjFCoztsx2JPvVI705i7XcW4MOiYfju3Hvw7nP/Qu0LN+OIoZ9gxPHBd87XG4vxVfcfYdCPb8ER9IMDQTQLjPP2+5Y3efJklJaW4je/+Q3Gjx+PYcOG4e6778aCBQtw0kknYfv27dp48/322w/Tp0/H1VdfjRtvvBHPP/88VqxYIc9/9tlnOOCAA7B8+XIMHz48ZG/Pnj1aeHFFRQX69esHDPgm4KQaUYNsv9gsvcK4dYpjUfM1RJhxYnrLceU5AItae0rNausVpuzHM/rAACcLm1oej0QiGY5msHw2STGOhDGRr63PbfbFE+DISUaXH2czx+H2zn1MXgYgwTiSiezSmidyEvox23w9ts5+ygEcI/woQpMI20xGpIu1yZFKMusta/PPPJaTjFeApOxppMlNAcx4Nm31MtuWMSAnGfFsmj4ap3JzAMec+cb8yrDUJZEAkso1sUaeWNqHAcjNVb4OlKbK1LZOgiOVDKez7TNtvx75eVy7nlH+mXVJJtNIJtTIGa7lYUoZ6tw1jpNGQW46iCbSMqk+6uUBLlKpeiQSXCsbSjnSlj9sVZSTTNSjIK8uqBfTh1V5w/9s0T0cubm7wZgro3W09mHesxp8XwTCT2VVHXodsQQ7d+5EUVERCKKtU1FRgeLiYrpnCaKV+fP3pmF86asoGrIJeV2D/tHuylxU/7cU+X0qkFtcg0SO92+ZW8+wc2tXrC+6FEde+vOoYgmiQ9OS/4a124iep59+GsuXL8fSpUtD55pqqVCTllvisxWU7YwmYxSF7ApopM29KqDBuIiO2BE/6EcJOCKNOGaL1FEjWwBvYtq4lo2rYaaBbVERSQlusRnRYQ7l53rn0rQh6m2zG2rXLG3aysrGJjhCeqgmWDTB7ROSyZX5UVSDUcKQmqwhET16HhZqXF0kiS5X280mwka1EScmxetkenncuJdtIXGMQZ0LR/oU1sO8LP6OI46LSCs/k5bWt+cF/aihOg44r7dEyAQ+Q4g1Cp6I4wBwlegb07dguXSmjuFLAHL2bgaYBXAZxqTFkclIK/FXH44l5hgK8geCD0M7/q2HIAiCaCVqqndhTNFr6D72c6DXKXCOmImaxH545xc/wqhD30Tp0E3yvaBuVxJrNwzBQVf+Cfvuuz/2bVXPCaLz0C6Fng0bNmDatGmYN28e8vLyWszurFmzMGPGDLkvI3qaHGu3qhntsJhepkiSKUGU7BATeZSxmo2RMjIJUvFkWiUrygs50W4W6VUbou+ZqaaRxAgHcXnMriJT+7dqGdaGCHdsM9llCJaWj7KpihOZyMYmWFiICdXZIkhkEoCsd5h/wJuMWe2ZG/ZZkD7+HtNbJeSTqVQYjWsO87LaixGH5J8YwSaYBDjYV8UaWQP1ubA8I6H7jNmuLw+1u+VWDPJr95YynEnWjavJ5XFHsc8c14sg4rpDIRGN2865hkDEw2Wo8/Mw5a6RFybIo4qrwRLqIqrH+xZRv4M4uNHWfhswX+byHTe/twiCIAgiG168/nacMWIDNlcego2pC1H6qwvRs9/XGDeyRqbhLvDpB/0w4KalGJJPy58TREvTLoWeZcuWYcuWLTjyyCPlsXQ6jYULF+J3v/sd/vWvfzXJUqEmLbvEZ3O/fVvKz6QOxHbjo8ig5sT10mPLzWSzMdIJ0+Y+Ua1klJxiTDLjL1f+7k1ET1w14/xliLAZ06lXC2ZGtICaP8ruXtmMKFOKKxHnnYhObCYBIxOyxrayzYgew2bU/cHMI0Y6Wx0DYYBZy85KqMmiEUJCnFK2zZ4V8+Y3y1dFI8Uv+ew4zLi/9GiySLGCwRJ2FZQcZOOaexxAUnwfWK4F4OksXDmntQ/3InpUS/owR68mci4hXxhzZGiOXmoQicPAhaono+tswqv4vxlSxGV7MyWqiCAIgiAaQnHtCiS71KJuVSWG5V6ExLA0AO/fq83ru6N635NwYJensW7HIBxCIg9BtArtcnn1k046CStXrsSKFSvkNnLkSEyaNEl+boqlQjs23Ngaml7dohYPb0hZljRZqRy2cjKJRHFlBp+ZUq762fzPnKfGtKx2Hs2IGbUFXXDrZvtPDr+IIJMoFdhUNm4s2c2NlvWFE70VlDpxP8jAcln1ejbMJowi5Tkeb1MrU9lc7i8T7trPZxPRY3UKAOfcWieXw19CW7dlayNveW01keV6Km0FzpUlusObWBJd9QlQRBXLFikqKb6Kx97WhmpZXC3TrknJe0s3oBTjcjDubaLifiCMvP4hP4SdyImsQnewJjBxzsPt4NuTAhTXI424n0hZcF1u6v9Vp716ecKLGxrrxb2VuRiXoo6+cHuwvHoggUGWzyGuN1cL9XIy5glo1u9fgiAIggiz4HcP4vOfHoSjhy0CAPQbvAmJ3DQ4Byo2d0X5AX9Gn1mfY82n+wMAupa11uI2BEG0y4ierl274vDDD9eOdenSBd26dZPHm2Kp0NZFvHw316+tEWEgkSYbHXcSn95qMy5UIxtbDe24cIQ1T2Y3bz3GrXvZ5NUjXcI57LX1LHCOyDm543x34jrb9uJkoUIIMWY5CT4ZZZid+4baVEe/REammDb9ncZG9GQ9ZYkazSEEAhYxAbRUBiKLUD6z0MnQ9WTqZMjMqmVERdhIUSLmRuXKByHuAcaS6o79WlvtKUoIN8+px1m4HMdTJZTrzOX/zWXDTZtCCNOUI2bmMNQ2UTfGIAZKSZvGNee+XKtcClm27RtELpmu2BHeOUxE9IiKevnVOXQ8m1yzpQ/rEh+DMphsUOU59ZvFYWEfCYIgCEKlcnsF5v3kWgwtWYRxQ76AUxqMF9+9PR9ra07BYT/+LfYp2Af7AKjdvRu9P38JGAIMu/hHrec4QXRy2qXQkw1NsVRo69OcL+C23mxcr7uh4kmc70ZZoR4iV443xI/GtlfQCdJLiF5GXCAmezWrENGv17p+ZiBFcNSeN3S8EdXl8KI6xLLu5smQXmB2JhUNzholYBQqJsB1mdnVDGzaJmnWltBG+HOsTf+v6+rLSKvnrSJYqOOcAVUfEDoC5/7y2UZSHlEu0+8H9ZMmclnGD7mKTUvQRkgICcrS/1o1EOGbcU+rcxUzVxc+zEKYecgiRnGEbcC3I/K4HGDc1ezJdOrcRIYw5oiymSq5iFAf2/MelMN9JUQsg25DzsakCGFyqBTjuk3tr/JZqacnpLrGDW2IyDwYGigEIFVcCn+7WOzJZ5n7NimihyAIggjz0s03Yxh7HIXFNTjrhCp5nLvA9vJi5JbUgtWksOOVLXhx68MYedE5ePfJZ1Gy7FWMOOVzVFV3RVG/E1uxBgTRuWnXy6u3NmJ5tPa5vHoG26HTiQzLq8fIEtbl1TP99A94y6tna0fFsffwIwnKTCSSoTOmxWA/6CwmHXjLq0emDT6rniXhLa+eUdQxbMK3aatmhJ4g/4rl1a2CToQfgU0ub71IO5ayxPLqDbXJACST+n52NjlyUswqImh5I4ynIpZXt92Oahm5KaaveGSro+UYA5BKWp5N20emP4o5KQaHObG2bLsOA5LJzL6KZ0AV+/JS4XvPfHzlceVYwuHa9TTrpX5UJwpmThoFubpw4RgNqO0qO6lE2lvq3FIvVVQ265lIuMjPrdcjZtQHSaSXETaBoJOTTCORDA/98uyoEy/rEyLnJOuQl1urVMY17lEOx9EjfEQdcnP3wGFpw0fvnGOExqllVFbVotcQWl6daD/Q8uoE0XxsWV+Ot26cgRG9l6L34C/lOx/nwNcbS/FV8Tk4ZPJspPIKwTf8A+mF30PlxhJUf9wddRV5SBXtRpdDtqJrnx1IHDcXrN9ZrVshgmhj0PLqnZpsxZ+9xTqAIsv0UedtskgEoVW81F+i49yKiuvIlCY77DnDR82aZlyUTMGFXlvzt3evfLvNbAiiC8LHRKRDyCaXQQXhDjMz9o0yVf+UYAk5FAeIiOjJwqZpo0E2jYgeW7vY6hXX0KEyeBDlwiCiayIaybzF/XoKgUSN6AnKtyg9SjQPpM2gXFl8hCrGIs5bH0ke+Cbr6dfZtqKZaVKbAsby9RA5tM4vUJpIu170jiKwuGp0ivq1oYSrMeX/QWSRuf6bUYRIyF3jhhNPpff0aFVzjPqzwH8mJ5FSbXGtnvLacW9WLqa0mbbsPWNwXV8scoxIMT+EKyg7qGcwL5MfxeMGIhF3jQtJEARBdDr+ceWPcXSvfyK/y26cefIueZynGTav74biSc+h5/7D0VPJw/qdhcRxc9F12fUo6rs2yFOwHxIj7ieRhyBaGRJ62hxxL91NKQJlKCvUa4tKn0lkiZlEJpTX+Fne1nOMLizCn0ywmL2goxjWAYJhXZrYEGFe/ZGdWY5FeaIfs/SsI2zG6RYMuh9RaB1R8ZErS1VHlC+PKSccntmmEbiQXTpl3xQNHCc4ltFuA/q6Vnv+rZtx1a0Iu5xDCXwLJ7TZVOcFUsUxa12NR4shQqixPZJmucYNJOoWZVfDhbfUnLDJLTah1k38deTy4N4+t7apLFfV2mRFeXBSuYfVttXqwhzACYZgeceCB05G5jhB2ZpYBK41mu2ZVG0GFRarfXFlyF4QNaSvpMWNuZbUujKtbDGAzYsg4tIwi43UJAiCINojNVWVeHvOdCSrN6K+Sx+MnnU38gv11a8+X/kx3r99JkYeuAKnjdkSDA12gc0beqCq//dxyKSb0cexReZ7sH5nIdHndGDrW+A15WD5ZUD3Y8Bi8hAE0TKQ0NMmaYkX76iImrj0tjRqOTZRJ67Hbgo0RtqsmmFv20qvV3SckaVjqKQTQQRR3sggAeOzOldOOOIkbDNbVF9sUTFmjIFVrLH0TKMmdlXLCEVGwB7Rk61Nk8i7xnBEjeiJiuaxLsiUobHl9VMjLbiIejEieiyY8w8xQEaE6GdE3z1Y5lu1qc4LpEX0WAQfdUJleS5GeJIHROQO09vZnN84iF4xbOpah33yZOPBUTWSwKar15H73zaqPSgRXDKCxRPChKLEhUCklC7LVR5M79n0lytj6mlRcJDXUUK6pAamOafa4XJC5dA9BF+U8h3iHJ6/xnXiUIfuMW1FrvCzyf225P78zFz6Ae4JPhTRQxAE0bFYOONMjBq4COMOq5XHauc+g4Wrx+GYO57Fi/8zCWMPfBM9impw2jd3yzRuPcP6T/ug79SX0WffA7K2x5wE0PO4Fum9EASRPST0tEniXryb6ms0i3K03nFUevW4ze+YuXJkcpuigCwjehoqWMVjE3G8/bBsojaPCyCRwXR02VFe6yv+xBbagNMMcTYVLGpREIGhdyhFsqirnXVET5xCZUsfZ5MpEROZbDaQ0LAn/38Njujx4S5iI3psNsUxFiWg2PxWd4x6a4+bsBmxspY6r4w4EanRqSKOEtFjE98YgnLVNmNMRKdwTVAxhR7bVwrnHBACDwds4ywds/2EauQIYUhEEAmZKIiwMUUqDk9kZAl9CJYUmPyHSIiMav0d/4b1Inn0iB4RSafds8wXhDgH5wzasDTul6NGIan3kl9RiughCILoOCyccSaOHjEf2zaU4qPyc3DweZdi7d8fxqDU33H0iPmovrcXvnlCtfz3wE0zbFzfC+5hP8aAs6bhwEbPAUoQRFuDhJ42RUu+cHNYe45x6a3+aXEJMecVQskievcZ3Wv69lKja8KlR8dqZCNiROkJajBBON/e/dpuy+3yDDajxJGYSoqyzNlPRHI1oifKppWYRs1ok3sCU6zNKCUsA9Yp7EWHO6YAc44jKf6FInqChNr8QxY/OEew+hQLCyhRNuOEJ5HQXNVKRu/wYJ4gITxwpRBVO1CHNEYKUkrUkIgC0ltBGaKkNgQL/kbdt8wPZ+LqcCXNcFBPBsiV4gDXi3ZhelrFXasIJmwKscbM7eUL6sMhrhGHy12AcxlFpeXnviClti0LWsocUilzc2+pd8bc4PuNczC/oV2K6CEIgugQ1FRVYtTARdi2oRQl09ZgVBpYOPVbOPLglehaWgPGgC49qwEA6ToHn3xyEA6Z+Qr2L+yZoWSCINojJPS0KbJ94W4KgYNlbw7IsieeZURPVnZZFhE9toL2rm3i+/7RZXPoc/bYPDL/ZrJpT61bjcsVldNhGWyq4oh5eUNKQTiqx/wMeJ392N+IbJ1387iFOJsOy7BQXBblR9lUm0DzIcuIHjOJiHSJus9Cc7mY57KI6LHqq5ZdUyCyrVzGzLZV2kGzYxiNjejh0CKI9GFg6hAl5bxZvr7rD43kUi1j5lgpsxym/GGOvzqViKRhxnlXy6PWx4uw4dp9Iv54dWSh44C3ehpTFCshAGlRdEoVuBLpw7kxNM2fBFpGfRmNJaKFnAatUEgQBEG0Vd6eMx3jDqvF8sVDsM+VJ2Dg4HX4xsmV8rxbz+AkOZZ/MBYjb52HwQ36wZcgiPYGCT1tilb4ws3aZEbVJeJ8NhE9EfYi0zVfO7mwCxKm9pCtWMONz1m2UMhmQ4nTL1weY1Pvf4YcYjGlm6uJKdnAmX7eZtPuUMy5DDZdv48ea7MRjcyNnVAHP6LQyMgaBEJN5HUTGSwJZESPITiYIlfo/osSpJRyTadE1I4a0SNOMHgRJ6aLtnpGIaaoYcI+E9VmyqTKeoXMMkPPKgvSShmEGXYMUYYD4I4L1+XWSBkwro0AM9s1GGqmt7sXKMQg49Ck7350DXfhqr6IiCPOg+FlmtgUpFQjmoKVu/SVwoKMwcXlrn0VMoIgCKJ9kE6n8fy0aTjhgBcAABNOXAiWCP6Rqt+TwOrPh2Pfs2ajbPXJ2LMrqQwNJgiio0JCT5uiJSN6GmoyG5tZRtjwmHNmOmuSOKf3rm3MfqhNwIkSa7jhL4O2wJC1DDVLozzPoL/ZynZYjE11OImZgEPOgBs2GY7s0Tr4PLPNUAHKuWwlRs0mC9+2Zjm2IVjZvvtIm+o1j4voifNDiXQJZbeIb2ZkSybhKmRP/k8/GRqSxPTP4k8oose0EeEHN1bdEjbN6KBQ2SKaRxM57NfKfCS4C+VfuuAGd8z8Sht6dXTgOIGCFjwPHGByzS0Art5unMkJss355h1/hS4zokc8UwwJOE5a+qq+iOv3qhdDyPy28c5zZUExJh0SUUn6PGNBnRhF9BAEQbQ70uk0/vXrP6DLqqcw+JDPcfrobXB8cYclOHbtyMf6bYNRdvYN6Hb4NzAUwBu3XImyg4H6Ln1a13mCIFoEEnraJK2gsmc0GRXH0qBCjORR43Xiisumy9/49gt1Ei1WbV6Lzq9J2kgH6CttZSo3IzEJzWgimcWM0BDHLYKCaScILLGLbUwpM8q+rHucPeNEJmnPapMjnDEkIMSfj0PaVNvT5eCGSpFxXkNDcAjV1RfJxDxHIZscwXLiQhjLQvgxVu42TUYeFCKRObWLo17PCAEySgQzBbe0EAb99A5nSpQKAhHFpswa5pm2JF7ggOsnlEc49+rARUQPN+bo8dM7XA4BC4mh8ByTET3+eVE91xWSDlfSiqI5ONJG5JJvR0kXLK+uioqekKOuCsb9Okmb6pLyyqphFNFDEATRfph331Po98lsDDj8C3yjVxpOv+C73a33/lGo2NIFXX/8KQ4r6CLP1dbswqDc51BbmYvRs+5uabcJgmgFSOhpkzRfxErjTdrsZhMOlEmJiBF4giWeGmiz8ZjWMg3jEkRF9BijLUJlZBJ34morozIyRHGYQQtRESvqJLhRjnoyWuZrELIZYS82c5bRPGY2+dcW7ZHJ9QwRUqY/MrJE2rREO7n6rR5qY0WwiBPZtPoxvZ6hfDy8GxIVLXW13afq46/OFWTazeQD4IlDoSg3s37M9peH0saJWZqYaYvoYcqzad6gfr2YA21FKiYLZuBMb01mPMmcBxMfC1+0CCLZjkwTbsAdTZRhSgYulEv/BmL+ilti/h4OVwqwjEPOBcRkFJHioeoHRfQQBEG0aV6+7W6w9/+JIw76HMcP2IrEqLQ8V1udg/WbDkC3b/4U3Uacg0XXnOWtunXvQHy052wcdM4l+OTZRzAo9zmU9tuG/yw7CccVdm3F2hAE0VKQ0NNmaSZBR5JFLy8WW+JG+KyuP7xX5TVNe5nCiy0gxOZxVJ9TjTSJqiE3/prno8jUPbPZ5givRKV15DOUCQ6jk6uftAlYYr+xNjNpM6ZNYcfluhhhddviQzb+aDa5Gt3DtfaxLaGtnbPWwbgTRN/eUGrUiCL1nHU4ks1+YFAtVvfHaFxVe1Xn01HnvLEbsvhmGrXv+gKk94QIoY4xRVi16MFcva5+4zF1+S/RfqIc6Wqww134c/SovqthTSKTkse/1o4U/Lj0QYjBjHFpX7otbqSEL9b4+8x2kRRvvfYREy4H8wIx/4njfj2YHy8Uujc4RfQQBEG0JDVVlXh7znQkqzeivksfjJ51N/Itwsvbz76KrX+9GyeMW4xv9NsN1j84V7c7gZqKfKxjZ2HoVQ/iUCV0+Li7nsfCGWdi1MBFOLbwUeCDR1F2MFBbmeuJPHc93xLVJAiiDUBCT5uluaN6LGXEmrTGBDTOTshmRJqQzYwFNSB9hEnjc7bCi9rpts0LE9X3DokfEeXacAEkMnXkbfvMciWV/iuzZtL3mUydve1G24w/HG3TrKfFXZswla2wJHbMSAntvK9wcUtart6y2jm7AGu7fkJkCdlUP0c1nKoUmvUwP1sEM3PoXeT14daPWhn6QeV+EXZlhIvFR0sZLNQG/pw5xsTKWh2MesEBHD+iJ/BBr7RuWgidHGkeRPmYPqu2gvr5/5MRPYG4JRUZS3mBGCQEPx4IX77qFMzhxKSPqoBEE3ISBEG0DEKAGXdYrTxWO/cZLFw9Dsfd9Tzemvssct64Fd1LKjH0wC1InRGkc9MMG9b1R+6oH6P3Ny5HnpPEPhF2jrvredRUVWKRIShRJA9BdC5I6GlztMJLd1YmM3bnsi3Ikiwu3qXBhWUoMx61723r8HNkGM7FwpbNaBa13IRhM7LcqHMx1YwULJRoEFkXa8fVklUMGYnzybCtd1MbblPaztKmLE+JdIlcZt0USbL0Q7OhnlNshiYPVs1atJy46yX67apN2a5i31TTbIbNikTUI5TfsAnfrtqu1lvRrlkFeXhwPOrZ83Qa76gWUWSUqz1jSrtrq25xfWidsM/9eXx8K15eEdEDgJlhcKpIyYJK+DnhMAeAGzyjfuGceVKLusw7B/PCbgAgkfYjetToG/3KiCGW5lkhSZliVrDUO5fp1dFabsbxjARBEMTesnDGmd6Qqg2l+Kj8HBx83qVY+/eHMSj1DI4eMR9bZ/fF6P22gx0f5EnXOdj6ZSm21B+OIdf9BQfkFWZtL7+wK8bf+kgz1IQgiPYCCT1tjtBv3s1ox9JjikL+mt3QToHFf1sR1mpmI9ZEtVfj2s3sK0d9jvIgk1VbX9xWTlZkMBYpHCknxKANdZLbqLSAGhkg/h/2OpNgFWWT2RJnUXbkcbWePPJUIyVBvxxr+/jl+uqWfGoU8cGcz0kXnIyTplhk7MuIHkNRVKOmxNAqDXNZcpsoY9oyzqtLoAdG1QR6mVHXWjPNjOeEQYoUTrhZZBuHfFdtMrU+0VE9+tw1AHO8OXTUNausPgJKFJELFy4cIe5o6biWj/nL0QXROwkALhhjfr6wr/ZoHG7UTVWivPK9OXsCZ4VNhyJ6CIIgmpWaqkqMGrgI2zaUomTaGvRYtgqf33UpBh+4EV2Ka8AYULr/dgDev6vby4tQ2ediDJj0M/RJFoDWyCIIojGQ0NMmaYkX7ww/tVvTxyVqhM9Z2Wzs+cZ1311Ez30jSrR2Kg3NLE7MMSN64lp2b5ooKiLJX31Tr6etw23Lz4OOZpReF2U3VM8sbZrlZGsTHKEVr7SOeRM8ZmYwBOd6tIRNlLERK6FaInpCuaPqyWLa1+K7zR/1s4wcQrgutrmBogQYaUrXIyw7gVGu3HOmeGbakpFc4riItPIzaWlFtA9DEGnDvdzcrfeieUy4Yt8cDgbAgRfRw7kaweY5wRVBhnM1uocBCddQCHUVi0v1To950qOWFAFIPqs8mBRcpPe/uDhF9BAEQTQrS269CscNqcW6pT1RdeOR2P/ALTjkpD3yPHe995WPPzoAB1w7D92Ly9C9Ff0lCKJjQEJPmyTrkJcmsGEoFFGwTAmihJUGKhHMTJBJzLEVkEmUiibTKllxXljnD8lgQ/TP9iaqJIrI8vwT6hSs2Uf0xEfwxAlWZve0MRE9DbEpOr0qmlBgRqOIcxnsRwlWVpumfTVixFKu3VOLT6ZSYcynaw7zsgo+Nq3XvIdjhCJ1YmSxr4k10IUh5XD4+hkHwteXh9pd0UtCFQndW0q7e3+4mlwedxT7zHH95dbDjaC1jSGQebjaEubBylpq/mCiZvkZjr/Sl//EhEQ1EYkTVJKxIK26VLv+fcT06CHfccbQJIInQRAEobNl/WbM/8WvMbr0Hzhm0CYAwJEnfCTPcw7U1aTwyVfjUDphBso+Ph1fV/bBoOKy1nKZIIgOBgk9bZbmfvu2lB9rMk48aaiv2SgimcSaTNJLY6QTJnOZHVJbaSFNKsKk0cfUoiP2KqKnkdWMlMJiOvWBSWYEj4jefrAXJfc11mYmwSrKpractZktC7tRyH69rWzGYm1G3R/MPGKki73/xNgtZhy3FxVRSAQiEsZIymATT2KKNdJw47M6xEw9KU04XkXkKTFPlK3OShGyvBDMko/LunIASfF9YGkj7XvC6oMX0aNa0odh+fIP8791uHfWE5Z4qEKBqOYtoy6KUIdxce1RFP+3lAXIKCIeGkNIEARBNJb59z+O4v/7LYoKatCr/9c4//gqLbI4Xeeg/Msy1PY6BfudMxP5xX0xBMAbt1yJsoOB+i40SIsgiKYj0wrNbZI5c+Zg1KhR6Nq1K3r06IGzzz4ba9as0dLs3r0bU6ZMQbdu3VBYWIjzzjsPmzdv1tKsX78ep512GgoKCtCjRw9cd911qK+vb8mqtCLc2BqaXt3ciOMNKcuSJiuVw1ZOJpEorszgM1PKVT+L/1x/E9EGAlunU/VY9VBtQVcpU91s/2Vqn1gZjKs2lY0HG+dBOum30gk2PJGJWMRl1evZMJuRVzqDTa1MZXM54LreZjufTUSPVinFLufcWieXA9z1N+W4rY04vMlzg0SW66m0Fbg3SbEoX9ZN3Td8AhQRxLIFIpJRZ9VX4YNrr5tZTxZTnvRHNaC65HIw7m0igolxgLneBos9rhVsv6g8dAED/7wJjA0f/ftN6CueT8E9GB5apV5VxZ6SQZTH4N075g0oom6EqMOU/wD1erKgCkyxpt1s3sYY5BxE2k1IEATRSamp3oX/d+UtmPutK/D/rrwFNdW7Muap3lmFv14zBwsmjcfGnx2E47tOxfDjV+PAUetQ0NMTeXbtzMPXG0tQtzuBHZuKUTb9/3DQD36LVHFfAEBtzS4Myn0OtZW5GD3r7mauJUEQnYl2KfS88cYbmDJlCpYsWYJ58+ahrq4OJ598Mqqrq2Waq6++Gv/85z/xt7/9DW+88Qa+/PJLnHvuufJ8Op3GaaedhtraWvznP//BE088gccffxw33nhja1TJgtELbHKYsWUyaaZXNyfieBZlcVtaca6hfqgxAg1pO27xmYFH2DD/EyVk04xaFAP0FnTArJvdouJ2RI2i7PvBEb5NZWPBJjuXassoFbP6A+VyKs2pltNQm+pIsai7K8qmVqayOcybR8dx7OczTVkSCAj65uVn1jo5juhYh+2Fi/NbVUlguiTK9Eb5MKXjHrEpdVeHWlkblimSgCG4qNcILFwX6bLRLuaNb3tGpHBlXGCHAcxh4MzbtK8OcSNF+MFNa6Fra3mAuChP/Q7wT/l2hZ+c6XE6TPnElP8HV9YJnOeeky5j3vpcjHkTI2s+MkXAErXxJdbQkvOKt8r3KtMuiocQO2O/RIh2y7Zt2zBp0iQUFRWhpKQEl1xyCaqqqmLzNNWPY6+//jqOPPJI5Obm4qCDDsLjjz8eafO2224DYwzTp09vbFUJokn48/emYe13TsXY2r/jxD5vYGzt37H2O6fiz9+bFkr70Vv/h2e+cy6239Ebyb/3wTlDb8Xxpy1F2eBNYAnvh5fqbQVY+/Xp4Ce/i65XfI0e123EOx+MR2m/7dhxz6F445YrsfH9FXjjliux455DUdpvG5auORb5tPw5QRBNCOMdYCbGrVu3okePHnjjjTdw3HHHYefOnejevTvmzp2Lb33rWwCA1atXY9CgQVi8eDHGjBmDl19+Gaeffjq+/PJL9OzZEwDw4IMPYubMmdi6dStycnIy2q2oqEBxcTEw4JuAk2qE55lesCPO8XKDTwAAzr1JREFUMyA0y2zWxOVjMS6JXmWUQ1HHGMAS8S4x+T/DZOZrEGnTyWDTmg9IJNTRjNySQv8sJK4kAxKJcJq4lgGAJDhytAGUus24/EnHmPg3woZJEhzJiEGbIZuKcCJsRi37bPVXyZdI2L2y2VQFkFTMpYy2yZFKsMj2AcK3szrJcDIR/SzEtW1uDsD8Z9NMZwssUYWtZFJ3VrvnYsKMcnMAJ+IZs31NiHonLNck7jqo5/Jyg/lsQuctTSeFpgS3X0+m/dHK8v7UoUteOJ1m3zAqRK1kwkVSzEAOIY4YkVJqufKeTaMgL+jEhr6m1Dl2pE1PgMlNpuEkREQQ19MDEEOqzDZLJWuRn1+n1Cn8z3MwHMs7593jHLm5e+A4bmBTE4ICX4Ihd96xispa9B76Nnbu3ImioqKQPaL9ceqpp2LTpk146KGHUFdXh4svvhijRo3C3LlzI/NcccUVePHFF/H444+juLgYU6dOheM4eOuttwB4P44NGzYMZWVluOOOO7Bp0yZcdNFFuPTSSzF79mwAwGeffYbDDz8cl19+OX70ox9h/vz5mD59Ol588UVMnDhRs7d06VJ8+9vfRlFREU444QTcfffdWddPvHvRPUs0BX/+3jSML30VRUM2Ia9rMEHy7spcVKzshde+OhGpsq7otWMB+nTfgZ77f4XcfWq0Mmp3pfDl5n5gB52J/mdci2TBPlZbC2eciVEDFyGnsDbIW5mLpWuOxXF3Pd88FSQIok3Rkv+GdYg5enbu3AkAKC0tBQAsW7YMdXV1mDBhgkwzcOBA9O/fXwo9ixcvxpAhQ6TIAwATJ07EFVdcgQ8//BDDhw9v2UpYUX9ybw4ifs22noqSHWLKyta2ZtM4HqUcZKRxv1TruZi11kxJrXXDlMw29dQmFjHjs2ktXIOgZDkawxQPMtlmps3gQ2SLqf1kmdZMrYx7Mcpy0ECb3H4u0j+bTU2UiijP9gjESN+221ETQ/yInki/IoST8L6eUbvMzLiGYl4gW3vG2AvVM+LrILCjJ7WuaGYpgyl10IZUqfl8Q9zIJ8U3xwF3AqGEg4fa2bzHRbGcc7945dmBWFzcEHzUz35Ej3WCY6WyolymzADNwRDMtSMKUObk8c+LwBtRF8YcqJM/c86Ne9QTcFRRUms1bX4fv4ZcvXZM1kOIhyxSwCfaIx999BFeeeUVLF26FCNHjgQA/Pa3v8U3v/lN/PrXv0bv3r1DeXbu3IlHHnkEc+fOxYknnggAeOyxxzBo0CAsWbIEY8aMwb///W+sWrUKr776Knr27Ilhw4bhF7/4BWbOnImbb74ZOTk5ePDBBzFgwADceeedAIBBgwbhzTffxG9+8xtN6KmqqsKkSZPw8MMP45e//GULtApB2Kmp3oUxRa+h+9jPgV6nwDliJlA8GFs/eA01r/8EfcZ+jnMq/oRUUa2+sAAHaqtT+GrLPkiMuQG9T7oYB2Yxs/1xdz2PmqpKLJozHcnqjajv0gejZ92N4yiShyCIZqDdCz2u62L69Ok45phjcPjhhwMAysvLkZOTg5KSEi1tz549UV5eLtOoIo84L87Z2LNnD/bsCdT+ioqKpqqGQku+dEeIIXE976zKylAHs3Oi5eHhQ1n5kbUkkJHQkBKE5+oA9NgoBoQW54nygBufeei43Z55TC7OE1E+sxyLtMlFt9DSYdaEDNXLsH8c0Fc88vO6zHKFo2wqDRu+Fg2w6YYjnqLkP5bNhYOlbXkgmDAIUYGFM6jOChylLFPkkEONLEqNGMojdn0hwxydZE5szKFHMYVEsMjG9XZdRUNQpg+S2No2JDqZNuO+flRRKO16y4MrYpYrhlgJ40oB6jeRWJVKiDvepD5h30MV4a5xQlwV7wnSLqchsskVt6TW40K/LZToHuWB5NyblUteC6a3M2PMF3i5HJqmR9g5StlKPblIayptHNzN9N1OtCcWL16MkpISKfIAwIQJE+A4Dt5++22cc845oTxN9ePY4sWLtTJEGnNo1pQpU3DaaadhwoQJJPQQrcqL19+OM0ZsQPnOg7Fs3cno/fSPcehh69El4aK0ew0YA3KKvegb7gJfbuiF+v3OwP7nXo+C/J7o3wib+YVdMf7WR5q2IgRBEBbavdAzZcoUfPDBB3jzzTeb3dacOXNwyy23NLOVbIWMvaUhvdm49Lbjlt6s9bSlW2iqE1lXu7HRTyxmL/jNPnycaxZdAEnDX5v0xTIc062GbWrw7K5KyJ4aQRCR37QTRBgwOI79Ho3TShxu7efb82fod0aJCaGhO0pET0a7DejrhoQw/6AQFSLFixhhhXMoIxzNhGGbZhRRZERPVBmwCzVmOlmmepwpm2LPWm/zmAtvqTlhk8fnC/46YIwr/oSHTam7qj4WVFQRz5TGV/0XZXp/HcDh8vb3jgUPg5jBxzsZSECA1yFAQo+uUbJabQYVDlb7MiN2vHqr5XLlOopvosDPoGwuvfbKDaKNKKKnY1FeXo4ePXpox5LJJEpLSyN/xGqqH8ei0lRUVKCmpgb5+fl4+umnsXz5cixdujTrOrXMj2xEZ6JiWwVemXUDxnZ7AckutSjaXI5Te12HxP5pLd3uqlzkFe7BqvcOxuG3L0X/Rk3TQBAE0Tq0y8mYBVOnTsULL7yA1157DX379pXHy8rKUFtbix07dmjpN2/ejLKyMpnGnGhQ7Is0JrNmzcLOnTvltmHDhiasjQqzbE0NN7Zs0mcqx+Z3TNlyCRuhAhg+ZVXtvW0nHtqztYq64pUcqqFYdEIl2a2oZbuWY/qmr21lu1KhVaostVLtuUYeeSym7hq23nXGengRPaq9SJumGmExZfoo97lRN2UVqNDKXlzp/zfwcdPaVpQjV53ikStPmVEwah2YE33dYebjwcparmpTrLJlqaut3WwiCTMTCReUNhTtqp7jHKFVzISvanlqtFZUU4trqZbNXX/9Oe5t6qpiYoOrXELlayWYlZkpcxSzkD25apjfrnK5MmGTB/bF1XFFCaJ+COYuCk3kDPgTKivPtFIm5F/PIe5yuK55PzHoAThMTswN+R0ctHewgps4xIN29JeCo4ie9sH111/vi7rR2+rVq1vbzVg2bNiAadOm4amnnkJeXl7W+ebMmYPi4mK59evXrxm9JDoi28q/xtwpN+CF80/F6quGwH1kIM497g/ofbgnUnbpWYlEbhpumqG2OoW1aw8BTl2OBZ9dDQCo7nIoGIk8BEG0M9plRA/nHFdeeSWeffZZvP766xgwYIB2fsSIEUilUpg/fz7OO+88AMCaNWuwfv16jB07FgAwduxY3HrrrdiyZYv89WvevHkoKirC4MGDrXZzc3ORm5vbjDUTxL14N5Xok0U5akc4q9gRm98xWqK1x6mEDnBk4aZpc+/ax9QZgs/heqrN4wKImGs4VJ6ZLKrDy7SGiMibpU3TVpQvGdGuWfh6R9Ul64ge9Zpn6H+qIpsNuepVNjYbiHXuHwZ/hSN7u0dFnwCeuBAX0WOzKY6ZiyplrK80mjkRcyzPBEMo4iayzkZ5XInokZdaueYMUWWL6BQubZltqt2Rqk14qk9gK1xxxxCgPMXGi+gJ3ODSqWBeHtNWIBwxRQAK2odBhDGJvGo7OeL+8YebBZNwc30CaVmuEII4OGeAow/X8u7HoIG1tvUrShE97YNrrrkGP/jBD2LTHHDAASgrK8OWLVu04/X19di2bVvkj1jqj2NqVI/549g777yj5TN/HIv6Aa2oqAj5+flYtmwZtmzZgiOPPFKeT6fTWLhwIX73u99hz549SCTCs7XPmjULM2bMkPsVFRUk9nQiaqoq8bYxp02m1ak2fboRC+78A0q3voUjD1+D0v2/xrfH8tDcciKqdPPqHii84LfoeujJSCVyMBBA7e7d6P35S8AQYNjFP2q+ChIEQTQT7VLomTJlCubOnYt//OMf6Nq1qwwbLi4uRn5+PoqLi3HJJZdgxowZKC0tRVFREa688kqMHTsWY8aMAQCcfPLJGDx4MC688EL86le/Qnl5OX72s59hypQpLSTm2GjJF+6g05J1+sjf34HobrylNxlKFtG7z+he07dXfJ/fPneO8CRTBzuq7MbajMsbpZWIlubGflR+dUfthMf5o+mDPmKOHts5xJUbY0/4rs64otnknsAUWcdohS0j3LhNue8Q595MMJHXxRAFpIjgqNdK91ibf8jih5yvx89mtnGUzch6KgVwo3GDesJbIVxEzQgf/DId1Q9VyDEFFcOxQCQJXsCFOGOOwNIrCV3U08Qjz1E1miew7c+14wbFcXlB/WgXrVC9ePVKq8KTY1EY9W/KoD4cop4cLneDqB7Au4FFfr89tDZUJmKS8+8Y1xvcuyMZc4PnnnMw3wGXInraBd27d0f37t0zphs7dix27NiBZcuWYcSIEQCABQsWwHVdjB492pqnqX4cGzt2LF566SWt7Hnz5skyTjrpJKxcuVI7f/HFF2PgwIGYOXOmVeQBWvJHNqKtIVapGneYskrV3GewcPU4bZWqzz/8FG/e/QeMKXoGvQ74Cl3TDN8evRtOUp+Trb42ga+27IvqgiOx77jvo+SwU1H5eH90LdqNZVfdix0jVmHkRefg3SefRcmyVzHilM9RVd0VRf1ObLE6EwRBNBXtcnn1qKWGH3vsMfmL1+7du3HNNdfgz3/+M/bs2YOJEyfi/vvv137RWrduHa644gq8/vrr6NKlCyZPnozbbrsNyai1pw2af3n1qGzG236DEPkaaJslIkInMpWTyNJXFv7spBrRRAzeUvANaZ/ASCKRzNj3D4kUAJIOR8Kxp1X6oqHzCXDkxSx1HrefdHgo4sJm1yTBOFLJBjYtEzbNprVHG4V99Zcsz2wilDkV8zha72ZfechJxi+vbvNTHEwlwx11NY/aqVeT5eQwr5Nvs8OMfYNUygmiPsz8LFyGIDcHcJxEZLniROi+ZEAyGeErgsfWtsR6Xo5yHxjtYBMJBQnGkTS+KjW/HKMcUTarR0EuRyiCKKpNxVcIgISTRjIpomGU+5Upf9U6+GUnmIuC/DTEPa5fcw4w13pNGDhSyTQSCdd6r3h15Ho9fHJStcjLrVPqHUinwTLpqsoYRPrk5NR6y6vLMv1IJimCqXUP7FdW1aHXkMW0VHUH4tRTT8XmzZvx4IMPyuXVR44cKZdX37hxI0466SQ8+eSTOOqoowB4y6u/9NJLePzxx+WPYwDwn//8B0CwvHrv3r3lj2MXXnghfvSjH4WWV58yZQp++MMfYsGCBbjqqqusy6sLxo8fj2HDhtHy6kSIhTPOxNEj5mPbhlJ8VHsODj7vUqz9+8MYlPMsSvttw6olByA/px4ph2PfPtuR160aLKF3aep2J7F1azfs3l2ALiddj15HfxfM0QVFvuEfSC/8Hio3lqD64+6oq8hDqmg3uhyyFV377EDiuLlg/c5qyaoTBNGBoeXVM5CNNpWXl4f77rsP9913X2Sa/fbbL/TrU+vSSOGnRUzGRfRYu3jK+YbaDDohdjIV0Hjt0oVdz1Itmi0hRoNkqn2mmKg4m1ZiIj0yZcvKZqSz9txmXdViOPPa1ipKxVU0QyOIMk2PGPxVmSKuC4u7ZTPAjQ9qpEZo1S2bTYTvBSGqRF5LMxRLPcX9iBQWbvc4mzY3tfvaEkEjonZc9SZSHldblJStnlGIYBa1qt5npn/vKyety7wryZiYQIsF8XFyxSpARtCoQiMHwB0XrhsIKboRro0AM5vSYcFEyGq7+4FCQQUccZ777erC5eodFDSyec8yURFxmKl2xFA1faUwkUAu2845uBtehYxo3zz11FOYOnUqTjrpJDiOg/POOw/33nuvPF9XV4c1a9Zg165d8thvfvMbmVb9cUyQSCTwwgsv4IorrsDYsWPlj2M///nPZZoBAwbgxRdfxNVXX4177rkHffv2xR/+8IdIkYcgoqipqsSogYuwbUMpiq9ajdx/LcHrt9yBPqnPsLtfPtwyB4eN/TSUr35PArW7crBh28E49IqHkNvtcPTL8A8P63cWEsfNRddl16Oo71p5nBfsh8SI+0nkIQii3dIuI3raChTRk005DpTJRxqQD4CT04gm8nuajj0EPDqPRyKRtJ4xoyHMNEnmR/QY0Q1RXgTns4vosTVDknHZtFYxA3b/HT+iJ8pWnDNJa9NyW1KtzKQDJPyInihbLPTBI1P0Uaid/MiHlCWiJ5vbibH4iB5AfxRUMWVvI3q8c/aE0TYBx3IjsIj06rGoekpbTL8u4nNuDmQEm2bPctOqh2wRPYC+7DuYsQ/IiB5d5NBtRtU3qUT0qNE5Yg4nzV+lLC+ip95ePzlfjhJxo5xOpeplRI/mrx/NY7vXGYBUsg75+cEQhUBkUqNxgmnc5QpaAHJy9sDx5xQKBDeRxlVMqdFEHJXV9RTRQ7QrKKKn45JOp/HWX15BzUt3YMI3l2LDij4o7b0TBd2rAIT/LXPTDOVf9oDb/3T0O/XHYMWHRkb9Z4K7aWDrW+A15WD5ZUD3Y0LRPwRBEHsLRfR0Whop/LSIWVs8Sqb9OHu2GIxsistkY+/a0BqtY0ljWpKdK4O0Jb+5QpdNaY2theWkrTXjImxs+eOaW+uwihCmCGxTqUT6Yumk2xzNVo3W7FhCl+R8KIb9KLtZ2xGfXQ5uqBSRcwEp9szIG3OHIZjnKGSTI1hOXASK2NQ23WRgk4fPWdub69FM5tQupvBjtRvxmJs/N6SFpuOndzjzolSUKCrGkdW6kV7kCoM2WRCCwBp5hHNlxSyAO9yYo8dP7wTz21iFJsaDiB6lvMAoCwyLyBqIdbjSRsSqb4dx/Vnx/6ffy8HsUBxBlJL3WdQ/KE+UThE9BEE0lMZMkGxSX1eHhX9+GV/861X0qP0I/btvwYFHfo6j8+uBU7w0/YZt1O1W5mHr9t5Idz8KA/KfxtvLjsGxd/+rSerEnATQ87jWehMnCIJockjoaVNk051thn+C4szafnrOKmNMPp7hvNqbDfUMm7eNbKJMNqVz+J1V4xd6LQrD8tkmGsE4F3kiTiRRjkf9zcqe6FirmWNEnky2Q8XLzqglM9d3G2wzIuPexjCGriNTbbKwv1yvX6i+imCRtdjG9HqGnpKwPhHat6l7NunWFMaE/dBiTVYjOi63RLmZ9fOFMfWrhxk3Q5SwarPNNWEyKEeLWjIuKgO86Bulkl69/YFWTK8o84Ua5qd0uRLNo4hv0auV+R+4AwYmMzEloViaXepE/gTVIgnnLrjIxyHnO/LqwAM7xjAw1qD5zAiC6OxkO0Gyyp6a3Xj9iRdQvmA+emENBh28Dj0P2opxDgf7phv+Pvf3d27tiq927Ye6ZHcMmHwHCnsMQiGAN265EgMOBuq79G2eShIEQXQASOhpszT3bwpZ9PJisSVuhM+yA7a35WXqdWaH2TeNi5SxCTcNlaVMwSeuLOsxNZojwk9u7JsrUWVqJa1LywHGLcqCtKR/Uv+GbGYQqsKlxvtn1tflugiSKXon2/ZQ6yEEE2mbc00ECHXsVdMW20GchXJS9O0NESWwqZ8LDUeKgAUG1WJ1f0xVSrGlRvSIVbbihKqQb6ZRdTfUyJ4jIZFOih5GGaYgxP1VqUShXCtWqbdyD7vw5+hR/VdCiuRBJY8QiaTgx+W9LsRgIdB4h5UG5hxIuDIax4y+CeNb4GJ4VjAvkBhSxv16eEIUD98bnCJ6CILIHm2C5HJ9guSjR8zHwhln4sgbn8Jrf3gWOxa/jt6pTzFi1BoUdKvC+KSDxFn11n8HanelsH17KXbV5qO2cAj6nH0tcv41AfU1Kew//TXk5BcEaWt2YVDuc6itzMXoWXe3XOUJgiDaGST0tFmyCrPZCyxlxJrM4uf6bO2EbEakCdnMWJCSvnFtZPaDGio/2YKQbOWY4kRU/zdOOFICQaxE2dSiJZD5SmoCjaxMfC6b7ZBNpc/MzMRZlJnRpllPi8u2drAJZ5F2DBuh6BqlDx9qd/Xia+fsAqzt+gkhw2ZTfo6oD49YNcv0IhAP/H1DvMr4xHHrR60M07C8X4RdGeES2IqbUFvVRkREjxDRbEP3mPyfUq4DOH5ET+BDuLGCjyJqhsPlHA50n6Xfhq1gnykRPb4IJG0KqdRss6A+nuDHA+HLV50CG0z6KIaLeYeb4t8TgiA6OuoEySXT1uD4/AJsK/8aX+Ew/HvJ1ziry78wdvgCuM/0wTeKEkidV6sXkOOJyrurcrF9RwnStQw1+56Ag753I/KL+iPfsLdw9XGeqHTPofhoz9k46JxL8Mmzj2BQ7nMo7bcN/1l2Eo5r4HAxgiCIzgQJPW2OFn7pztpcNgJKloWFkkX1RrN1LluZJDNq3zv+d/TokzZJLEomSyj7kcJMhE0H0aJEnP9iZSNRRhTWVuXQoh6isEVGhWxGCAzZlmkUE46KUqJPQsOMlIQNvUu0a8V1u2p0TWjZbtWsRcuJbFUzoscQTuRKVaaaZjOsnjLCrsz20zRYHtRbjSRS29X6xNk1qyAPD45HPXueTuMd1YcqQWtfLQjIFITkSaa3n6i7P3GWb8XLKyJ6AG9OeXU8nC2iRxbNkGAOAFfRRL28nHlSi5jjxxNrmRd2AwCJtB/Rw0OCjuqz7buEaeJQcNa7P7huU3nwXVqPgSA6DE0xd47JnprdWPLca6h58W5845u1+HL5vqifcxj26bMDufUJnN67HsmD6mR6J5GGN0MhsKsiD7uq8lG9uwtw8LkYcNbV6JLXA12ysHvcXc/LYWLHFj4KfPAoyg4GaitzPZEnYpgYQRAE4UFCT5sj9Jt3M9phYXNRaL8sNwSL/7YirNXMVqwJdfGzzBfthq2UqBKliBEjGqjlxZWjps3U0i6iV/VSyzGPqR1Ic9CGtQOpnmdRqTLbNW2ao2CYLXEWZUceV+vJI081UhLUIzPkMVX48G8MUzRh4pyaX7t3DI9MscjYlxEbhqKoRk2J5cQ1zFWzokQZprmpnQ9FKlnVooCoa62ZZsZzwgJxkdl8MYQPszzt2QyUF6s9fe4agDnBECzzmpjPMpMNzpHmLpJC3NHScS0fY9y49gl4AhHz8+kX1RuiFdSBSRUriOgJ/A2yOkrDMd9ZMWzMtkocQRDtj8bMnSNI19djxYKl+PjpuUjtWIcubDd6lmxHt2470WvwJhyTcsEnemmHHPextYxdO/NRUFyDbRuLkRh3K/Y58mx0zdkHeyMzHXfX86ipqsQiQ7yiSB6CIIjMkNDTJmmJF++oXl1c+rhEjfA5K5t7VUCDcRFeFUsgulu2TqXoe6mRBLa8amQLEAg1ceJInLgRF+VikwxFBEim8iNbloc+RCexHNO0hYi2iiNKI7R6I0SIGDt708dVhRR5jOvRErY6Rq48ZWZSDDHYI3pkeuPGjBVSlHJN30P+ME/AUIN/VIFJ88JUgSx2Q1XQ9QjLTsgjPbLIVmcEbeWI4+LZ9DNpxfvnvIWplEZwHXC3Hsyx3F1cse+LK6qIJcQTztUINs8QV4QazkV0j+9RwvXOmYKNMKuISWqLBAKQH/Fj3PQudxVhlSvRX2KSZ4IgmpOa6l148fo7ULupHDm9ynDabdchv0tB5oxZks3cOePu/Ac+Wb4ay/8+D0X/fR77l5WjqiIfpSW7UFxahcPKdmLYhLpIG8x/YdmzK4Xqqnzwegfb9/TFgRfNRqJsFJbP/imOLX4UH+44FePHXNxkdcsv7Irxtz7SZOURBEF0FkjoaZNkHfLSBDaYvhtFxpWWouIiYvzOGLyUKdYiqp0a31bGtCV2tyxeqB2tTPlUG1F9d7XjaIusgbAZ41ekQGRccls8VJTNTPdBnCgFGBFEHKEOcmwhEacjkysdXyC6nlZNIQMsaofpgontwloXk9OKCbd+1IokUgwwFERTFLLOn21G9Ogm5X6kWKNEC4l9Tayx2I2OEtMNmCKkmJA49FzGKH/avWUUqA4/VOstlldnAJjjekPTLI2ntY0hkHkfuLaEua0OjAUTNQfCjuOLNP5y8qpN3xgT3xzaylxcSaOIOdIv5vskPvtlWb63CIJoWv78vWk4rGIFxvbZjkSfeqR3J7H2OwvwYdEwfHfuPXtdvjp3TtcpH6LP2k1Y+sJ/8PUqBzzRH2P2rcTYoQuw9ZYDUFa8C2cfsAfJw6MFHQDYsysHVZWFqK7vgbpaDrfbUPScMBl5C85G1bauKJm2Bjn5Bejhp6cJkgmCINoeJPS0WZr77Tu2pxmRPlM3vmncyO5kQwWg7NCiBSyfI73gCA1jiZKpVLEjEZEmyp563OXRc+yofpvHowQis3NtQ3Qz1SPRe4ZdW9kZxCq7D9mhLmdt+gLsXQeXR+wwxqJtRjxC4nBkRI+wYSgmSvdeK9sUoSKraUb0KMdUf8z7P1hZKtqO1abxbEi7plCiPEfyFPOECtkMTI/oiXxW/EK4+WD7OfR253CVA0nOQoJJkFLJy8wrxwHugPvz7piX3ROGObgf3cP8iZ044P1i7i2TpaT2P/kCIRffIFJo09N4NlWVx283RQ8M5uvJJKYTRMemOea0Ufnz96ZhfOmrKDp+E/K67pHHSypz0X3lV/jz96ZlJfbsrq7Bmnc+xOfvrETF+++iN/8QBandqK7JwwH9y7HfkbXoiiok/t4XZZU56FuSRvL0WjDlJaHbIVu0MrkLVO0owM6aMqS7HowuA4Yht88IFB96HApSXWGLN1p4P02QTBAE0V5gnOK2G01FRQWKi4uBAd8EnFQjSmikkOH93NwIe0CkNJCx1+vEzGgrC7HnYxEzyZg90lDWnAz2ogp1jPEz2eTxTSaSsrOWseMq8gBIMSDhjxOJFYSgd/wS4MiRcisP5Y3aZwCSDkIT8GYj1iQZRzJG4o0TZBKyacNfG3E2Uw7gJOwpQj6r9WFAKsPtY/MV4EglWewtGxIrfBwHSCaiFZIoEQMAclMAU57N0D0Ucb0cBiST+j0btIcprukJ8lIAszxjTEljs5lMAAnlmmi+KvlstvNyg+gXq72I8hJJjmQi4r5m4fTB+Xp0yefaPRL39SHK8+4fF8mEq6WTgoxhW+aDN6Fol9x6zZ55jzKhuGl1dZGTSiORcNWk8r707rvg+XGUc8lkPQry6wLfWHhSZr3e/jAuBuTm7oHjpGU+vT24nA8I8L/K/c8VlXvQ+4i3sXPnThQVFYEg2jri3Wtv71kxp01OoTKnTVUOlmYxp0021FTvQvk1Y9Bv3H+BXqcgccRMoHgwsHMV0u/fDmx6BRsWHYj1x9+BTUuXIbnhXRSgEnV1OSjOq0RJ4S4cePgGpLrswZ6d+Uik0kjm1SNRUAsnkd2re7qOgbsOkrlpbFrXAwWjL0XJ4PFgJYcAud0ateqetd0qc7F0zbE0QTJBEEQGmurfsGygiJ42S1wcSVNglGsb1xJKH/m7eeNsR48PCs5HnYulodolR1gAY5G/dYePce1ENhFAomymWWbWtPYyvJ/no/Q+m+9C2NibiB67d8EQFWu5Sic5SkCKtGIbFmPkt9aVZYjoibGbSfpWo0pMm4zZxaXYpcBDp1gorVlHTaQSUUQRYkeUvdCE0Apc/aCcd1TXnAghy2ZPtLcxjE59HtRIHhXHr2ww/5Vyvyn3f8gXWQd1TJt6UrOup3GAYIhUkEZd5c6LrmFQxRf1r02eExMgS5OOF+HnCTHMm9vH8DGYl0fY1IdpBfe4IgLxwG99AnUvr8sBxrnffs31bwxBtF2ymdMmk2jBOcfu6l0o/7wcX23Yii2ffo7q8q9Ru7MKdTsrUPzlcpx2wefYsaUrFv21FrnJn6IobxcGDVmH/KIasFQKfY/+FD22fh+pw/cgMTIdaSvVJTzUirvArsp8uPUOunarxldfFqNq9z5IDjgOfY8/D07pYDj5vbDw51fh2IMfxSdVJ2L8if+7121HEyQTBEG0DyiiZy9o3oiemPMM0T38jNjyMevHUL64NaojjzFERvRoSS1lNDiiR7HpZLBpzQckEqr2Ge7AmYjWTDEgoZiM7HQa+0klokcKODF5VZtJJ3wbZCPWJBhHKkbitd4hfoEJLVgqs6+CpKNHj5h5mOWgsBkV0SOS2rVAjlSCxQZ2mbez2nFucESPT24OZESPTXyyikP+YXtED9N8s5GXA2tEDyKELke5lrHXxPBXTWmL6JG7lqYTAqOT4PbraWhbphbDUIcu+Rb/bI4jEMK8a+kiKX/9DiJl7HaCz0knjYL8er1MLT3X0gflcuSm0nASxoTKik0RCWTWJZWsRX5+nVZeOLpJqQsL6uJF9LiBTRYIQmoeUyCqqNyD3kMpoodoP+ztr6E1VZXA3P6o3FaITw/6Az5+/mV02fU5UJ9GdX1XnDH+NeSX7MIXH/VCQZc92FpeArgOcnPqUdy9AiW9dyJdl8DubQVIpNJIpNLI714FlnSRrkl6K/YlXThJc13L7Kjbk8SemlzU7M6Hw+tRV5/ErtxB6DvubOT2GghW2AfILwNLdQ3VR8ydI6it2YUd9xyKrvtUA5PWNemwNIIgCKLhUEQP4dOcUT0RZcZG2cT50VAflfSaTeO4vRffbOgm1a62zQNjhgtlJ9McP2qHNmLAToR8ps/HEaVj2JpOTgJt64hn2NcKtoa02PVikUKsfmQVdGJsZe2XpSxHDG2LyB8lnsRJ37bbUZv01hbRo7Z3nD+2M8r9pJahXUMjosfWxrbdUD2N+8K8b/XJfWEXGS1tqk3+zcN3ChMnmRFYqLQtcxy5dLiI54mdI0ixybl4TtXZeERsjS+GKA2stivnTClLuSGVygZLv+vlB7NTiwKYUVnuRQip9WQO1KX7wr/DeAJOOJpHnFYfGr+GWqQUC0cEZRySSxAdi7fnTMe4w2rxUfk5yHnjl/juSe/BSYW/+AeM2AAA6DlwS+gcAOSX7godSxbUh45xDqTrEqivS6J2Tw4Y0nDrE9i+vRj7H/oF/rthKAZ8fw6SxfsD+T2Rl8hDHoDiLOuTX9gVC1ePo7lzCIIgCA0SetoULfnCHSMiWd3IFPildkUz1EN2dmxGM43VifKj6dqOG3tRQ5LMVbTNxXmiPOLGZzeUNlo00VrZ7BhH2BOI3xbV5bJNi+ZVYDCFDIv6ElGGesxluuglTpp1AnQBIcpPmVbkV4cE+YW5bniqJtWWuvqVbWlwG6E6cigT4ApRgUVnUm9vR9fM9GtpUz50mzJGwxcyQsOiLI3LIj4DCC+oZuR3ud52pgZhe05CopNNeIoSo9T7NO0CrqaBwFUFDOPCqN9EzGwI5ob8VIdkyaTcNW64YNCYJhoZAhigiD7SL1drb/UuYTxIx7kbiFKBHhSU7T/0nHP5/KtDt/TGdPW2ZEG5TPkS4G6m73aC6FgkqzcCAA4+71J88cfrQiKPm2ZwEhxuPYObdlBdmY899QWod/PA02nkJnZhd20+WK9RyCnpgS779sK2DV+A55ei++AxKOjRF0gVYk/5MqTemYz/e2EIhjz2Orrk5aGLb6N29258dvEJwKFfoP8FtyDV5/i9qtNxdz0v5845tvBR4INHUXawN3fOf5adRHPnEARBdEJI6GlTxL1wN7UI1JDebFx623G1HpaYE3na0i20dbiywiZVZJOZxewBYp6N8HGuWXThraBlemT2bZlxjMHWQnabMCWEaM3FKjipAlGWuoZnRo1U4kzr5GayKXC4tZ9vz5+h3xklJpjChRrRk9FuFqKF1bZRqYyrbqnHFJucQxnhGFY+tLZlpmATE9ETVQbsQo2ZTpapHjdu4qj2tx6ztXNMY6uRLqrIKJcmh55XPv2qqMIVJcU/yRGIMbbnwmtPB3C4/hwbzspnwVhKnbsAElwRf5l2U4kSZDCNrBfzy/FnCArdnPqkyl791NpzBCuFqca49EOKn+I0RfQQnYz6Ln0AAGv//jCOu2UusHszkCwEUoVAshBv/mIGjj34Uby15jsYf+sjyM2iTFusTO4B/VHx+hQcMmId/nP6mdgxYgJGXnQO3n3yWZQsexUjTvkcVdVdUdTvxCapF82dQxAEQajQHD17QfPN0ZPhxVt0QhpF3ILccYeyXXXLJt4056pbET3ERs7R4yR07TNoLR46plpOMT96JEZEMQUXIJijx14LrnwOl5Nk/m1g6eian1Vsc/RkFDX8nWTMqltx5SUTQMK4f2yCjS2yJtMcPaHPfsfWNkePTaixzfWSipmjx1qO/zcnB3Asz6Yp9NiGQSUT8ZJf1FCz3BzAMZ8xi2Cj5nekTVW0sBq3Dn/LzfFXmTOzmGkNgSiZ4HIuK5tQo/qn+YA6FOSH/bC1oymKJRMuEr7SGAyvsqxwZ7RZ0qlHfp4L/boFwk4g1gQhN943IEdOKg3mmCtscWlH/cyUvMlkLfJz6+V5GHP0eJ9d7b4W8/jk5Oz2J672I45UO7CJZJ5oVFFZR3P0EO2Kppyjp7nntOEb/oH0wu+hcmMJqj/ujrqKPKSKdqPLIVvRtc8OJI6bC9bvrL2yQRAEQbQfWnKOnsaqBR2K++67D/vvvz/y8vIwevRovPPOO63sEY/ZmgpmbDFuyPSZylEziS2mt8zh/fTOmZJFKS+r6jZt+6i10YdmBf+ZlsXnUOc6pmy1xWzHmWZTT6eVyeKvolmmY3x2jGMZ7oiMmDakLZ6lTT14IfaSqu0iy1PaQ6y65SgbMzfVTgNvI7UcMF9k8AsN2TE24bd8alzbdRcFI7JMEbXEhH1fiLPV1bRpi64xEzHHsMeCY2YbOLa0RnnctdhT/BDXTauzAzDHbxGm1NEJ6szETQXL1wrnkOIHt1QcSvSXYlM0pBcxxfw29T8LX836wYvu84a56TeTdk3hrbAlyvPqxoL7R15LcRxwHE+YcRiUenhz8LguB+cMepgYZJuJynnXRLQj8yKlKKKH6GTkF3bF0tXjUNpvG3bccyjeuOVKbHx/Bd645UrsuOdQlPbbhqVrjm2SiYtZv7OQOG4uuh5ShF4nrkX/s1ei14lr0fWQYhJ5CIIgiGal0w/d+stf/oIZM2bgwQcfxOjRo3H33Xdj4sSJWLNmDXr06NHC3rTkCzcPemvZprf6pw5gijuvwGznmbEfUVx0QU1CfB8/HAWgehLnjeyb7YVN24g6W9648+Iqql1Qm9/MssNCKcLWo8o15+gxbUbeijGNaq2Lcpu63BOYYm1mrHyM/SCQQ+5z7g26ibqm+jwtQV4xX0/4DGBO92L6IOfr8bOZ9Y2yadYzJAAxXZiRZQubDNrcv+oQP4fZr0nUcuiq79p8wvIcD4YoqQ2hJBRRLsyoKBPz2jD4go/+hDAE9WTw03EAcL35ayzKmChenevGs6XYlHMD6bkZ9wQbbtTDu5auJ0yJc46SnysilFJ5pjaw8TXtXS/vjmTMVcQ17yJyeCIRQXQ2WnJOG9bvLCT6nA5sfQu8phwsvwzofgxYgyKPCYIgCKJhdPqhW6NHj8aoUaPwu9/9DgDgui769euHK6+8Etdff31s3uZfXj0qm2U5oaxJNM4uSzYg/ovpnzMtrx6V10k1wlW/974Xy6tn0hxCggGAJLMMSTHy2PInwJEXIbfa7KgkGQdLRGsVUfUQQ7ca1LRM2DSblptJrOUmjWFCMSZCBWSzFHxYmOLIScYvrx5hDmBAKskiG0i2rUUAy8nxIjWsNpixb5BKOaLrHs7PwmUIcnMAx78okS1sVIf5ZSaTEb4iEBRsQ7fychCe68XIb3MowTiSxlelet21eXLUslk9CnKVYUyW9rDZdQAknDSSScATdbieX9SPGX4ASDAXBflpiHtcv+YcclLnUPtwpJJpJBKu/V5h4eFegpxULfJy65R6BzKmGPKlyYgMAFx/6NYeP+pHZPcjmWS7Ks+qYr+yqg69hiymoVtEu6Epw95rqirxtjGnDS1BThAEQTQXtLx6C1FbW4tly5Zh1qxZ8pjjOJgwYQIWL17cCh5l2/WOiq5phI2si3HRoPl9GuBC49I1tv7xiBWw4kQWs/Xlr/uW8tSokygioz9iPc2cPy59VjYtDqhLSEf5YaZg8KIkwquLiTLjBYo44my6fkWjbKp/G4I55Ei9H6yrbhk2zTxAIKpEtm7MjcS5H5HCwsEuatRS6FvDdn2NckNO+TZc9SZSIptsUVK2elrhwb2pVtX77IDzdFAHI5DQnKtHKdKz6YcJcV9ZEeUz+FFYXGkf0Y6O60W7GCKJVy4Prbqn1ZkxrSx5fwCeLSVsifv/ZwxwuQvXj+jRJ2JWxB5DNBIHPJuiNP8u5MER9WEQv+8wcHBXDdsiiM5FfmFXjL/1kdZ2gyAIgiCanE4t9Hz11VdIp9Po2bOndrxnz55YvXp1KP2ePXuwZ88eub9z507vg1vXSA8aKVaYM/A2iKjudiabCehrEGedEY2P6InopWfMzwDeOJsMaQD24T56x1PP7TKuD1Hxz9m8UPO64EYXUj2n2zDTuf7BqCaK6oi6jMPs2mVsZkUYCV/OcFRP6HMagB/RE2WLhT74uw2IPgr6vRyMZ56M2VqGd0FjEztqWiNvYyN63JTjn7MnjJNZ5WTMETaiVsSKilxSr4XquzjOeHgyZrOOZh7Ai+hJWIIfHSOfWVfG6uEo4oiwZfNN1E2QdFwkE4AQO+SzaYpShojiMBcc9fb6sUAoUSN+xOk9tfUyokfz19HTmb6nki7SacWmuva6PCaeXhGtw32bdcoE0DDSqEvK6xFCVbu88jp5cC/RjhD3akVFRSt7QhAEQRANQ/zb1RLvXZ1a6Gkoc+bMwS233BI+sW5eyztDNAuN/W27tkm9IAiCaFkqKyu9ocgE0caprKwEAPTr16+VPSEIgiCIxtES712dWujZd999kUgksHnzZu345s2bUVZWFko/a9YszJgxQ+67rott27ahW7du3uSbHYCKigr069cPGzZsoPkaQO2hQm2hQ+0RQG2h057ag3OOyspK9O7du7VdIYis6N27NzZs2ICuXbuG3r3a07PXVFCdO0edgc5Zb6pz56gz0Hnq3ZLvXZ1a6MnJycGIESMwf/58nH322QA88Wb+/PmYOnVqKH1ubi5yc3O1YyUlJS3gactTVFTUoR+yhkLtEUBtoUPtEUBtodNe2oMieYj2hOM46Nu3b2ya9vLsNSVU585DZ6w31bnz0Bnq3VLvXZ1a6AGAGTNmYPLkyRg5ciSOOuoo3H333aiursbFF1/c2q4RBEEQBEEQBEEQBEE0iE4v9HznO9/B1q1bceONN6K8vBzDhg3DK6+8EpqgmSAIgiAIgiAIgiAIoq3T6YUeAJg6dap1qFZnJDc3FzfddFNoiFpnhdojgNpCh9ojgNpCh9qDIFqHzvjsUZ07D52x3lTnzkNnrXdzwjitqUoQBEEQBEEQBEEQBNEhcFrbAYIgCIIgCIIgCIIgCKJpIKGHIAiCIAiCIAiCIAiig0BCD0EQBEEQBEEQBEEQRAeBhB6CIAiCIAiCIAiCIIgOAgk9nZQ5c+Zg1KhR6Nq1K3r06IGzzz4ba9as0dLs3r0bU6ZMQbdu3VBYWIjzzjsPmzdvbiWPW47bbrsNjDFMnz5dHutMbbFx40Z8//vfR7du3ZCfn48hQ4bg3Xfflec557jxxhvRq1cv5OfnY8KECVi7dm0retx8pNNp3HDDDRgwYADy8/Nx4IEH4he/+AXUOew7cnssXLgQZ5xxBnr37g3GGJ577jntfDZ137ZtGyZNmoSioiKUlJTgkksuQVVVVQvWommIa4u6ujrMnDkTQ4YMQZcuXdC7d29cdNFF+PLLL7UyOkpbEERb5L777sP++++PvLw8jB49Gu+8805ru9Rk0Dtb53o362zvYZ3lXaszvlPRu1PrQkJPJ+WNN97AlClTsGTJEsybNw91dXU4+eSTUV1dLdNcffXV+Oc//4m//e1veOONN/Dll1/i3HPPbUWvm5+lS5fioYcewhFHHKEd7yxtsX37dhxzzDFIpVJ4+eWXsWrVKtx5553YZ599ZJpf/epXuPfee/Hggw/i7bffRpcuXTBx4kTs3r27FT1vHm6//XY88MAD+N3vfoePPvoIt99+O371q1/ht7/9rUzTkdujuroaQ4cOxX333Wc9n03dJ02ahA8//BDz5s3DCy+8gIULF+Kyyy5rqSo0GXFtsWvXLixfvhw33HADli9fjmeeeQZr1qzBmWeeqaXrKG1BEG2Nv/zlL5gxYwZuuukmLF++HEOHDsXEiROxZcuW1natSejs72yd6d2sM76HdZZ3rc74TkXvTq0MJwjO+ZYtWzgA/sYbb3DOOd+xYwdPpVL8b3/7m0zz0UcfcQB88eLFreVms1JZWckPPvhgPm/ePH788cfzadOmcc47V1vMnDmTH3vssZHnXdflZWVl/I477pDHduzYwXNzc/mf//znlnCxRTnttNP4D3/4Q+3YueeeyydNmsQ571ztAYA/++yzcj+buq9atYoD4EuXLpVpXn75Zc4Y4xs3bmwx35sasy1svPPOOxwAX7duHee847YFQbQFjjrqKD5lyhS5n06nee/evfmcOXNa0avmozO9s3W2d7PO+B7WGd+1OuM7Fb07tTwU0UMAAHbu3AkAKC0tBQAsW7YMdXV1mDBhgkwzcOBA9O/fH4sXL24VH5ubKVOm4LTTTtPqDHSutnj++ecxcuRInH/++ejRoweGDx+Ohx9+WJ7/7LPPUF5errVFcXExRo8e3eHaAgCOPvpozJ8/Hx9//DEA4P/+7//w5ptv4tRTTwXQ+dpDJZu6L168GCUlJRg5cqRMM2HCBDiOg7fffrvFfW5Jdu7cCcYYSkpKAHTutiCI5qS2thbLli3Tvoscx8GECRM67PdwZ3pn62zvZp3xPYzeteidSkDvTk1LsrUdIFof13Uxffp0HHPMMTj88MMBAOXl5cjJyZEPmqBnz54oLy9vBS+bl6effhrLly/H0qVLQ+c6U1t8+umneOCBBzBjxgz87//+L5YuXYqrrroKOTk5mDx5sqxvz549tXwdsS0A4Prrr0dFRQUGDhyIRCKBdDqNW2+9FZMmTQKATtceKtnUvby8HD169NDOJ5NJlJaWduj22b17N2bOnInvfve7KCoqAtB524IgmpuvvvoK6XTa+l20evXqVvKq+ehM72yd8d2sM76H0bsWvVMB9O7UHJDQQ2DKlCn44IMP8Oabb7a2K63Chg0bMG3aNMybNw95eXmt7U6r4rouRo4cidmzZwMAhg8fjg8++AAPPvggJk+e3MretTx//etf8dRTT2Hu3Lk47LDDsGLFCkyfPh29e/fulO1BZKaurg7f/va3wTnHAw880NruEATRwegs72yd9d2sM76H0bsWQe9OzQMN3erkTJ06FS+88AJee+019O3bVx4vKytDbW0tduzYoaXfvHkzysrKWtjL5mXZsmXYsmULjjzySCSTSSSTSbzxxhu49957kUwm0bNnz07TFr169cLgwYO1Y4MGDcL69esBQNbXXNWiI7YFAFx33XW4/vrrccEFF2DIkCG48MILcfXVV2POnDkAOl97qGRT97KystBkqPX19di2bVuHbB/xorJu3TrMmzdP/iIFdL62IIiWYt9990UikegU38Od6Z2ts76bdcb3MHrX6tzvVPTu1HyQ0NNJ4Zxj6tSpePbZZ7FgwQIMGDBAOz9ixAikUinMnz9fHluzZg3Wr1+PsWPHtrS7zcpJJ52ElStXYsWKFXIbOXIkJk2aJD93lrY45phjQku2fvzxx9hvv/0AAAMGDEBZWZnWFhUVFXj77bc7XFsA3ooAjqN/TSYSCbiuC6DztYdKNnUfO3YsduzYgWXLlsk0CxYsgOu6GD16dIv73JyIF5W1a9fi1VdfRbdu3bTznaktCKIlycnJwYgRI7TvItd1MX/+/A7zPdwZ39k667tZZ3wPo3etzvtORe9OzUzrzgVNtBZXXHEFLy4u5q+//jrftGmT3Hbt2iXTXH755bx///58wYIF/N133+Vjx47lY8eObUWvWw51ZQfOO09bvPPOOzyZTPJbb72Vr127lj/11FO8oKCA/+lPf5JpbrvtNl5SUsL/8Y9/8Pfff5+fddZZfMCAAbympqYVPW8eJk+ezPv06cNfeOEF/tlnn/FnnnmG77vvvvwnP/mJTNOR26OyspK/9957/L333uMA+F133cXfe+89uRpCNnU/5ZRT+PDhw/nbb7/N33zzTX7wwQfz7373u61VpUYT1xa1tbX8zDPP5H379uUrVqzQvlP37Nkjy+gobUEQbY2nn36a5+bm8scff5yvWrWKX3bZZbykpISXl5e3tmtNAr2zeXSGd7PO+B7WWd61OuM7Fb07tS4k9HRSAFi3xx57TKapqanhP/7xj/k+++zDCwoK+DnnnMM3bdrUek63IObLRGdqi3/+85/88MMP57m5uXzgwIH897//vXbedV1+ww038J49e/Lc3Fx+0kkn8TVr1rSSt81LRUUFnzZtGu/fvz/Py8vjBxxwAP/pT3+q/QPUkdvjtddes35PTJ48mXOeXd2//vpr/t3vfpcXFhbyoqIifvHFF/PKyspWqM3eEdcWn332WeR36muvvSbL6ChtQRBtkd/+9re8f//+PCcnhx911FF8yZIlre1Sk0HvbB6d5d2ss72HdZZ3rc74TkXvTq0L45zzpo8TIgiCIAiCIAiCIAiCIFoamqOHIAiCIAiCIAiCIAiig0BCD0EQBEEQBEEQBEEQRAeBhB6CIAiCIAiCIAiCIIgOAgk9BEEQBEEQBEEQBEEQHQQSegiCIAiCIAiCIAiCIDoIJPQQBEEQBEEQBEEQBEF0EEjoIQiCIAiCIAiCIAiC6CCQ0EMQBEEQBEEQBEEQBNFBIKGHIAiCIAiCIAiCIAiig0BCD0EQTQrnHABw8803a/sEQRAEQRBE00PvXgRBmDBO3wQEQTQh999/P5LJJNauXYtEIoFTTz0Vxx9/fGu7RRAEQRAE0SGhdy+CIEwooocgiCblxz/+MXbu3Il7770XZ5xxRlYvGuPHjwdjDIwxrFixovmdNPjBD34g7T/33HMtbp8gCIIgCKKx0LsXQRAmJPQQBNGkPPjggyguLsZVV12Ff/7zn1i0aFFW+S699FJs2rQJhx9+eDN7GOaee+7Bpk2bWtwuQRAEQRDE3kLvXgRBmCRb2wGCIDoW//M//wPGGG6++WbcfPPNWY8TLygoQFlZWTN7Z6e4uBjFxcWtYpsgCIIgCGJvoHcvgiBMKKKHIIgGMXv2bBlqq2533303AIAxBiCYEFDsN5Tx48fjyiuvxPTp07HPPvugZ8+eePjhh1FdXY2LL74YXbt2xUEHHYSXX365SfIRBEEQBEG0RejdiyCIhkJCD0EQDeLKK6/Epk2b5HbppZdiv/32w7e+9a0mt/XEE09g3333xTvvvIMrr7wSV1xxBc4//3wcffTRWL58OU4++WRceOGF2LVrV5PkIwiCIAiCaGvQuxdBEA2FVt0iCKLR3HDDDfjjH/+I119/Hfvvv3+jyxk/fjyGDRsmf5kSx9LptBxnnk6nUVxcjHPPPRdPPvkkAKC8vBy9evXC4sWLMWbMmL3KB3i/gD377LM4++yzG10XgiAIgiCI5oLevQiCyAaK6CEIolHceOONTfKiEccRRxwhPycSCXTr1g1DhgyRx3r27AkA2LJlS5PkIwiCIAiCaKvQuxdBENlCQg9BEA3mpptuwpNPPtmsLxoAkEqltH3GmHZMjEF3XbdJ8hEEQRAEQbRF6N2LIIiGQEIPQRAN4qabbsITTzzR7C8aBEEQBEEQBL17EQTRcGh5dYIgsuaXv/wlHnjgATz//PPIy8tDeXk5AGCfffZBbm5uK3tHEARBEATRsaB3L4IgGgMJPQRBZAXnHHfccQcqKiowduxY7dw777yDUaNGtZJnBEEQBEEQHQ969yIIorGQ0EMQRFYwxrBz584Ws/f666+Hjn3++eehY+bCgY3NRxAEQRAE0Zagdy+CIBoLzdFDEESb4P7770dhYSFWrlzZ4rYvv/xyFBYWtrhdgiAIgiCI1oLevQii48I4SasEQbQyGzduRE1NDQCgf//+yMnJaVH7W7ZsQUVFBQCgV69e6NKlS4vaJwiCIAiCaEno3YsgOjYk9BAEQRAEQRAEQRAEQXQQaOgWQRAEQRAEQRAEQRBEB4GEHoIgCIIgCIIgCIIgiA4CCT0EQRAEQRAEQRAEQRAdBBJ6CIIgCIIgCIIgCIIgOggk9BAEQRAEQRAEQRAEQXQQSOghCIIgCIIgCIIgCILoIJDQQxAEQRAEQRAEQRAE0UEgoYcgCIIgCIIgCIIgCKKDQEIPQRAEQRAEQRAEQRBEB4GEHoIgCIIgCIIgCIIgiA4CCT0EQRAEQRAEQRAEQRAdBBJ6CIIgCIIgCIIgCIIgOggk9BAEQRAEQRAEQRAEQXQQSOghCIIgCIIgCIIgCILoIJDQQxAEQRAEQRAEQRAE0UEgoYcgCIIgCIIgCIIgCKKDQEIPQRAEQRAEQRAEQRBEB4GEHoIgCIIgCIIgCIIgiA4CCT0EQRAEQRAEQRAEQRAdBBJ6CIIgCIIgCIIgCIIgOggk9BAEQRAEQRAEQRAEQXQQSOghCIIgCIIgCIIgCILoIJDQQxAEQRAEQRAEQRAE0UEgoYcgCIIgCIIgCIIgCKKDQEIPQRAEQRAEQRAEQRBEB4GEHoIgCIIgCIIgCIIgiA4CCT0EQRAEQRAEQRAEQRAdBBJ6CIIgCIIgCIIgCIIgOggk9BAEQRAEQRAEQRAEQXQQSOghCIIgCIIgCIIgCILoIJDQQxAEQRAEQRAEQRAE0UEgoYcgCIIgCIIgCIIgCKKDQEIPQRAEQRAEQRAEQRBEB4GEHoIgCIIgCIIgCIIgiA4CCT0EQRAEQRAEQRAEQRAdBBJ6CIIgCIIgCIIgCIIgOggk9BAEQRAEQRAEQRAEQXQQSOghCIIgCIIgCIIgCILoIJDQQxAEQRAEQRAEQRAE0UEgoYcgCIIgCIIgCIIgCKKDQEIPQRAEQRAEQRAEQRBEB4GEHoIgCIIgCIIgCIIgiA4CCT0EQRAEQRAEQRAEQRAdhDYt9Hz99dfo0aMHPv/884xpr7/+elx55ZXN7xRBEARBEEQHJdO71+uvvw7GGHbs2AEAeOWVVzBs2DC4rttyThIEQRAEEUubFnpuvfVWnHXWWdh///0zpr322mvxxBNP4NNPP21+xwiCIAiCIDogDXn3AoBTTjkFqVQKTz31VPM6RhAEQRBE1iRb24Eodu3ahUceeQT/+te/skq/7777YuLEiXjggQdwxx13NLN3BEG0BdLpNOrq6lrbDYJol6RSKSQSidZ2g2hDNPTdS/CDH/wA9957Ly688MJm8owgiLYAvXcRxN6Rk5MDx2mZWJs2K/S89NJLyM3NxZgxY+SxDz/8EDNnzsTChQvBOcewYcPw+OOP48ADDwQAnHHGGfjpT39KQg9BdHA45ygvL5dDBwiCaBwlJSUoKysDY6y1XSHaALZ3r5deegnTp0/Hhg0bMGbMGEyePDmU74wzzsDUqVPx3//+V76TEQTRcaD3LoJoGhzHwYABA5CTk9Psttqs0LNo0SKMGDFC7m/cuBHHHXccxo8fjwULFqCoqAhvvfUW6uvrZZqjjjoKX3zxBT7//POsQ44Jgmh/iJeNHj16oKCggDqpBNFAOOfYtWsXtmzZAgDo1atXK3tEtAXMd68NGzbg3HPPxZQpU3DZZZfh3XffxTXXXBPK179/f/Ts2ROLFi0ioYcgOiD03kUQe4/ruvjyyy+xadMm9O/fv9mfozYr9Kxbtw69e/eW+/fddx+Ki4vx9NNPI5VKAQAOOeQQLY9Iv27dOhJ6CKKDkk6n5ctGt27dWtsdgmi35OfnAwC2bNmCHj160DAuIvTu9cADD+DAAw/EnXfeCQA49NBDsXLlStx+++2hvL1798a6detazFeCIFoGeu8iiKaje/fu+PLLL1FfXy81jeaizU7GXFNTg7y8PLm/YsUKjBs3LrZBxEvrrl27mt0/giBaBzE2vKCgoJU9IYj2j3iOaM4FAgi/e3300UcYPXq0lmbs2LHWvPn5+fT+RRAdEHrvIoimQwzZSqfTzW6rzQo9++67L7Zv3y73hYgTx7Zt2wB4ShlBEB0bChsmiL2HniNCxXz3agjbtm2j9y+C6MDQvxcEsfe05HPUZoWe4cOHY9WqVXL/iCOOwKJFi2J/dfzggw+QSqVw2GGHtYSLBEEQBEEQHQbz3WvQoEF45513tDRLliwJ5du9ezf++9//Yvjw4c3uI0EQBEEQmWmzQs/EiRPx4Ycfyl+Wpk6dioqKClxwwQV49913sXbtWvzxj3/EmjVrZJ5FixZh3LhxWUX/EARBtDQLFy7EGWecgd69e4Mxhueee65VbPzgBz8AYwyMMaRSKfTs2RPf+MY38Oijj8J13Sb3qSORbdvtv//+Mp3Y+vbtGzpvdpqnT5+O8ePHa8cqKirw05/+FAMHDkReXh7KysowYcIEPPPMM+Ccy3SffPIJLr74YvTt2xe5ubkYMGAAvvvd7+Ldd99tnsYgOhzmu9fll1+OtWvX4rrrrsOaNWswd+5cPP7446F8S5YsQW5ubuSwLoIgiNaC3r3aN/Te1XjarNAzZMgQHHnkkfjrX/8KAOjWrRsWLFiAqqoqHH/88RgxYgQefvhhbc6ep59+GpdeemlruUwQBBFLdXU1hg4divvuu6/BecePH2/tYDXWximnnIJNmzbh888/x8svv4wTTjgB06ZNw+mnn66tZkiEybbtfv7zn2PTpk1ye++997Ry8vLyMHPmzFhbO3bswNFHH40nn3wSs2bNwvLly7Fw4UJ85zvfwU9+8hPs3LkTAPDuu+9ixIgR+Pjjj/HQQw9h1apVePbZZzFw4EDrKkkEYcN89+rfvz/+/ve/47nnnsPQoUPx4IMPYvbs2aF8f/7znzFp0iSaw4MgiDYHvXu1f+i9q5HwNswLL7zABw0axNPpdMa0L730Eh80aBCvq6trAc8Igmgtampq+KpVq3hNTU1ru7JXAODPPvts1umPP/54/thjjzWJjcmTJ/OzzjordHz+/PkcAH/44YcbZKczkW3b7bfffvw3v/lNZDn77bcfv+qqq3hOTg5/8cUX5fFp06bx448/Xu5fccUVvEuXLnzjxo2hMiorK3ldXR13XZcfdthhfMSIEdZ/L7dv3x7pR0d5noimoyHvXpxzvnXrVl5aWso//fTTZvaMIIjWoCP9O0HvXu0Peu9qPG12eXUAOO2007B27Vps3LgR/fr1i01bXV2Nxx57DMlkm64SQRBNDOe81VZ6KSgo6FCTE5544okYOnQonnnmGfzoRz9qFR+qq6sB6G1bW1uLuro6JJNJ5ObmhtLm5+fDcbwA1bq6OtTW1iKRSGirB9nSNiWNabsBAwbg8ssvx6xZs3DKKaeE/HJdF08//TQmTZqkLXktKCwsBAC89957+PDDDzF37lxr3UpKShpeIaLT0pB3LwD4/PPPcf/992PAgAEt4B1BEG0BevdqOlr73asl37vq6uqabElxeu/KTJsduiWYPn16Vi8a3/rWt0JLgBIE0fHZtWsXCgsLW2XriEsJDxw4EJ9//nmr2Rdt+9VXX8ljd9xxBwoLCzF16lQtbY8ePVBYWIj169fLY/fddx8KCwtxySWXaGn3339/FBYW4qOPPmo23822mzlzpna/3HvvvaE8P/vZz/DZZ5/hqaeeCp376quvsH37dgwcODDW7tq1a6V9gmgKsn33AoCRI0fiO9/5TjN7RBBEW4LevZqW1nz3asn3rmyGwTUEeu+Kp80LPQRBEJ2R2bNna/9YLVq0CJdffrl2TP2HtqngnHeoX8paErPtrrvuOqxYsUJuF110UShP9+7dce211+LGG29EbW1tqLxs7RIEQRAEsXfQu1f7gt674qFxTgRBtGsKCgpQVVXVarabi8svvxzf/va35f6kSZNw3nnn4dxzz5XHbGGle8tHH33UqkMwxLVU2/a6667D9OnTQ0Nzt2zZAgDaSotTpkzBpZdeikQioaUVv/g056qMZtvtu+++OOiggzLmmzFjBu6//37cf//92vHu3bujpKQEq1evjs1/yCGHAABWr15Ny1sTBEEQzQ69ezUtrfnu1ZLvXT/4wQ+a0nV678oACT0EQbRrGGPo0qVLa7vR5JSWlqK0tFTu5+fno0ePHln9A9ZYFixYgJUrV+Lqq69uNhuZsF3LnJwc5OTkZJU2lUpZx3839z2yN21XWFiIG264ATfffDPOPPNMedxxHFxwwQX44x//iJtuuin0cllVVYW8vDwMGzYMgwcPxp133onvfOc7ofHiO3bsaDPjxQmCIIj2D717NR2t/e7Vku9dTTU/D0DvXdlAQ7cIgiBaiKqqKhlOCgCfffYZVqxY0aRhwNna2LNnD8rLy7Fx40YsX74cs2fPxllnnYXTTz/dGupKBDRH21122WUoLi7G3LlzteO33nor+vXrh9GjR+PJJ5/EqlWrsHbtWjz66KMYPnw4qqqqwBjDY489ho8//hjjxo3DSy+9hE8//RTvv/8+br31Vpx11llNUW2CIAiCaHfQu1f7h967GgdF9BAEQbQQ7777Lk444QS5P2PGDADA5MmTm2yCumxtvPLKK+jVqxeSyST22WcfDB06FPfeey8mT57cLKtSdSSao+1SqRR+8Ytf4Hvf+552vLS0FEuWLMFtt92GX/7yl1i3bh322WcfDBkyBHfccQeKi4sBAEcddRTeffdd3Hrrrbj00kvx1VdfoVevXjj66KNx9913722VCYIgCKJdQu9e7R9672ocjLeX2YQIgiAA7N69G5999hkGDBigLeNIEETDoeeJIAiCiIP+nSCIpqMlnyeSDgmCIAiCIAiCIAiCIDoIJPQQBEEQBEEQBEEQBEF0EEjoIQiCIAiCIAiCIAiC6CCQ0EMQBEEQBEEQBEEQBNFBIKGHIAiCIAiCIAiCIAiig0BCD0EQ7RJaMJAg9h56jgiCIIhsoH8vCGLvacnniIQegiDaFalUCgCwa9euVvaEINo/4jkSzxVBEARBqNB7F0E0HbW1tQCARCLR7LaSzW6BIAiiCUkkEigpKcGWLVsAAAUFBWCMtbJXBNG+4Jxj165d2LJlC0pKSlrkhYMgCIJof9B7F0E0Da7rYuvWrSgoKEAy2fwyDAk9BEG0O8rKygBAvnQQBNE4SkpK5PNEEARBEDbovYsgmgbHcdC/f/8WEUsZpwGXBEG0U9LpNOrq6lrbDYJol6RSKYrkIQiCILKG3rsIYu/IycmB47TM7Dkk9BAEQRAEQRAEQRAEQXQQaDLmJmLhwoU444wz0Lt3bzDG8NxzzzWrvf333x+MsdA2ZcqUZrVLEARBEATRFmjpdy8A2LhxI77//e+jW7duyM/Px5AhQ/Duu+82u12CIAiCaAgk9DQR1dXVGDp0KO67774Wsbd06VJs2rRJbvPmzQMAnH/++S1inyAIgiAIojVp6Xev7du345hjjkEqlcLLL7+MVatW4c4778Q+++zTIvYJgiAIIlto6FYzwBjDs88+i7PPPlse27NnD37605/iz3/+M3bs2IHDDz8ct99+O8aPH98kNqdPn44XXngBa9eupZnwCYIgCILoVLTEu9f111+Pt956C4sWLWoapwmCIAiimaCInhZi6tSpWLx4MZ5++mm8//77OP/883HKKadg7dq1e112bW0t/vSnP+GHP/whiTwEQRAEQRBo+nev559/HiNHjsT555+PHj16YPjw4Xj44Yeb2GuCIAiC2HsooqcZMH9VWr9+PQ444ACsX78evXv3lukmTJiAo446CrNnz94re3/961/xve99L1Q+QRAEQRBEZ6Al3r3y8vIAADNmzMD555+PpUuXYtq0aXjwwQcxefLkJqkHQRAEQTQFFNHTAqxcuRLpdBqHHHIICgsL5fbGG2/gv//9LwBg9erV1smV1e3666+3lv/II4/g1FNPJZGHIAiCIAgCzfPu5boujjzySMyePRvDhw/HZZddhksvvRQPPvhga1WTIAiCIKwkW9uBzkBVVRUSiQSWLVuGRCKhnSssLAQAHHDAAfjoo49iy+nWrVvo2Lp16/Dqq6/imWeeaTqHCYIgCIIg2jHN8e7Vq1cvDB48WDs/aNAg/P3vf28irwmCIAiiaSChpwUYPnw40uk0tmzZgnHjxlnT5OTkYODAgQ0u+7HHHkOPHj1w2mmn7a2bBEEQBEEQHYLmePc65phjsGbNGu3Yxx9/jP3222+vfCUIgiCIpoaEniaiqqoKn3zyidz/7LPPsGLFCpSWluKQQw7BpEmTcNFFF+HOO+/E8OHDsXXrVsyfPx9HHHFEo0Ua13Xx2GOPYfLkyUgm6VISBEEQBNF5aOl3r6uvvhpHH300Zs+ejW9/+9t455138Pvf/x6///3vm7JaBEEQBLHX0GTMTcTrr7+OE044IXR88uTJePzxx1FXV4df/vKXePLJJ7Fx40bsu+++GDNmDG655RYMGTKkUTb//e9/Y+LEiVizZg0OOeSQva0CQRAEQRBEu6E13r1eeOEFzJo1C2vXrsWAAQMwY8YMXHrppXtbFYIgCIJoUkjoIQiCIAiCIAiCIAiC6CDQqlsEQRAEQRAEQRAEQRAdBBJ6CIIgCIIgCIIgCIIgOgidegbfdDqNm2++Gf+/vfuOa+p6/wD+CXvJFkRkOHGiOIu71Urdo7Xu2do66t5171Wt1l2tq8tVrXvi3gsQraICoiJLkL2T+/vDH/dLBFmGXBI+79crL5PDPTfPjYH75Mm55/zxxx8IDw9H+fLlMXjwYMycORMymSzf/gqFAq9fv0aZMmUKtD0REVFJIggCEhISUL58eejo8LsfKvmYexERkaZSZ95Vqgs9y5Ytw8aNG7Fz507UqlULd+7cwZAhQ2BhYYExY8bk2//169dwcnJSQ6RERETF5+XLl6hQoYLUYRDli7kXERFpOnXkXaW60HPt2jV07dpVXGLT1dUVf//9N27dulWg/mXKlAHw7j/K3Ny82OIkIiIqDvHx8XBychLPZ0QlHXMvIiLSVOrMu0p1oadp06b49ddf8eTJE1SrVg1+fn64cuUKVq1alev2aWlpSEtLEx8nJCQAAMzNzZlsEBGRxuIlMKQpst6rzL2IiEhTqSPvKtUX5E+bNg29e/dG9erVoa+vDw8PD4wbNw79+vXLdfslS5bAwsJCvHHoMBEREZVWly5dQufOnVG+fHnIZDL8+++/+fa5cOEC6tevD0NDQ1SpUgU7duwo9jiJiIhKm1Jd6Nm7dy/+/PNP/PXXX7h37x527tyJn376CTt37sx1++nTpyMuLk68vXz5Us0RExEREZUMSUlJqFu3LtavX1+g7YODg9GxY0d8+umn8PX1xbhx4/Dtt9/i1KlTxRwpERFR6VKqL92aPHmyOKoHAOrUqYOQkBAsWbIEgwYNyrG9oaEhDA0N1R0mERERUYnTvn17tG/fvsDbb9q0CRUrVsTKlSsBADVq1MCVK1fw888/w8vLq7jCJCIiKnVK9Yie5OTkHMua6erqQqFQSBQRsG/fPty5cwcZGRmSxUBERESkatevX0fbtm2V2ry8vHD9+vUP9klLS0N8fLzSjYiIiPJWqkf0dO7cGYsWLYKzszNq1aoFHx8frFq1CkOHDpUkntTUVPTp0wdyuRwvXrwQ5wB68OAB3rx5Aw8PD1hYWEgSGxEREdHHCA8Ph729vVKbvb094uPjkZKSAmNj4xx9lixZgnnz5hVLPIGBgThx4gRsbGzQp0+fYnkOIiIiKZTqET1r167FV199hZEjR6JGjRqYNGkSvv/+eyxYsECSeGJiYvDZZ5+hevXqqFChgti+ceNGfPrpp1i4cKHYplAocOzYMYSGhkIQBCnCJSIiIipWxTk/op+fH0aPHo1169apbJ9EREQlQake0VOmTBmsXr0aq1evljoUAED58uVx+vTpHO2WlpZwdXVF/fr1xbZnz56hU6dOMDIyQkJCAvT03v1X+vv7iytZvH9ZGhEREZFUypUrh4iICKW2iIgImJub5zqaByje+RGrVKmCL7/8ErVr1y6W/RMREUmFlQANsGjRIgQHB4uTRgPA27dvUatWLdSvX18s8gDvJph2c3PDli1bxLaEhATcu3cPaWlpao2biIiIKIunpye8vb2V2s6cOQNPT09J4nF3d8f+/fsxd+5cSZ6fiIiouLDQo0FkMpl4v0mTJnjw4AEuXbqktI2enh6MjIxQr149se3SpUto0KABGjdurLStXC4v1niJiIhIeyUmJsLX1xe+vr4A3i2f7uvrixcvXgB4d9nVwIEDxe2HDx+OoKAgTJkyBY8fP8aGDRuwd+9ejB8/XorwiYiItBYLPRpOV1dX6fHRo0eRkJCAhg0bim0xMTGwsrJCnTp1lLZt1qwZevTogaCgILXESkRERNrjzp078PDwgIeHBwBgwoQJ8PDwwOzZswEAYWFhYtEHACpWrIhjx47hzJkzqFu3LlauXImtW7dKvrS6IAic75CIiLSKTOCZrcji4+NhYWGBuLg4mJubSx1OngRBQHJyMkxNTQEAAQEBqF69OvT19fH69WvY2toCeLeMaXFdC09ERCWLJp3HiADVv2c/+eQT+Pn54cqVK2jQoIEKIiQiIsqdOvMujugpJWQymVjkAQA3Nzf4+/tjy5YtYpEHAAYNGoTGjRvj8uXLUoRJREREpDbp6elITU1FZGSk1KEQERGpTKledau0q127ttJKEykpKTh27BgSExOVikJJSUkwMjLKcZkYERERkSb7+++/YWhoiPLly0sdChERkcpwRA+JjI2NERQUhG3btonX2wPAsmXL4OLigl27dkkYHREREZFqubm5wdXVFQYGBlKHQkREpDIs9JCSsmXLYsiQIUorfB09ehShoaFKc/ekpqYiPj5eihCJiIiIiIiI6ANY6KF8Xb9+Hfv370fXrl3Ftn379qFcuXKYPn26hJERERERFV1qaio2b96M0aNHQ6FQSB0OERGRSrDQQ/kyNDTEl19+CSMjI7Ht3LlzSElJgYmJidgmCAJCQkKkCJGIiIio0PT09DBx4kSsW7cODx48kDocIiIileBkzFQk27Ztw/Dhw+Hi4iK23bx5E56enujQoQOOHj2qdPkXERERUUmjp6eH77//HkZGRrCwsJA6HCIiIpVgoYeKRCaToUmTJkptN27cgEwmg7W1tVKR5/Tp02jUqBGsrKzUHSYRERFRnlauXCl1CERERCrFQg+pzLhx4/Dll18iIyNDbIuIiICXlxd0dXURHR0tflsmCAJH/BARERERERGpGOfoIZVycnJCpUqVxMevXr1C9erVUadOHaUh0d9++y1atWqF8+fPSxEmERERkUihUMDHxwevXr2SOhQiIqKPxkIPFasGDRrg0aNHuHr1qtgmCAKOHz+OS5cuKW0bEBCAlStXwt/fX91hEhERUSk2ZMgQ1K9fH5s3b5Y6FCIioo+mEZduTZgwodB9Zs6cCWtr62KIhooi++pcAHDp0iV4e3vD09NTbDt8+DCmTJmCTp064ciRI2J7aGgoypcvz0u9iIiIioB5VP68vLzwzz//ID09XepQiIiIPppMEARB6iDyo6OjA09PTxgYGBRo+ytXriAgIEDpEqLiEB8fDwsLC8TFxcHc3LxYn6s0OHjwILZs2YKuXbvi+++/BwAkJyfDysoK9vb2uHfvHmxtbSWOkohIe/A8VjqU1DyqKIrrPZuWlga5XJ7jiykiIiJVUWfepREjeoB3RQA7O7sCbVumTJlijoaKQ/fu3dG9e3eltgcPHiCrFmljYyO2L1++HCEhIfj222/h4eGh1jiJiIg0DfOovBkaGkodAhERkcpoxBw927dvV5rINz+bN2+Gvb19MUZE6tK4cWPExsbixIkTSpdu7dq1Cxs2bEBgYKDYlpSUhKCgICnCJCIiKrGYRxXOy5cvkZmZKXUYRERERaYRhZ5BgwZBT6/gg4/69u0LU1PTYoyI1MnExAS1atUSHwuCgIULF2LUqFH49NNPxfb9+/ejcuXKGDRokBRhEhERlUjMowpu2LBhcHV1xbFjx6QOhYiIqMg0otADAI6Ojpg2bRqePHkidSgkMZlMhm7dumHdunVKl3M9fvwYMpkMVatWFduyVvji5IpERFSaMY8qGCsrKygUCly7dk3qUIiIiIpMYwo9o0aNwv79+1GjRg20aNECO3bsQHJystRhUQmyZMkSvHz5EsOHDxfbrl+/jo4dO6JatWqQy+USRkdERCQd5lEFM27cODx8+BDLli2TOhQiIqIi05hCz6xZs/Ds2TN4e3ujUqVK+OGHH+Dg4IBhw4bh5s2bUodHJYSjo6PSylzh4eFwcHBAq1atoKurK7b/8ccfnM+HiIg0gpWVFaytrQt0+xDmUQVTvnx51KxZU+owiIiIPopGLK+em8TEROzevRs7duzAtWvXUKNGDXzzzTeYMGGC2mLgsrSaQS6XIy4uTkyAw8PD4ejoCIVCgRcvXsDJyUniCImIpMHzmGbYuXOneD86OhoLFy6El5cXPD09AbwbvXrq1CnMmjUL48ePL9A+S0IeVRTqfM8mJCQgNTUVZcuWLdbnISKi0kGd5zC1FHoKkzSsWrWq0Ps/duwYBg4ciNjYWLVensMEWTP9999/GDduHJKSknD16lWxfceOHbCyskL79u1hYGAgYYREROrB85jm+fLLL/Hpp5/ihx9+UGpft24dzp49i3///bfQ+5QqjyoKdb1n9+7dixEjRqB9+/b4448/iu15iIio9FBn3lXwJRg+go+Pj9Lje/fuITMzE25ubgCAJ0+eQFdXFw0aNCjwPpOTk7F3715s374dV65cQeXKlTF58mSVxk3aqWbNmjh9+rTSBM0ZGRmYMmUKoqKicOzYMXTo0EHCCImIiHJ36tSpXOeP+eKLLzBt2rQC74d5VN4qV66Mt2/f4v79+0hOToaJiYnUIRERERWYWgo958+fF++vWrUKZcqUwc6dO2FlZQUAePv2LYYMGYIWLVrku69r165h27Zt2LdvHzIzM/HVV19hwYIFaNmyZbHFT9op+6id5ORkDBw4EN7e3mjXrp3Yvnv3bjx58gQDBw6Eq6urBFESERH9j42NDQ4dOoSJEycqtR86dEhpJcoPYR5VMA0aNMDZs2fRsmXLQi1NT0REVBKofY4eR0dHnD59GrVq1VJqf/DgAdq1a4fXr1/n2m/58uXYvn07njx5goYNG+Kbb75Bnz59UKZMGXWEnSsOedd+TZo0wa1bt/Dzzz9j3LhxUodDRKRSPI9pnh07duDbb79F+/bt0aRJEwDAzZs3cfLkSWzZsgWDBw/OtV9JzKOKgu9ZIiLSVFp36VZ28fHxiIqKytEeFRWFhISED/ZbsWIF+vfvj3379qF27drFGSIRAEAQBIwaNQqWlpbo06eP2O7r64t9+/Zh4sSJea5wQkREpGqDBw9GjRo18Msvv+DAgQMAgBo1auDKlSti4Sc3zKOKThAEbN26FR4eHmjYsKHU4RAREeVL7SN6Bg4ciMuXL2PlypVo3LgxgHffRE2ePBktWrRQWlkiu4yMDOjr66sz1HzxW6XSqWvXrjh8+DAGDx6M7du3Sx0OEVGR8TxWepTEPKoopHjPLl26FNOnT4eTkxP8/f1hYWGhluclIiLtos5zmE6x7j0XmzZtQvv27dG3b1+4uLjAxcUFffv2xRdffIENGzbk2ueXX34p1CoQmzZtynN0ENHHGDJkCDw8PJQmvUxISOB7joiI1CIwMBAzZ85E3759ERkZCQA4ceIEHj58mOv2zKM+zogRI1CrVi1MmjSJBVEiItIIai/0mJiYYMOGDYiOjoaPjw98fHwQExODDRs2wNTUNNc+48ePL1TCkbV6ElFx6NatG+7evSuuGgcAy5YtQ8WKFfH7779LGBkREWm7ixcvok6dOrh58yb++ecfJCYmAgD8/PwwZ86cXPsUZx61fv16uLq6wsjISJzXLi+rV6+Gm5sbjI2N4eTkhPHjxyM1NbXAsUnBwsIC9+7dw5gxYyCTyaQOh4iIKF+SLSMQFhaGsLAwtGzZEsbGxhAE4YMnT0EQ0KZNmwKvepCSkqLKUIlyyP5eVSgUOHnyJKKjo2FmZiZhVEREpO2mTZuGhQsXYsKECUoTKX/22WdYt25drn2KK4/as2cPJkyYgE2bNqFJkyZYvXo1vLy8EBAQADs7uxzb//XXX5g2bRq2bduGpk2b4smTJxg8eDBkMhlWrVpVoOeUSvaVOlNSUjBjxgzMmjVLXEGWiIioJFF7oSc6Ohpff/01zp8/D5lMhqdPn6JSpUr45ptvYGVlhZUrV+bo86FvqD6ka9eunCSX1EZHRwc3btzAkSNH0K1bN7H96NGjeP78OYYNGwZDQ0PpAiQiIq3h7++Pv/76K0e7nZ0d3rx5k2uf4sqjVq1ahWHDhmHIkCEA3l3ydezYMWzbtk3p8uYs165dQ7NmzdC3b18AgKurK/r06YObN28WKj6pjR49Gr/99huuX7+Oa9eucZQPERGVOGov9IwfPx76+vp48eIFatSoIbb36tULEyZMUEmhp6BcXV0REhKSo33kyJFYv359sTwnaSc9PT10795dfCyXyzFlyhQ8evQIKSkpmDx5soTRERGRtrC0tERYWBgqVqyo1O7j4wNHR8dc+xRHHpWeno67d+9i+vTpYpuOjg7atm2L69ev59qnadOm+OOPP3Dr1i00btwYQUFBOH78OAYMGKDy+IrTmDFjcOHCBSxevJhFHiIiKpHUXug5ffo0Tp06hQoVKii1V61aNdeiS3G6ffu20uSEDx48wOeff46ePXuqNQ7SPoIgYMyYMdi4cSO+//57sT06Ohrm5uZasfIJERGpX+/evTF16lTs27cPMpkMCoUCV69exaRJkzBw4EC1xfHmzRvI5XLY29srtdvb2+Px48e59unbty/evHmD5s2bQxAEZGZmYvjw4fjxxx8/+DxpaWlIS0sTH8fHx6vmAD6Cu7s7Hj16pHQuv3DhAhwdHVG1alUJIyMiInpH7ZMxJyUlwcTEJEd7TEyM2i9vKVu2LMqVKyfejh49isqVK6NVq1ZqjYO0j56eHoYPHw5fX1+lFTq+//571KhRAxcvXpQwOiIi0lSLFy9G9erV4eTkhMTERNSsWRMtW7ZE06ZNMXPmTKnDy1PWKJgNGzbg3r17OHDgAI4dO4YFCxZ8sM+SJUtgYWEh3pycnNQY8YdlL/IkJSWhf//+qFWrFq5duyZhVERERO+ovdDTokUL7Nq1S3yc9W3U8uXL8emnn6o7HFF6ejr++OMPDB069IPDcNPS0hAfH690I8pL9vdSbGwsLl++jKCgINja2koYFRERaSoDAwNs2bIFgYGBOHr0KP744w88fvwYv//+O3R1ddUWh62tLXR1dREREaHUHhERgXLlyuXaZ9asWRgwYAC+/fZb1KlTB927d8fixYuxZMkSKBSKXPtMnz4dcXFx4u3ly5cqP5aPFR8fD3d3d9jb26NBgwZie/aRSEREROqk9ku3li9fjjZt2uDOnTtIT0/HlClT8PDhQ8TExODq1avqDkf077//IjY2FoMHD/7gNkuWLMG8efPUFxRpFUtLSwQGBsLb2xu1atUS27du3QpLS0v06NEDOjpqr70SEZEGcnZ2hrOzs2TPb2BggAYNGsDb21tciEChUMDb2xs//PBDrn2Sk5NznOeyilOCIOTax9DQsMQvaODg4IDjx4/j9evXSrG2bt0aFhYWWLNmDdzc3CSMkIiIsnv16hXKlSsHPT09REZG4ocffsCQIUPQvn17qUNTGZnwoTNrMYqLi8O6devg5+eHxMRE1K9fH6NGjYKDg0Oe/TIyMlC9enUcPXpUaSJnVfDy8oKBgQGOHDnywW1yu07cyckJcXFxSpfnEBXU27dv4erqivj4eBw5cgSdOnWSOiQiKkXi4+NhYWHB85gGmTBhQq7tMpkMRkZGqFKlygdXzVJ1HrVnzx4MGjQImzdvRuPGjbF69Wrs3bsXjx8/hr29PQYOHAhHR0csWbIEADB37lysWrUKv/76K5o0aYJnz55hxIgRaNCgAfbs2VOg59SU9+yzZ89QtWpV6Ovr49WrV+Jy8+np6UpLtRMRkfo1b94csbGx2L17N/766y8sWbIEbm5u+O+//4r1i3d1nsPUPqIHACwsLDBjxoxC99PX10dqaqrK4wkJCcHZs2dx4MCBPLfThG+VSLPo6elh3Lhx8Pb2RocOHcT2qKgo2NracjUPIiJS4uPjg3v37kEul4ujRJ48eQJdXV1Ur14dGzZswMSJE3HlyhXUrFlTqa+q86hevXohKioKs2fPRnh4OOrVq4eTJ0+KEzS/ePFCKWGeOXMmZDIZZs6cidDQUJQtWxadO3fGokWLVBZTSVGlShU8e/YMN27cEIs8ADB48GAEBwdjxYoVaN68uYQREhGVTlFRUbh37x7S0tJgY2OD6dOnIzAwEKNHj9aqqyvUPqLn/v37uQfy/99EOTs751lMWbx4MZ48eYKtW7dCT081daq5c+di8+bNePnyZaH2qSnfKlHJJwiCWNRRKBSoV68ezMzMsH37dg73JqJiw/OY5lm9ejUuX76M7du3i/9ncXFx+Pbbb9G8eXMMGzYMffv2RUpKCk6dOpWjf3HkUeqkye/ZlJQU2NnZITExEbdv30bDhg0BvLukzcjISKs+YBARlWRxcXG4du2a2i/VUuc5TO2FHh0dHfEDbdZTZx+1oK+vj169emHz5s0wMjLK0b979+7w9vaGmZkZ6tSpA1NTU6Wf5zcq530KhQIVK1ZEnz59sHTp0kL11eRkg0qu+/fv45NPPoGhoSEePXr0wUktiYg+Fs9jmsfR0RFnzpzJMVrn4cOHaNeuHUJDQ3Hv3j20a9cOb968ydFf1XmUumn6ezY8PBxHjhzBt99+K+a/c+fOxbZt27Bo0SIMGDBA4giJiKi4aPWlWwcPHsTUqVMxefJkNG7cGABw69YtrFy5EnPmzEFmZiamTZuGmTNn4qeffsrR39LSEl9++aXK4jl79ixevHiBoUOHqmyfRB/D3d0dgYGBOYo8z549Q5UqVSSMjIiIpBYXF4fIyMgchZ6oqChxNVBLS0ukp6fn2l/VeRQVTrly5TBs2DCltsOHD+Ply5dKq6ZlZmZCLpdzygAiIjWJiIjA7t274erqiq5du0odzkdTe6Fn0aJFWLNmDby8vMS2OnXqoEKFCpg1axZu3boFU1NTTJw4MddCz/bt21UaT7t27T640gORVBwcHJQmJ/fx8UGjRo3Qu3dvbN++Hfr6+hJGR0REUunatSuGDh2KlStXolGjRgCA27dvY9KkSeLqV7du3UK1atVy7a/qPIo+3rVr13Do0CF06dJFbNu/fz/Gjh2L6dOnY9y4cdIFR0SkRS5fvox169ahU6dOOUZQ/v7775g8eTJatmzJQk9R+Pv7w8XFJUe7i4sL/P39AQD16tVDWFhYnvuJiopCQEAAAMDNzQ1ly5ZVfbBEJcSVK1cgCAIyMjJY5CEiKsU2b96M8ePHo3fv3sjMzATwbmL/QYMG4eeffwYAVK9eHVu3bs1zP8yjSg4jIyP06tVLqW3v3r2IjIxEbGysNEEREWkhb29v7N27FwYGBjkKPT179sTx48fRuXNniaJTLbXP0ePh4YG6devi119/FZeXzMjIwLBhw+Dn5wcfHx9cvXoV/fv3R3BwcI7+SUlJGD16NHbt2gWFQgEA0NXVxcCBA7F27VqYmJio7Vg0/Tpx0iy3b9+Gk5OTeDlXUlISwsPDUblyZYkjIyJNxfOY5kpMTERQUBAAoFKlSjAzMytQv5KURxVFaXnPZmRk4ODBg2jZsqV43r937x6+++47TJgwAX379pU4QiIizePj44Pjx4+jXr166Nixo9qfX53nMLVP779+/XocPXoUFSpUQNu2bdG2bVtUqFABR48excaNGwEAQUFBGDlyZK79J0yYgIsXL+LIkSOIjY1FbGwsDh06hIsXL2LixInqPBQitWrUqJHSnD1z585F7dq18euvv0oYFRERScHMzAzu7u5wd3cvcJEHYB6lKfT19fH1118rnffXr1+Pu3fv4ujRoxJGRkSkuTw8PDBjxgxJijzqpvYRPQCQkJCAP//8E0+ePAHwbshw3759UaZMmXz72traYv/+/WjdurVS+/nz5/H1118jKiqqOELOVWn5VolKHrlcjo4dO+LUqVM4evRoqfhjRUSqx/OYZrpz5w727t2LFy9e5Jh0Ob9Vs0pSHlUUpfk9++bNG2zZsgWff/65uDT7mzdvMHz4cPTt2xc9evSQOEIqyRQKBWJiYmBrayu2Xbp0CS9fvkTDhg3h5uYmYXREJUd6ejquXbuGVq1aKa0OrgpaveoWAJQpUwbDhw8vUt/k5GTY29vnaLezs0NycvLHhkakEXR1dXHixAlcuHABn376qdj+8OFDuLi4FOrbXSIi0hy7d+/GwIED4eXlhdOnT6Ndu3Z48uQJIiIi0L1793z7M4/SXLa2tpg+fbpS29atW/HPP/8gLCxMqdAjCILKP6CQ5rp8+TI6duwIOzs7PHv2TGzfuHEjdu/ejdWrV4uFnrCwMDRq1Aju7u44dOgQ54YkrRESEoKwsDDUqVMHpqamuW4jl8vh7OyMiIgI3L9/H3Xq1FFzlKqj9ku3svz33384efIkDh8+rHTLj6enJ+bMmYPU1FSxLSUlBfPmzYOnp2dxhkxUoshkMqUiT1JSEjp16oRatWqJE5sTEZF2Wbx4MX7++WccOXIEBgYGWLNmDR4/foyvv/4azs7O+fZnHqVdunbtiqlTp+KHH34Q29LT0+Hq6orevXtzMudS6s6dO7h37574uGbNmkhMTERoaKjSKMDatWujbdu2cHV1FdtCQ0MRGhoKf39/pSLPokWLMGLECPj6+qrjEIhU7s8//4Snpye+++67D26jq6sLd3d32NnZ4dWrV2qMTvXUPqInKCgI3bt3h7+/P2Qymbi0eda3DnK5PM/+q1evxhdffIEKFSqgbt26AAA/Pz8YGRnh1KlTxRs8UQkWEhIi3q9YsaKEkRARUXEJDAwUL9c1MDBAUlISZDIZxo8fj88++wzz5s3Lsz/zKO1So0YNLF26VKnt0qVL4mV92S8NOHbsGPT09NC8efMPfptNmm/lypWYNGkSevTogX/++QcAYGNjg4cPH6JKlSpKxZsZM2ZgxowZSv1r166NK1eu5CgS/vnnn3j06BE+++wz1KtXD8C7IjEAGBsbF98BEalQ+fLlUatWrTy32bt3LywsLDR+VKTaR/SMHTsWFStWRGRkJExMTPDw4UNcunQJDRs2xIULF/LtX6dOHTx9+hRLlixBvXr1UK9ePSxduhRPnz7N9z+NSJvVrFkTDx48wLFjx5Qu3bpw4QIkmIqLiIiKgZWVFRISEgAAjo6OePDgAQAgNja2QJdeMY/Sfp9++imuXbuGdevWQUfnf6n+rFmz8MUXX+DQoUNiW2xsLIKCgpgnaJEOHTpAX18flpaWSv+vNWrUKNBlWEZGRmjWrJnS/I+CIGD58uUYMWIE2rdvL7b//fffsLOzy1EsIiqJfvzxR4SGhua4BPZ9lpaWGl/kASQY0XP9+nWcO3cOtra20NHRgY6ODpo3b44lS5ZgzJgx8PHx+WDfjIwMVK9eHUePHsWwYcPUGDWRZjA1NUXt2rXFx6dOncIXX3yBNm3a4OTJk9DTk2RaLiIiUpGWLVvizJkzqFOnDnr27ImxY8fi3LlzOHPmDNq0aZNnX+ZRpYOurm6Oy/AUCgXq16+P6Ohopcu+//33XwwZMgQdO3ZUWs0rODgYFSpU4PwsGuDff/9FeHi4OP9pjRo18PLly1zn4ioqmUyGTp06oVOnTkrtFy9eRGJiIoyMjMQ2uVyOv//+G15eXihbtqzKYiBSFW0o4hSE2j/1yeVycXUtW1tbvH79Gm5ubnBxcUFAQECeffX19ZWuKSeivL1+/RrGxsaoXbs2izxERFpg3bp1Yi40Y8YM6Ovr49q1a/jyyy8xc+bMPPsyjyq9dHR0sHXr1hzt4eHh0NfXR/Xq1cU2hUKBOnXqICMjA//99x8qV64MAHj+/DkSEhJQuXJlmJiYqC12+rAzZ86ge/fuMDU1RadOnVChQgUAUGmRJy87duzAqFGj4OjoKLbdvHkTAwYMgI2NDSIiIqCrq6uWWIhUacOGDdi+fTt++OEHDBo0SOpwikTtl27Vrl0bfn5+AIAmTZpg+fLluHr1KubPn49KlSrl23/UqFFYtmwZMjMziztUIo03ZMgQPHz4EAsXLhTboqKicOPGDQmjIiKiosjMzMTRo0fFD046OjqYNm0aDh8+jJUrV8LKyirffTCPouymTZuG+Ph4pUtvwsPDxW+8XVxcxPZNmzbB3d0dU6dOFdvkcjnmzp2LrVu3Ii0tTX2BEwCgTZs2+PzzzzFq1ChJRs/IZDI0btxYqdCTlJQEDw8PeHl5KRV5BgwYgAkTJuDly5dqj5Po1q1b+OSTTzBlypQCbf/q1SvcuXOnQFPLlFRq/4p/5syZSEpKAgDMnz8fnTp1QosWLWBjY4M9e/bk2//27dvw9vbG6dOnc10a7cCBA8USN5Gmen9i5gkTJuDPP//EihUrMHHiRImiIiKiwtLT08Pw4cPx6NGjIu+DeRS9z8jISOnSm/LlyyMuLg5hYWE5RgNbWVmhSpUq4uPIyEjMmzcPOjo6GDx4sNg+Z84c7Nu3D2PGjBEvKcrIyMDOnTtRtmxZdOrUiSM9iiA5ORkbN27EuHHjoKurCx0dHZw4caJEvZaff/45Pv/8c2RkZIhtb968wV9//QWFQoGxY8eK7a9fv4aJiQksLS0liJRKE39/f9y8eRMWFhYF2r5v376oU6cOWrZsWcyRFR+1F3q8vLzE+1WqVMHjx48RExMDKyurAl0vZ2lpiS+//LI4QyTSWpmZmdDR0YFMJtPoP1xERKVV48aN4evrqzTSojCYR1FB6OjoKI3SAIClS5di6dKlOVbI/e6775CcnKxUFHry5AkePXokrsoEvCsKDRs2DLq6ukpLfI8fPx67du3Cjz/+KH4BlZycjJEjR8LS0hI//fSTuO+bN2/i6dOncHd3h7u7O4B3l5r5+vrCyMgI1atXFyegTk1NhSAIMDQ0VJqUWlMpFAq0atUKd+7cgVwuF0cmlKQiT3bZ53cyMzPDvn37cO/ePaW/XfPmzcO2bduwZMkSTJo0SYowqZT44osvsG/fPnEKmfzUrl1bad5TTaTWQk9GRgaMjY3h6+ur9MJZW1sXqH9mZiY+/fRTtGvXDuXKlSuuMIm0lp6eHnbu3ImZM2eiatWqYvu9e/dQrVo1pdW6iIio5Bk5cqR4+UODBg1yjMjJ+vCbG+ZRpArZCwsODg7YvHlzjm2WLl2Kb7/9VpzfB3hXqOjQoQPkcrlS4eXNmzeIiYlRWiEqJiYGO3fuhK6uLn7++WexfdeuXdiwYQNmzZolvtcTExPRoEEDAO+KO4aGhgCA2bNni6OXf/rpJwDvfgcqVKgAAwMD3L9/XxxJsn37duzcuRPdu3dXGnHyww8/wMbGBmPGjIGNjQ2AdytQSTGZq46ODkaMGIHZs2eLy5trCiMjI/To0QM9evRQag8MDERmZibq1q0rtoWHh+PcuXPo0qUL81JSGUdHR3z11VdSh6FWai306Ovrw9nZOcc3AQWliiHLRASlIk9oaCi8vLxgbW2NU6dOwdXVVbrAiIgoT7179wYAjBkzRmyTyWTih8+8cizmUaQuLi4uOUadOTk54dixYzm2/fnnnzF9+nTY2tqKbaampli6dCnS0tKUiipubm74/PPPUa1aNbEtIyMDjo6OSE1NhYGBgdieNWro/baIiAgAUBqBFBgYiIsXLyoVSgVBwIYNGyAIgnj5GfBuktYVK1Zg6NChmD17dsFflCLw8fGBoaEhatasCeDd3Is9e/Ys8KiEku7s2bN49uyZUu75999/Y8KECfjss8/g7e0tXXBU6oWHh8Pb2xvGxsY5ipSaQO2Xbs2YMQM//vgjfv/99wKP5MmucePG8PHxKfKQZSJSFhERAUNDQ5iYmPAbXiKiEi44OPij+jOPopLG1tZWqcgDvJsLKPukz1nGjBmjVOQEABsbG7x69SrHtitWrMCCBQuURiAZGhrCz88P6enpSiuH9enTB3Xr1lUagSQIAubMmYOwsDDY2dmJ7Q8fPkRISIjSZWlyuRxeXl5o27YtfvjhB5WMRPn777/Rv39/1K1bFzdu3ICBgQFkMpnWFHmyZJ/zCQDKlCmDKlWqKH2wTk9Px9ixY9G9e3e0adOmxF6uRiWTIAj4559/4OLigvr16xf4/XP27FkMGDAAnp6eGlnokQnZx0mqgYeHB549e4aMjAy4uLjkGHJ87969PPvv3bsX06dPx/jx4ws9ZFnV4uPjYWFhgbi4OJibm6vteYlULTo6GvHx8UoTNyclJeX4/SIi7cLzWOlTkvKoouB7lqQWExODhw8fwt7eXhxZ5OPjg/r168Pc3BxRUVHiKKI3b97A2tq6SHMERUZGolq1avDy8sKvv/5a4ElktYEgCJDL5eKoq+PHj6Njx46wt7dHaGgoCz1UKOHh4XBwcICOjg5SU1OV5o/KS2BgIPr3749WrVph6dKlKolFnecwtY/o6dat20f1/5ghy0SUOxsbG/HacwDYuXMn5syZg927d+OTTz6RMDIiInrf77//jk2bNiE4OBjXr1+Hi4sLVq9ejYoVK6Jr16559mUeRfRxrK2t0aJFC6U2V1dXbN68GbGxsUqXin399dd48uQJdu3ahc8+++yD+3z58iV++eUXpKWl4ZdffgEA2NnZ4dGjR3BwcCieAynBZDKZ0qV1rq6uGDFiBMqWLatU5Pnxxx/RsmVLeHl5STJvEmmGpKQkNG3aFBkZGQUu8gBA5cqVcf369WKMrHipfUTPxwoJCcnz5+ocisxvlUgbyeVy1K9fH/fv38eCBQswc+ZMqUMiomLC85jm2bhxI2bPno1x48Zh0aJFePDgASpVqoQdO3Zg586dOH/+fJ79S1IeVRR8z5KmSE5ORoUKFfD27VsEBQWJo6a3bduGbdu2YdiwYRg0aBAA4OnTp6hWrRp0dHTw9OlTVKpUScrQNYK/vz/c3d2ho6ODZ8+eKY1KJyqptHpEDwDExsZi//79CAwMxOTJk2FtbY179+7B3t4+x1KO7yvpCQiRptPV1cXly5exfv16celOIiIqGdauXYstW7agW7duSkPJGzZsWKDliZlHEamHiYkJXr9+jRs3bigVIZ48eYKrV6+ibt26YqGnatWqmDJlCj755BM4OztLFbJGsbKywrhx45CSkqL0+j58+BA1atQo0uVyRNpE7b8B9+/fR7Vq1bBs2TL89NNPiI2NBQAcOHAA06dPL9A+fv/9dzRr1gzly5cXv5lavXo1Dh06VFxhE5Uq5ubmmD59ujg8VqFQYMiQIfl+U0xERMUrODgYHh4eOdoNDQ2RlJRUoH0wjyJSDyMjI7Ru3VqpbdCgQdi3bx969eql1L5s2TJ0795d6ZIl+rAKFSrg559/xqZNm8S2N2/eoEmTJvjkk0/E1dWISiu1F3omTJiAwYMH4+nTpzAyMhLbO3TogEuXLuXbf+PGjZgwYQI6dOiA2NhY8VpyS0tLrF69urjCJirVtmzZgh07dqBz586Ijo6WOhwiolKrYsWK8PX1zdF+8uRJ1KhRI9/+zKOIpFWjRg189dVXaNmypdShaB1fX19xrrGyZctKHQ6VEEOGDEHTpk1x7tw5qUNRK7UXem7fvo3vv/8+R7ujoyPCw8Pz7Z81ZHnGjBlKk3E1bNgQ/v7+Ko2ViN7p378/vvnmG6xevVpp0mYiIlKvCRMmYNSoUdizZw8EQcCtW7ewaNEiTJ8+vUCX2zKPIiJt1bZtWzx79gy7du0SL91SKBT4+eefER8fL3F0JJXbt2/j+vXryMzMlDoUtVL72EBDQ8Ncf9GePHlSoMqrKoYsE1HhmJqaYuvWrUptgYGBePz4MTp27ChRVEREpc+3334LY2NjzJw5E8nJyejbty/Kly+PNWvWiCtq5YV5FBFpM3t7e9jb24uPt23bhgkTJmDz5s148OABL40rhXbt2oXAwEDUr19f6lDUSu0jerp06YL58+cjIyMDwLvl8168eIGpU6fiyy+/zLf/xw5ZJqKPl56ejt69e6NTp05K10YTEVHx69evH54+fYrExESEh4fj1atX+OabbwrUl3kUEZUmFStWROXKlfHdd9+xyFNK1a9fHz179oStra3UoaiV2gs9K1euRGJiIuzs7JCSkoJWrVqhSpUqKFOmDBYtWpRv/48dskxEH08QBDRt2hTW1tYc0UNEpEYLFy5EcHAwgHer+tjZ2RWqv6rzqPXr18PV1RVGRkZo0qQJbt26lef2sbGxGDVqFBwcHGBoaIhq1arh+PHjhX5eIqKCaNOmDfz9/TFmzBixLTAwENu2bYMgCBJGRlS8ZIJE7/ArV67g/v37SExMRP369dG2bdsC9/3zzz8xd+5cBAYGAgDKly+PefPmFfjbLFWJj4+HhYUF4uLiYG5urtbnJioJoqKilC65DAoKQqVKlSSMiIgKg+cxzVO3bl08ePAATZo0Qf/+/fH1118X+ltKVeVRe/bswcCBA7Fp0yY0adIEq1evxr59+xAQEJBrASo9PR3NmjWDnZ0dfvzxRzg6OiIkJASWlpaoW7dugZ6T71ki+hiZmZlo2bIlrl+/jtmzZ2PevHlSh0TF6MmTJ/Dz80OtWrVQs2ZNqcNR6zlM7YWely9fwsnJSSX7Sk5OFkcHSYHJBtH/3Lt3D56enhg8eDDWrl0LAwMDqUMionzwPKaZHj58iD///BO7d+/Gq1ev8Pnnn6Nfv37o1q0bTExMCryfj82jmjRpgkaNGmHdunUA3k166uTkhNGjR2PatGk5tt+0aRNWrFiBx48fQ19fv0jPyfcsEX0MuVyOVatWYfny5bhz5w5cXFykDomK0apVqzBx4kR8/fXX2LNnj9ThqPUcpvZLt1xdXdGqVSts2bIFb9++/ah9FWXIMhEVjytXriAjIwORkZFFTuCJiCh/tWrVwuLFixEUFITz58/D1dUV48aNQ7ly5Qq1n4/Jo9LT03H37l2lEdk6Ojpo27Ytrl+/nmufw4cPw9PTE6NGjYK9vT1q166NxYsXi0u8ExEVN11dXUyePBnBwcFKRR5fX18oFAoJI6PiYGtri6ZNm+a6CIG2U3uh586dO2jcuDHmz58PBwcHdOvWDfv370daWpq6QyEiFRozZgzOnDmD3377DTKZDMC7b3d5/TMRUfExNTWFsbExDAwMxIUu1OHNmzeQy+VKq9sA71a8CQ8Pz7VPUFAQ9u/fD7lcjuPHj2PWrFlYuXIlFi5c+MHnSUtLQ3x8vNKNiOhjmZmZifd9fHzwySefoHPnzkhISJAwKlK1gQMH4urVq7mOMtV2ai/0eHh4YMWKFXjx4gVOnDiBsmXL4rvvvoO9vT2GDh2q7nCISIXatGkDa2tr8fHMmTMxcOBAnjSJiFQoODgYixYtQq1atdCwYUP4+Phg3rx5HyywlBQKhQJ2dnb49ddf0aBBA/Tq1QszZszIc/XGJUuWwMLCQryp6vJ/IqIsz549E7+kLMzlr0QlmWSTMWd37949fPPNN7h//75GDd/ldeJEH/bixQtUqVIFGRkZOHToELp06SJ1SET0Hp7HNM8nn3yC27dvw93dHf369UOfPn3g6Oio9jjS09NhYmKC/fv3o1u3bmL7oEGDEBsbi0OHDuXo06pVK+jr6+Ps2bNi24kTJ9ChQwekpaXlOrdbWlqa0qjv+Ph4ODk58T1LRCp1//59uLq68u8KFSutnqMny6tXr7B8+XLUq1cPjRs3hpmZGdavX1+ofaSmphZTdET0sZydnXHu3DnMnj2bRR4iIhXJWirYx8cHkyZN+qgiz8fkUQYGBmjQoAG8vb3FNoVCAW9vb3h6eubap1mzZnj27JnSPBhPnjyBg4PDByfwNzQ0hLm5udKNiEjV3N3dlf6+LFu2DGvWrOEUBBosLi4OFSpUQLNmzZCeni51OGqn9kLP5s2b0apVK7i6umLXrl3o1asXAgMDcfnyZQwfPjzf/gqFAgsWLICjoyPMzMwQFBQEAJg1axZ+++234g6fiAqhefPmSstWJiQkoHPnzrh586aEURERaa5FixZ91BKxqsyjJkyYgC1btmDnzp149OgRRowYgaSkJAwZMgTAu7kRpk+fLm4/YsQIxMTEYOzYsXjy5AmOHTuGxYsXY9SoUUU+HiIiVbt79y6mTZuGcePG4dKlS1KHQ0UUEhKC0NBQBAQElMrVgPXU/YQLFy5Enz598Msvv6Bu3bpF6r9z504sX74cw4YNE9tr166N1atX45tvvinU/kJDQzF16lScOHECycnJqFKlCrZv346GDRsWOjYiytuSJUtw9OhRPH78GI8fP4aurq7UIRERaZxXr17h8OHDePHiRY5vKVetWpVnX1XmUb169UJUVBRmz56N8PBw1KtXDydPnhQnaH7x4gV0dP73naKTkxNOnTqF8ePHw93dHY6Ojhg7diymTp1a4OckIipu9evXx8qVK/HixQu0atVK6nCoiKpWrYpbt24hNjZW6lAkofY5egRBECe7KooqVapg8+bNaNOmDcqUKQM/Pz9UqlQJjx8/hqenZ6GWbH/79i08PDzw6aefYsSIEShbtiyePn2KypUro3Llyvn259wGRIUTGRmJyZMno3fv3mjfvr3Y/rF/F4ioaHge0zze3t7o0qWLmPvUrl0bz58/hyAIqF+/Ps6dO5dnf1XmUVLge5aIpJCeno6IiAhOCE8fRZ3nMLWP6Mn6MJecnJzrN1Hu7u559g8NDUWVKlVytCsUikIvK7ps2TI4OTlh+/btYlvFihULtQ8iKjg7Ozvs3LlTqe3IkSP4+eefsWHDBlSvXl2iyIiINMP06dMxadIkzJs3D2XKlME///wDOzs79OvXD1988UW+/VWZRxERlQaCIGDYsGE4efIkjh07xis/SCOofY6eqKgodOzYEWXKlEGtWrXg4eGhdMtPzZo1cfny5Rzt+/fvL1D/7A4fPoyGDRuiZ8+esLOzg4eHB7Zs2fLB7dPS0hAfH690I6KiEwQBU6dOxfnz57Fjxw6pwyEiKvEePXqEgQMHAgD09PSQkpICMzMzzJ8/H8uWLcu3vyrzKCKi0iAuLg5+fn6Ijo5GVFSU1OFQAR0+fBj79+9HeHi41KFIQu0jesaNG4e4uDjcvHkTrVu3xsGDBxEREYGFCxdi5cqV+fafPXs2Bg0ahNDQUCgUChw4cAABAQHYtWsXjh49WqhYgoKCsHHjRkyYMAE//vgjbt++jTFjxsDAwACDBg3Ksf2SJUuUJpYloo8jk8lw7NgxLFy4ELNmzRLb09PTS+WkaURE+TE1NRVHQzs4OCAwMBC1atUCALx58ybf/qrMo4iISgNLS0tcunQJly9fVpp6gEq2BQsW4M6dOzh06FCpXAFY7XP0ODg44NChQ2jcuDHMzc1x584dVKtWDYcPH8by5ctx5cqVfPdx+fJlzJ8/H35+fkhMTET9+vUxe/ZstGvXrlCxGBgYoGHDhrh27ZrYNmbMGNy+fRvXr1/PsX1aWhrS0tLEx/Hx8XBycuJ14kQq9vXXX0OhUGDNmjUftXQwEeWN851onm7duqFjx44YNmwYJk2ahEOHDmHw4ME4cOAArKyscPbs2Xz3oao8Sgp8zxJRSRAdHY1t27Zh4sSJSpPOU8kxcuRI+Pr6YuvWrR+1WqUqafUcPUlJSbCzswMAWFlZISoqCtWqVUOdOnVw7969Au2jRYsWOHPmzEfH4uDgkOM/vUaNGvjnn39y3d7Q0BCGhoYf/bxE9GFPnz7FgQMHIAgCZs6cyUIPEVE2q1atQmJiIgBg3rx5SExMxJ49e1C1atV8V9zKoqo8ioioNFIoFOjevTsuX76M0NBQrF69WuqQKBcbNmyQOgRJqb386ObmhoCAAABA3bp1sXnzZoSGhmLTpk1wcHDIt3+lSpUQHR2doz02NhaVKlUqVCzNmjUTY8ny5MkTuLi4FGo/RKQ6VatWxb1797B27VrUq1dPbI+JiZEuKCKiEqJSpUriwhWmpqbYtGkT7t+/j3/++adA+Ysq8ygiotJIR0cH3333Hezs7DBs2DCpwyHKldoLPWPHjkVYWBgAYM6cOThx4gScnZ3xyy+/YPHixfn2f/78OeRyeY72tLQ0hIaGFiqW8ePH48aNG1i8eDGePXuGv/76C7/++itGjRpVqP0QkWq5u7tj5MiR4uPw8HBUqVIF33//PZKSkiSMjIio5Bg5cmSB5uXJTpV5FBFRadW/f3+lOdKAdyN9iEoKtV+61b9/f/F+gwYNEBISgsePH8PZ2Rm2trYf7Hf48GHx/qlTp2BhYSE+lsvl8Pb2hqura6FiadSoEQ4ePIjp06dj/vz5qFixIlavXo1+/foVaj9EVLyOHDmCt2/f4u7duzAyMpI6HCKiEuGPP/7ApEmT8syfshRHHkVEVJqZmZmJ9x8/foyePXti165dXMGwBNi8eTMWLFiAAQMGYMmSJVKHIwm1F3qyu3r1Kho2bIj69evnu223bt0AvFul5/0VsfT19eHq6lqgVbve16lTJ3Tq1KnQ/YhIfYYNGwY3NzeYm5tDV1cXwLtvTYKCglClShWJoyMikkZh1tMorjyKiIiAKVOm4MGDB5gyZQrnQCsBnj17htDQUKSkpEgdimQknSK8ffv2BR4mrFAooFAo4OzsjMjISPGxQqFAWloaAgICWLAh0mItW7ZUmrNn+/btqFmzJhYtWiRdUEREGoJ5FBFR8fn9998xdOhQ/PXXX1KHQgB+/PFH3Lx5U2kqiNJG0hE9RVnZPTg4uBgiISJNc+XKFWRkZMDY2FjqUIiIJJGQkFDoPsyjiIhUz8LCAr/99ptSm5+fH9zd3SGTySSKqvSysrJC48aNpQ5DUpIWeopi/vz5ef589uzZaoqEiKS0fft29OnTB5999pnYFhQUBH19fTg5OUkYGRFR8QoMDMT27dsRFBSE1atXw87OTlzcIvvEoLlhHkVEVPzOnDmDDh06YNCgQdi0aRP09DTuYzdpOEnfcZs3b4a9vX2h+hw8eFDpcUZGBoKDg6Gnp4fKlSszQSEqRdq1ayfeFwQBQ4cOxZ07d/Dnn3+ia9euEkZGRFQ8Ll68iPbt26NZs2a4dOkSFi5cCDs7O/j5+eG3337D/v378+zPPIqIqPiFhIRAoVAgOTkZOjqSzpZS6iQkJGDz5s2oVKkSunfvXmpHVElW6Hn27BlsbGzEN74gCAX6T/Dx8cnRFh8fj8GDB6N79+4qj5OINENsbCzkcjkEQVCay6egf1uIiDTBtGnTsHDhQkyYMAFlypQR2z/77DOsW7cu3/7Mo4iIit+3336LqlWr4pNPPmGhR82ePn2KyZMnw87ODj169JA6HMmo/V0XHR2Ntm3bolq1aujQoQPCwsIAAN988w0mTpxYpH2am5tj3rx5mDVrlipDJSINYmVlhYsXL+LGjRtwcXER20ePHo0hQ4bg2bNnEkZHRKQa/v7+uRZk7Ozs8ObNmyLtk3kUEZHqtWrVCoaGhuLjRYsWwdfXV7qASglDQ0P06dMHnTt3ljoUSam90DN+/Hjo6enhxYsXMDExEdt79eqFkydPFnm/cXFxiIuLU0WIRKShdHR0UKdOHfFxdHQ0tm7dih07diA8PFzCyIiIVMPS0lL8kiw7Hx8fODo6Fnm/zKOIiIrP7t27MXPmTDRv3pw5aTGrVasW/vrrL2zdulXqUCSl9ku3Tp8+jVOnTqFChQpK7VWrVkVISEi+/X/55Relx4IgICwsDL///jvat2+v0liJSLPZ2Njg/PnzOHz4MJo1aya279mzB4Ig4KuvvuLkeESkUXr37o2pU6di3759kMlkUCgUuHr1KiZNmoSBAwfm2595FBGR+nl5ecHLywuenp4oV66c1OFQKaD2TzhJSUlKI3myxMTEKA1t+5Cff/5Z6bGOjg7Kli2LQYMGYfr06SqLk4i0g6enJzw9PcXHGRkZmDx5Ml6+fImMjAwMGDBAwuiIiApn8eLFGDVqFJycnCCXy1GzZk3I5XL07dsXM2fOzLc/8ygiIvWzsrLCsWPHlOaNjI+Ph0KhgKWlpXSBaaGMjAzo6+tLHYbkZIIgCOp8wg4dOqBBgwZYsGABypQpg/v378PFxQW9e/eGQqHId7WIkiQ+Ph4WFhaIi4uDubm51OEQUQEkJyfjp59+wv79+3Hr1i0YGRkBAB49egRLS0s4ODhIHCGR+vA8prlevnwJf39/JCYmwsPDA1WrVpU6JLXge5aItIFCoUDXrl3x5MkT/Pvvv6hRo4bUIWmNypUrIyMjA8eOHYODgwOOHz+OM2fOIDMzE/r6+qhatSpatWqF5s2bq32ibHWew9Q+omf58uVo06YN7ty5g/T0dEyZMgUPHz5ETEwMrl69qu5wiKiUMTExwezZszFr1iylb1VGjhyJa9eu4ffff8fXX38tYYRERPlzcnKCk5OT1GEQEZUq6enpSE5ORlJSEpKSksT7GRkZAN5dDpslr/uRkZG4efMmYmNjceHCBbx69arAffO7DwC6uroFuunp6eVo09fXh6GhIQwNDWFgYPDBaQ4yMzOho6MDHR0dyOVyxMfH4+3bt3j79q3YPzU1FWlpaUhLSxPvZ/83IyMDMpkMOjo6Of7Nui+Xy6GjowOFQoHU1FQYGxsjISEBmZmZMDExEdtTU1MRHx+PoKAgAEDr1q0RExPzwf/LChUqYNSoUfjuu+9gbW39we00ldpH9ADvJvxbt24d/Pz8kJiYiPr162PUqFEf/Ca9MMuiHThwQFVh5ovfKhFph8TERHh5eeHWrVsICgoSPzylpqbC0NCQy7OT1uJ5TPN8+eWXaNy4MaZOnarUvnz5cty+fRv79u3L0aek5lFFoc73bEZGBqKiohAVFYXk5GSkpqYiJSVF/DcjIwMKhQKCIEChUCjdz/5vVqqddS6RyWR53n//sUKhyPVDUtZ9uVwOAOJzZX/OgrTltW122c+F2WMsSNuH9lPQbQrTpkqq/piUkZGB5ORkpVtKSor4b/YPuLq6utDR0YG+vj6MjIwKdDMzM4ONjQ2sra1hY2OjdN/S0hK6urofPM6kpCREREQgLCwMr1+/Fv998+YNMjIyIJfLIZfLxfdlVpzZb++3Z72Xsv8u5Pb7kdfvTl5tgiBAR0dHqVhRkPsymQzp6elIT08Xf5dyu5+SkqJUzElKSkJmZqZK3xOaIOt9CCj/TqSnp8PAwAD29vaIiIhAenq6VCHmqUaNGvjqq69gaWmJtLQ0+Pv74/jx4+ICBCYmJhgyZAjGjh1b7KNjtXpEDwBYWFhgxowZhdqeiKi4mJmZ4erVqwgMDFT6hnz8+PG4e/cufvrpJ7Rs2VLCCImI3rl06RLmzp2bo719+/ZYuXJlrn2YR+VOEAQcO3YM1atXh62tLQ4dOoTLly/j/v37CAwMzPObYCJNZG5uDisrK/EDpkKhEAs8KSkpEkenWXR1dWFqagoTExOYmprCwMBA/NmHip35FUMzMjLw8uVLODk5iXPXFqZ/1n1BEMTCXH63zMzMHI+zRidlySo25yY9PR0vX74UH5uYmMDKygpJSUlQKBQwNDSEkZGROMIn637Wv1kFpA8V+xQKBXR1daFQKAAAxsbGSE1NhZGREYyNjZGcnAxdXV2x2GloaAg7OzvUqFED1atXh5ubG0xNTXPEnZqaij179mDVqlW4f/8+1q9fjw0bNqBLly4YP348WrRoofbLulRNkhE9qampuH//PiIjI8X/tCxdunRRdzhFxm9CibRXSkoKHB0d8fbtW1y4cAGtWrWSOiQileN5TPMYGxvD19cXbm5uSu2PHz+Gh4eH1n9YU+V7dvr06Vi6dCmAd69rbq+djo4ObG1tYWZmJn6wyPpXX18/19EMuf37/qiZD93P7WcymSzPD0pZozSyjwj60EihwrRl/wCZ2yUiBW3L7v22gmxT2LaSPApXT09PLA68f8uaM1ChUEAul4ujxLJGl2RdmpI1qiy3W3x8PGJiYhAdHS3+Gx0djYSEhALFZ2JiAgcHBzg4OKB8+fJwcHCAnZ0dDAwMxNEwWTFmH8mW/ZbVnjX6J+t3ILffi7x+ZwrSlv31yl6wyO9+VgHCwMBA6RKl9+8bGxuLhZysf7PuGxgYqPy91qFDB5w4cQLt2rXDqVOnVLrvwhAEARkZGeJ7L2v00/t/LwwMDJCSkoLQ0FDY2trCxcWlQIsrlSSCIODcuXNYtWoVjh8/Lrbb29ujXr16qFu3LmrVqoUaNWqgUaNGH/186sy71F7oOXnyJAYOHIg3b97kDOb/r8EriKioKAQEBAAA3NzcULZsWZXGWRBMkIm0W1RUFPbv34/hw4eLJ7bffvsNQUFBGD16NJfHJI3H85jmady4MTp16oTZs2crtc+dOxdHjhzB3bt3C7SfkpBHFYUq37P+/v5wd3cXH9esWRNdu3ZF/fr14ebmhnLlysHGxkbjv9UlysjIQGxsrDh/Snx8vFigNDIygr29Pezs7GBmZiZ1qKXay5cvMXz4cGzYsAEuLi5Sh6ORdu3ahaioKHTu3BnVqlUrVN9Hjx5hzZo1+OOPP5CUlKT0sz59+uCvv/766Pi0utBTtWpVtGvXDrNnz4a9vX2h+yclJWH06NHYtWuXOBpIV1cXAwcOxNq1a3Ndur24MEEmKl0yMzNRpUoVhISEYNOmTfj++++lDonoo/A8pnmOHDmCHj16oG/fvvjss88AAN7e3vj777+xb98+dOvWLc/+JSmPKgpVv2cnTpyI9evXY/ny5Rg9enSJHg1CRKXPoUOH4OHhAWdnZ6lD0QjNmzfH1atXsXv3bvTq1atI+0hJScHevXsRHR2NR48eISQkBJ9//jkmT5780fFpdaHH3NwcPj4+qFy5cpH6f//99zh79izWrVuHZs2aAQCuXLmCMWPG4PPPP8fGjRtVGW6emCATlS4KhQKHDh3Ctm3bsHfvXhgbGwMA7t+/Dx0dHdSuXVviCIkKh+cxzXTs2DEsXrwYvr6+MDY2hru7O+bMmVOgS0xLUh5VFMXxnk1JSRH/nhMRlRR+fn5o0qQJTExMcOvWLVSpUkXqkEq8n376Cbdv38bcuXNL5JL1Wl3oGTp0KJo1a4ZvvvmmSP1tbW2xf/9+tG7dWqn9/Pnz+PrrrxEVFaWCKAuGCTIRAUCbNm1w7tw5bN68Gd99953U4RAVGM9jpU9JyqOKgu9ZIiotgoOD0atXL9jZ2eHw4cO8jFQLaPWqW+vWrUPPnj1x+fJl1KlTR5xpO8uYMWPy7J+cnJzrJV92dnZITk5WaaxERPlJTU2FtbU1DAwM8MUXX4jtGRkZOf6+ERFJjXkUEZFmqFixIq5cuYKUlBSxyCOXyxEeHg5HR0eJo6OSTu1lwb///hunT5/GP//8g7Vr1+Lnn38Wb6tXr863v6enJ+bMmYPU1FSxLSUlBfPmzYOnp2cxRk5ElJORkRH27duHFy9eKF0/PXHiRLRu3Rq3bt2SMDoi0jZyuRw//fQTGjdujHLlysHa2lrplh9V51Hr16+Hq6srjIyM0KRJkwL/zdu9ezdkMlm+cwoREZVmBgYGsLCwEB8vWLAAtWvXxpEjRySMqmSKiYlROreVdmov9MyYMQPz5s1DXFwcnj9/juDgYPEWFBSUb/81a9bg6tWrqFChAtq0aYM2bdrAyckJ165dw5o1a9RwBEREOWX/hjwpKQm7du3CxYsXkZiYKGFURKRt5s2bh1WrVqFXr16Ii4vDhAkT0KNHD+jo6GDu3Ln59ldlHrVnzx5MmDABc+bMwb1791C3bl14eXkhMjIyz37Pnz/HpEmT0KJFi0I9HxFRaZaZmYnTp08jNjYWcXFxUodT4syZMwempqZYtmyZ1KGUCGqfo8fa2hq3b98u8mTMwLthx3/++SceP34MAKhRowb69eun9on0eJ04EX3Iy5cvsWfPHkycOFFcxeXAgQPIzMzEl19+CV1dXYkjJOJ5TBNVrlwZv/zyCzp27IgyZcrA19dXbLtx40aBln9VVR7VpEkTNGrUCOvWrQPwbsJ6JycnjB49GtOmTcu1j1wuR8uWLTF06FBcvnwZsbGx+Pfffwv8nHzPElFplp6ejoMHDyqtKCUIAlcMBNC5c2ccPXoUO3fuxMCBA6UOJ1daPRnz+PHjUbZsWfz444/qfNpiwWSDiAoqIyMDVatWRUhICH777TcMHTpU6pCIeB7TQKampnj06BGcnZ3h4OCAY8eOoX79+ggKCoKHh4favuVNT0+HiYkJ9u/fr3T51aBBgxAbG4tDhw7l2m/OnDm4f/8+Dh48iMGDB+db6ElLS0NaWpr4OD4+Hk5OTnzPEhHh3aW37du3x9ixY9G9e3epw5GUIAgIDw+HqalpiT0/qDPvUvulW3K5HMuXL0erVq0wevRoTJgwQemWn507d+LYsWPi4ylTpsDS0hJNmzZFSEhIcYZORFRkGRkZGDJkCKpXr44+ffqI7aGhoUofYoiI8lKhQgWEhYUBeDe65/Tp0wCA27dvw9DQMN/+qsqj3rx5A7lcnmNiZ3t7e4SHh+fa58qVK/jtt9+wZcuWAj/PkiVLYGFhId6cnJwK3JeISNutXbsWFy9exPDhw5GQkCB1OJKSyWRwcHAosUUedVN7ocff3x8eHh7Q0dHBgwcP4OPjI958fX3z7b948WJxaPH169exbt06LF++HLa2thg/fnwxR09EVDQmJiaYM2cOHj58qHR5xJAhQ1C5cmWcO3dOwuiISFN0794d3t7eAIDRo0dj1qxZqFq1KgYOHFigkYJS5VEJCQkYMGAAtmzZAltb2wL3mz59OuLi4sTby5cviy1GIiJNM378eEyaNAl//PEHypQpI3U4VIKofXn18+fPf1T/ly9fokqVKgCAf//9F1999RW+++47NGvWDK1bt1ZBhERExSdreUwAiI6Oxn///Yfw8HBUqlRJwqiISFMsXbpUvN+rVy84Ozvj+vXrqFq1Kjp37pxvf1XlUba2ttDV1UVERIRSe0REBMqVK5dj+8DAQDx//lwpRoVCAQDQ09NDQEBArvM3GhoaFmikEhFRaaSvr48VK1Yotd24cQOvXr3CV199JVFU6rd//354e3ujS5cuaN++vdThlAhqH9HzsczMzBAdHQ0AOH36ND7//HMA75Y4TklJkTI0IqJCsbGxQWBgIM6ePQtXV1exfe7cuZgzZw5iYmKkC46INIKnpycmTJhQoCIPoLo8ysDAAA0aNBBHFwHvCjfe3t65LtNevXp1+Pv7w9fXV7x16dIFn376KXx9fXlJFhGRCrx9+xa9evVCz549sXPnTqnDUZvDhw9j06ZNuH37ttShlBhqGdHTo0cP7NixA+bm5ujRo0ee2x44cCDPn3/++ef49ttv4eHhgSdPnqBDhw4AgIcPHyp9UCIi0gSGhoZK36JHRUVh+fLlSElJQbNmzdCuXTvpgiOiEikgIABr167Fo0ePALxbNWv06NFwc3PLt68q86gJEyZg0KBBaNiwIRo3bozVq1cjKSkJQ4YMAQAMHDgQjo6OWLJkCYyMjFC7dm2l/paWlgCQo52IiIrGzMwMvXv3xsGDB0vV5Mz9+/eHg4MD8+Zs1DKix8LCQlzyLfuEernd8rN+/Xp4enoiKioK//zzD2xsbAAAd+/eVZrglIhIE1lbW2PXrl0YPHiw+E07AFy6dAlBQUESRkZEJcE///yD2rVr4+7du6hbty7q1q2Le/fuoXbt2vjnn3/y7a/KPKpXr1746aefMHv2bNSrVw++vr44efKkOEHzixcvxImjiYio+Onr62PZsmXw8fFRmpT4/v37EkZV/Nq1a4dly5bhk08+kTqUEkNty6vPnz8fkyZNgomJiTqeTi24LC0RqUNmZiaqVauGkJAQHDt2DF988YXUIZGW4HlM81SuXBn9+vXD/PnzldrnzJmDP/74A4GBgRJFph58zxIRFc7hw4fRtWtXTJw4EcuXL1eaL5LUS53nMLVNxjxv3jwMHz5cJYWet2/f4rffflMasjx06FBYW1t/9L6JiEqamJgYVKtWDQkJCWjZsqXYnnWSyBoxSUTaLywsDAMHDszR3r9//xwTcn4I8ygiotLj4cOHAICUlBStzBmzzmXVqlWDrq6uxNGUHGor56lq4NClS5fg6uqKX375BW/fvsXbt2+xdu1aVKxYEZcuXVLJcxARlSR2dnY4efIkHj16pFQs79WrFxo1aoQ7d+5IGB0RqVPr1q1x+fLlHO1XrlxBixYt8u3PPIqIqHSZPn06zp49izVr1mhloWfBggWoWbMmli9fLnUoJYpal1dXxRtr1KhR6NWrFzZu3ChW7ORyOUaOHIlRo0bB39//o5+DiKgksrW1Fe+Hh4fj8uXLSE1N5bfwRKVIly5dMHXqVNy9e1eci+DGjRvYt28f5s2bh8OHDytt+z7mUUREpU+bNm3E+4IgYObMmejduzfq1KkjYVSqYWRkBGNjYzRo0EDqUEoUtc3Ro6OjozQp84fkt5ywsbExfH19c6wsERAQgHr16ql1iXVeJ05EUnrz5g3Onz+Pnj17im0LFiyAjo4ORo4cCSsrKwmjI03A85jmKejcCjKZDHK5PEd7ScqjioLvWSKij7N582YMHz4c1tbWePbsmVbki2lpadDR0YG+vr7UoeRJK+foAd7N01OQlbXyUr9+fTx69ChHgvLo0SPUrVu3UPuaO3cu5s2bp9Tm5uaGx48ff1SMRETqYGtrq1TkiY6OxtKlS5GcnIyGDRvCy8tLwuiIqDgoFIqP6q/KPIqIiDTP119/jV27dqFfv35aUeQBAENDQ6lDKHHUWujp3bs37OzsCt0v+3JwY8aMwdixY/Hs2TOlIcvr16/H0qVLC73vWrVq4ezZs+JjPT21viRERCpjbm6OX3/9FcePH0e7du3E9vPnz8POzg61atWSMDoi+hjXr19HdHQ0OnXqJLbt2rULc+bMQVJSErp164a1a9fmmuwWZx5FRESaxcrKChcvXtSKz71yuZwTMH+A2i7d0tXVRVhYWJEKPTo6OpDJZPlO6PyhYcofMnfuXPz777/w9fUtdEwAhw8TUcknl8tRvXp1PHv2DAcPHkS3bt2kDolKEJ7HNEf79u3RunVrTJ06FQDg7++P+vXrY/DgwahRowZWrFiB77//HnPnzs3Rt7jyKCnwPUtEpFqpqakYNGgQRo4ciVatWkkdToHJ5XI4OzvD3d0dO3bsgL29vdQh5UsrL936mHpScHCwCiNR9vTpU5QvXx5GRkbw9PTEkiVL4OzsnOu2aWlpSEtLEx/Hx8cXW1xERKoQGxuLunXrIjY2Fp9//rnYHh0dDSsrqwLP90FE0vL19cWCBQvEx7t370aTJk2wZcsWAICTkxPmzJmTa6GnOPMoIiLSbEuXLsXevXtx4cIFBAUFwdTUVOqQCuTOnTt4/fo1UlJSYGNjI3U4JY7aCj0fc025i4uLCiP5nyZNmmDHjh1wc3NDWFgY5s2bhxYtWuDBgwcoU6ZMju2XLFmSY04fIqKSzMbGBvv370d8fLzSiXvAgAF4/vw5tm7diqZNm0oYIREVxNu3b5W+rbx48SLat28vPm7UqBFevnyZa9/iyqOIiEjzTZs2DX5+fhg3bpzGFHkAoHHjxnj48CECAwO14jI0VVPbpVuq9t9//+HFixdIT09Xas9tKdGCio2NhYuLC1atWoVvvvkmx89zG9Hj5OTE4cNEpFHevHmDKlWqICEhAQEBAahSpYrUIZFEeBmM5nBxccHvv/+Oli1bIj09HZaWljhy5Ii4ZK6/vz9atWqV7+qlWYojj1IHvmeJiEhTaeWlW6oSFBSE7t27w9/fX+l686xl2z/m2nJLS0tUq1YNz549y/XnhoaGnNGbiDSera0tXrx4gfPnzysVeebPn4/o6GiMGjUK1apVkzBCInpfhw4dMG3aNCxbtgz//vsvTExM0KJFC/Hn9+/fR+XKlfPdT3HmUUREpPnCwsKwaNEirFy5kp99NZjGTc4wduxYVKxYEZGRkTAxMcHDhw9x6dIlNGzYEBcuXPiofScmJiIwMBAODg6qCZaIqIQyNzdH165dxcdv377FihUr8MsvvyAiIkJsj4+PR2pqqhQhElE2CxYsgJ6eHlq1aoUtW7Zgy5YtMDAwEH++bds2pdX2PqQ48ygiItJsCoUCXl5eWL9+PcaNGyd1OB+0adMmTJgwAQ8fPpQ6lBJL4wo9169fx/z582FrawsdHR3o6OigefPmWLJkCcaMGVOofU2aNAkXL17E8+fPce3aNXTv3h26urro06dPMUVPRFQyWVhYYPfu3RgxYoTSnD3r1q1D2bJlsWzZMgmjIyJbW1tcunQJb9++xdu3b9G9e3eln+/btw9z5szJdz+qzKOIiEi76OjoYOXKlfDw8CjRhZ4NGzbg559/xs2bN6UOpcTSuEKPXC4XJ0q2tbXF69evAby7dj0gIKBQ+3r16hX69OkDNzc3fP3117CxscGNGzdQtmxZlcdNRFSS6ejooGPHjtiwYQN0dXXF9mvXriExMRHW1tZiW1JSEn799VelkT9EpB4WFhZKv6NZrK2tlUb4fIgq8ygiItI+n3/+Oe7cuQM3NzepQ8mVIAhYuHAh+vbtm+NLD/ofjZujp3bt2vDz80PFihXRpEkTLF++HAYGBvj1119RqVKlQu1r9+7dxRQlEZF2OHLkCO7cuaM098epU6fw/fffY/ny5Xj69Kk4twcRlXyqzKOIiEg76ej8bzzIkydPUL58eZiZmUkY0f/IZDJ06dKlxC8eIDWNG9Ezc+ZMcan2+fPnIzg4GC1atMDx48fxyy+/SBwdEZF2kclkaNSokdKIHn19fTRq1Ahdu3ZVKvJ8+eWXmDdvXoFX/SEi9WMeRUREBXXgwAHUr18fo0ePljoUKiSNXV49u5iYGFhZWan9W2Uu8UlEpZlCoRC/8Xn06BFq1qwJAwMDREVFiX8To6OjYWVlpfTNEJUcPI8RIF0eVRR8zxIRqc/ly5fRqlUrNGvWDKdPn4axsbGk8Zw4cQKBgYHo378/LC0tJY2lKLi8eiFl/6aZiIjUI3vxxtHREdu3b8fLly+VTlzffPMNbty4gS1btqBz585ShElE+WAeRUREuWnRogUuXLiAZs2a5To/nDplZmZi0qRJ+O+//xAbG4uZM2dKGk9JpxWFHiIikpa5uTkGDx6s1JaZmYlbt24hIiICzs7OYntAQAD8/Pzg5eUFCwsLNUdKRERERAXVsmVLqUMA8O4LxilTpuDnn3/GDz/8IHU4JR7H0hMRUbHQ09PD8+fP4e3tDXd3d7F9+/bt6NWrF0aOHClhdERERERUUAqFAmvWrMGRI0ckeX4dHR0MGjQIPj4+GnnZlrqx0ENERMXGwMAAn332mdLcH/b29nBzc0PHjh3Ftjdv3qBatWoYO3Ys5HK5FKESERER0Qds3rwZ48aNw8iRI5GQkKDW587MzBTva8J8ciUBCz1ERKRW48ePx+PHj9G7d2+x7eTJk3j69CkuXryodA34pUuXEBoaKkWYRERERPT/Bg0ahPr162PGjBkwNTVV2/MePXoUdevWxY0bN9T2nNqAc/QQEZEksk/m3K1bNxw8eFDp54IgoHfv3ggLC8Ply5fRvHlzdYdIRERERABMTExw+/Ztta6kqlAoMHv2bPz333/Yu3cvPvnkE7U9t6ZjoaeEkMvlqFq1Kuzs7GBvb49y5cqhXLly4v3s/5qZmUkdbqmUmpqKFy9eICYmBqmpqUhLS8vxb25tRf2ZQqGAhYUFLC0tlW5WVlY52t7/mYWFBfT0+OtdkqWlpSEuLq5At4SEBOjr68PIyAjGxsY5/s2tLb+fSb1ywvvMzMzQrVs3pbY3b97AxcUFiYmJaNSokdi+bds2XLhwAUOHDkXr1q3VG6jEBEFAQkICl5UmIiIitcte5FEoFBAEoVhzSh0dHZw9exbLli3DggULiu15tBE/CZYQUVFRCA4ORnBwcL7bmpqa5loEer/N3t4exsbGaoheO8TFxSEkJOSDt4iICLXHlJSUhNevXxepr5mZWYEKRNbW1qhcuTKqVq0KIyMjFR+B9kpNTcWrV68QGxuba3HmQ+1Zt7S0NEnjz144yl4EsrOzg6ura46bvb29Wr/BAYCyZcvi+vXrSExMhKGhodj+119/wdvbGx4eHmKhJy0tDYGBgahRo0axX7utUCgQHh6O2NhYpee7c+cObt26hVq1aqFVq1bi9tOmTUNaWhrmz5+PMmXKAAAOHjyIXbt24dNPP8WoUaPw5s0bREREoG/fvoiNjcWQIUOQnp6OiIgI3L59G48fP4ahoSEyMzNhYWGBqKioYj1GIiIiog/x8/PD999/j379+mH06NHF+lzW1tZYtmxZsT6HNpIJgiBIHYSmio+Ph4WFBeLi4j7629W0tDT4+PggPDwcERERCA8PV7qf9W9ycnKh9mthYZHrqCAnJye4u7ujRo0aSh+gtJUgCIiMjMyzkBMXF5fvfkxNTVG2bFkYGxvD0NAQhoaGMDIyKtC/hd1WJpMhPj4eb9++RWxsbI7bh9oTExOL9BrJZDK4urqievXqcHNzQ/Xq1cWbnZ1dqZz4TKFQ4NWrV3jy5AkCAgIQEBAg3g8JCYEq/nyamZnBwsIiz1uZMmUgl8uRkpKC1NRUpKSkKN3/0L/vt2VkZBQ5TkNDQ7i4uKBixYofLASp6z1y+fJlHD58GMOHD0flypUBAKdPn4aXlxeaNm2Kq1ev5tk/MzMTsbGx0NPTE1dtSE1Nxe+//47Y2FhMmjRJPJaNGzdi586d6NOnD8aOHQsASElJgYmJCYB354Gs4s3cuXMxb948jBw5EqtWrUJkZCQiIiLg6emJzMxMTJs2DampqWLx5tmzZzA0NER6enqh3ksymQzp6ekqGbWnyvMYkTrwPUtEJL2NGzdi5MiRcHR0RFBQEAwMDFS278TERIwcORI9e/ZE586dVbbfkkCd5zAWej6CupMNQRCQmJiYo/jzocJQQUYM6OnpoUaNGqhbt67Szc7OrtiPR5UyMzMRGhr6wSLOixcvkJqamu9+bGxs4OLi8sGbtbV1iS94ZGRkiCNKClIcioqKwtOnTxEbG/vBfVpYWIhFn+xFoMqVK6v0D7tUYmNjcy3mPH36FCkpKR/sZ2pqCmtr63wLNVk3c3PzHI/VeQlVZmYm0tLSPlgoSklJQXh4OIKDg/H8+XPx9urVKygUijz3bWRklGsBKOtW3MXCjRs3Yvz48ejfvz+2bt0qtg8dOhTBwcE4d+6c+PxjxozB2rVr8eOPP2LRokUAgOTkZHFiwezFm9mzZ2PBggUYOXIk1q9fD+Dd32IzMzMYGhpi5cqVePXqFR4+fAg/Pz+8evVKLMoVhkwmg62tLUxMTGBlZYWqVauifPnysLe3R5kyZWBsbAxnZ2e4ubnBzs5OZaPv+KGZNA3fs0RE0lMoFJg1a5ZY7FGl9evX44cffoCxsTECAwPh4OCg0v1LiYUeDVGSkw1BEBAXF/fBglBwcDD8/Pzw9u3bXPs7ODjkKP5Uq1ZNsnlfBEFATEwMgoKCEBQUhODgYKX7ISEh+S7JLJPJUL58+Q8WcZydnUvt/EeCICAqKgqPHz/G48ePERAQIN4PDg7+4GgDXV1dVKpUKdcikI2NjZqPIm/p6ekICgrKUcx58uQJIiMjP9hPT08PlStXhpubG9zc3FCtWjXx39Iy0ikjIwOvXr3C8+fPcxSBsgpB+Z1KjI2NcxR/KleuLN6yCisfIykpCQkJCShXrhwA4OXLl3B2dgaQe/Fm7NixWL16NYB3vwNffvklypQpgzVr1ogjfR4+fAh/f38IgoD4+Hjcv38ffn5+uH//fr5Li+rr68POzk6cey3rlv1x1n1bW1tJ/r6W5PMYUW74niUi0m5yuRwDBgzAiBEj0KJFC6nDUSkWejSEpicbgiDg1atX8PPzg6+vL/z8/ODn54dnz57l+qHNyMgItWvXVir+uLu7ix+IPlZqaipCQkI+WMyJj4/Ps7++vj6cnZ0/WMipUKGCVow+UbfU1FQ8e/Ys1yJQXpeJ2draKhV+3NzcYGVlBQBicST7v7m1FfVn0dHROYo5wcHBeRYDHRwclAo5WfcrVqzIia3zkZ6enmchKDQ0NN9CkK2trVLhp3LlyqhUqRIqV64MBweHIhXUIiMjcfToUZiZmaFLly7iKJjU1FTo6enl+H9VKBQIDg5WKubcv38fgYGBue5fX18fNWvWhLu7O9zd3eHs7KxUwLGysirxhUBNP4+R9NavX48VK1YgPDwcdevWxdq1a9G4ceNct92yZQt27dqFBw8eAAAaNGiAxYsXf3D73PA9S0RU8jx8+BAVK1YUL20vKIVCgUOHDuHvv//G7t271T4fpLqx0KMhtDXZSExMhL+/v1j4yfrAk5SUlOv2Li4uqFevnlIBqGLFijl+UQVBQHh4uFi8eb+Y8/r163w/DJYvXx4VK1ZEpUqVUKlSJfF+xYoVUb58ea3/41CSCIKAsLAwseiTvQj04sULqcPLlampaY5ROVn/qmJECeUuPT0dL1++FAtBWcWgwMBABAYG4s2bN3n2NzY2Fn/n3y8Gubi4FGmesazROdlv/v7+HyxeZo1yzCrquLu7o3r16tDX1y/0c5ck2noeI/XYs2cPBg4ciE2bNqFJkyZYvXo19u3bh4CAgFwvAe/Xrx+aNWuGpk2bwsjICMuWLcPBgwfx8OHDAg/953uWiKhkWblyJaZNm4axY8fip59+KlTfzMxMODo6IjIyEjt37sTAgQOLKcqSgYUeDVGakg2FQoHAwECl4o+vry9evnyZ6/ZmZmZwd3eHm5sbIiMjxaJOfvPkmJqaih/o3i/muLq6chUxDZGUlISnT58qFYGePHmCpKQksZgnCILS/dza8vv5h9rMzMxyvdSqfPnyJX6ERWkUHx+PwMBABAUFicWfrMchISF5zg8kk8ng5OSU60igrEvCAgMDc4zSef78ea77MzQ0RK1atcRiTt26dVGnTh2ULVu2mI5eWqXpPEaq16RJEzRq1Ajr1q0D8C5XcHJywujRozFt2rR8+8vlclhZWWHdunUFTu75niUiKlmOHDmCLl26YOXKlRg/fjxkMhni4uKQmZmpNL9pfHw8VqxYgZCQEOzatUvsP3v2bGRmZuLHH3/U+mk0WOjREEw2gJiYGPEDVNblXw8fPkR6enqu2+vo6MDJySnXQk6lSpVga2vLD+JEJMrIyEBISIhS8Sd7MSi/lQj19PSQmZmZ688qVKigVNBxd3eXdC4yKfA8RkWVnp4OExMT7N+/H926dRPbBw0ahNjYWBw6dCjffSQkJMDOzg779u1Dp06dct0mLS1NaXGJ+Ph4ODk58T1LRFSC/Pbbb+jVq5dYqFmzZg3GjRuHPn364K+//gLwbtVSU1NTCIKAgIAAVKtWTcqQJaHOvKv0ZLNULKytrdG6dWu0bt1abMvIyEBAQIA430+5cuXEQo6zs7PGX+pAROqjr6+PKlWqoEqVKjl+JggCIiMjc4wCyrofERGBzMxMcX6x7AWdOnXqlLgJw4k0yZs3byCXy2Fvb6/Ubm9vj8ePHxdoH1OnTkX58uXRtm3bD26zZMkSzJs376NiJSKi4vXNN98oPY6JiQEApRWzjI2NMXHiRLi5uYkLZ1Dx4Yiej8BvQomISq7ExES8efMGTk5Oal3GXpPwPEZF9fr1azg6OuLatWvw9PQU26dMmYKLFy/i5s2befZfunQpli9fjgsXLsDd3f2D23FEDxGRZkpLS0N6ejrnwcyGI3qIiIg+kpmZmdZf600kFVtbW+jq6iIiIkKpPSIiIt9van/66ScsXboUZ8+ezbPIA7ybN6soE64TEZG0+PdbWlyiiIiIiIgKxcDAAA0aNIC3t7fYplAo4O3trTTC533Lly/HggULcPLkSTRs2FAdoRIREZU6HNFDRERERIU2YcIEDBo0CA0bNkTjxo2xevVqJCUlYciQIQCAgQMHwtHREUuWLAEALFu2DLNnz8Zff/0FV1dXhIeHA+DoOyIiIlVjoYeIiIiICq1Xr16IiorC7NmzER4ejnr16uHkyZPiBM0vXryAjs7/Bo9v3LgR6enp+Oqrr5T2M2fOHMydO1edoRMREWk1Tsb8ETiJJRERaTKex0jT8D1LRESaipMxa4isGll8fLzEkRARERVe1vmL3/mQpmDuRUREmkqdeRcLPR8hISEBAODk5CRxJEREREWXkJAACwsLqcMgyhdzLyIi0nTqyLt46dZHUCgUeP36NcqUKQOZTKb0s/j4eDg5OeHly5elZmhxaTxmoHQeN4+5dBwzUDqPuzQdsyAISEhIQPny5ZXmUiEqqfLKvYqiNP2+FwRfj5z4muTE10QZX4+c+Jooy3o9Xrx4AZlMppa8iyN6PoKOjg4qVKiQ5zbm5ual7s1dGo8ZKJ3HzWMuPUrjcZeWY+ZIHtIkBcm9iqK0/L4XFF+PnPia5MTXRBlfj5z4miizsLBQ2+vBr++IiIiIiIiIiLQECz1ERERERERERFqChZ5iYmhoiDlz5sDQ0FDqUNSmNB4zUDqPm8dcepTG4y6Nx0xUWvH3XRlfj5z4muTE10QZX4+c+Jook+L14GTMRERERERERERagiN6iIiIiIiIiIi0BAs9RERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGWYKGnGKxfvx6urq4wMjJCkyZNcOvWLalDUpklS5agUaNGKFOmDOzs7NCtWzcEBAQobZOamopRo0bBxsYGZmZm+PLLLxERESFRxKq3dOlSyGQyjBs3TmzT1mMODQ1F//79YWNjA2NjY9SpUwd37twRfy4IAmbPng0HBwcYGxujbdu2ePr0qYQRfxy5XI5Zs2ahYsWKMDY2RuXKlbFgwQJkn7NeG4750qVL6Ny5M8qXLw+ZTIZ///1X6ecFOcaYmBj069cP5ubmsLS0xDfffIPExEQ1HkXh5HXMGRkZmDp1KurUqQNTU1OUL18eAwcOxOvXr5X2oWnHTER50+Z8LTtV5W4vXrxAx44dYWJiAjs7O0yePBmZmZnqPJRiUdS8TtteD1XkfNp0nlRVTqjJr4m68sX79++jRYsWMDIygpOTE5YvX17ch1Yk6solVfZ6CKRSu3fvFgwMDIRt27YJDx8+FIYNGyZYWloKERERUoemEl5eXsL27duFBw8eCL6+vkKHDh0EZ2dnITExUdxm+PDhgpOTk+Dt7S3cuXNH+OSTT4SmTZtKGLXq3Lp1S3B1dRXc3d2FsWPHiu3aeMwxMTGCi4uLMHjwYOHmzZtCUFCQcOrUKeHZs2fiNkuXLhUsLCyEf//9V/Dz8xO6dOkiVKxYUUhJSZEw8qJbtGiRYGNjIxw9elQIDg4W9u3bJ5iZmQlr1qwRt9GGYz5+/LgwY8YM4cCBAwIA4eDBg0o/L8gxfvHFF0LdunWFGzduCJcvXxaqVKki9OnTR81HUnB5HXNsbKzQtm1bYc+ePcLjx4+F69evC40bNxYaNGigtA9NO2Yi+jBtz9eyU0XulpmZKdSuXVto27at4OPjIxw/flywtbUVpk+fLsUhqUxR8zptez1UlfNp03lSVTmhJr8m6sgX4+LiBHt7e6Ffv37CgwcPhL///lswNjYWNm/erK7DLDB15JKqfD1Y6FGxxo0bC6NGjRIfy+VyoXz58sKSJUskjKr4REZGCgCEixcvCoLw7k2ur68v7Nu3T9zm0aNHAgDh+vXrUoWpEgkJCULVqlWFM2fOCK1atRITAm095qlTpwrNmzf/4M8VCoVQrlw5YcWKFWJbbGysYGhoKPz999/qCFHlOnbsKAwdOlSprUePHkK/fv0EQdDOY37/RFWQY/zvv/8EAMLt27fFbU6cOCHIZDIhNDRUbbEXVW7Jyvtu3bolABBCQkIEQdD8YyYiZaUtX8uuKLnb8ePHBR0dHSE8PFzcZuPGjYK5ubmQlpam3gNQkY/J67Tt9VBFzqdt50lV5ITa9JoUV764YcMGwcrKSun3ZurUqYKbm1sxH9HHKa5cUpWvBy/dUqH09HTcvXsXbdu2Fdt0dHTQtm1bXL9+XcLIik9cXBwAwNraGgBw9+5dZGRkKL0G1atXh7Ozs8a/BqNGjULHjh2Vjg3Q3mM+fPgwGjZsiJ49e8LOzg4eHh7YsmWL+PPg4GCEh4crHbeFhQWaNGmiscfdtGlTeHt748mTJwAAPz8/XLlyBe3btwegncf8voIc4/Xr12FpaYmGDRuK27Rt2xY6Ojq4efOm2mMuDnFxcZDJZLC0tARQOo6ZqLQojfladkXJ3a5fv446derA3t5e3MbLywvx8fF4+PChGqNXnY/J67Tt9VBFzqdt50lV5ITa9ppkp6rjv379Olq2bAkDAwNxGy8vLwQEBODt27dqOpriUZRcUpWvh97HHwJlefPmDeRyudIffQCwt7fH48ePJYqq+CgUCowbNw7NmjVD7dq1AQDh4eEwMDAQ39BZ7O3tER4eLkGUqrF7927cu3cPt2/fzvEzbT3moKAgbNy4ERMmTMCPP/6I27dvY8yYMTAwMMCgQYPEY8vt/a6pxz1t2jTEx8ejevXq0NXVhVwux6JFi9CvXz8A0Mpjfl9BjjE8PBx2dnZKP9fT04O1tbVWvA6pqamYOnUq+vTpA3NzcwDaf8xEpUlpy9eyK2ruFh4enuvrlfUzTfOxeZ22vR6qyPm07TypipxQ216T7FR1/OHh4ahYsWKOfWT9zMrKqljiL25FzSVV+Xqw0ENFNmrUKDx48ABXrlyROpRi9fLlS4wdOxZnzpyBkZGR1OGojUKhQMOGDbF48WIAgIeHBx48eIBNmzZh0KBBEkdXPPbu3Ys///wTf/31F2rVqgVfX1+MGzcO5cuX19pjJmUZGRn4+uuvIQgCNm7cKHU4REQqVVpyt7yU1rwuL6Ux58sPc0IqqpKSS/LSLRWytbWFrq5ujln5IyIiUK5cOYmiKh4//PADjh49ivPnz6NChQpie7ly5ZCeno7Y2Fil7TX5Nbh79y4iIyNRv3596OnpQU9PDxcvXsQvv/wCPT092Nvba90xA4CDgwNq1qyp1FajRg28ePECAMRj06b3++TJkzFt2jT07t0bderUwYABAzB+/HgsWbIEgHYe8/sKcozlypVDZGSk0s8zMzMRExOj0a9D1ok5JCQEZ86cEb+BAbT3mIlKo9KUr2X3MblbuXLlcn29sn6mSVSR12nT6wGoJufTtvOkKnJCbXtNslPV8Wvb79LH5pKqfD1Y6FEhAwMDNGjQAN7e3mKbQqGAt7c3PD09JYxMdQRBwA8//ICDBw/i3LlzOYaWNWjQAPr6+kqvQUBAAF68eKGxr0GbNm3g7+8PX19f8dawYUP069dPvK9txwwAzZo1y7H86pMnT+Di4gIAqFixIsqVK6d03PHx8bh586bGHndycjJ0dJT/LOrq6kKhUADQzmN+X0GO0dPTE7Gxsbh79664zblz56BQKNCkSRO1x6wKWSfmp0+f4uzZs7CxsVH6uTYeM1FpVRrytexUkbt5enrC399f6UNK1oeY9wsEJZ0q8jptej0A1eR82naeVEVOqG2vSXaqOn5PT09cunQJGRkZ4jZnzpyBm5ubxl22pYpcUqWvR6Gnb6Y87d69WzA0NBR27Ngh/Pfff8J3330nWFpaKs3Kr8lGjBghWFhYCBcuXBDCwsLEW3JysrjN8OHDBWdnZ+HcuXPCnTt3BE9PT8HT01PCqFUv++oMgqCdx3zr1i1BT09PWLRokfD06VPhzz//FExMTIQ//vhD3Gbp0qWCpaWlcOjQIeH+/ftC165dNW6p8ewGDRokODo6iktpHjhwQLC1tRWmTJkibqMNx5yQkCD4+PgIPj4+AgBh1apVgo+Pj7gqQEGO8YsvvhA8PDyEmzdvCleuXBGqVq1aopcLzeuY09PThS5duggVKlQQfH19lf62ZV/1QNOOmYg+TNvztexUkbtlLSferl07wdfXVzh58qRQtmxZjV1O/H2Fzeu07fVQVc6nTedJVeWEmvyaqCNfjI2NFezt7YUBAwYIDx48EHbv3i2YmJiUyOXV1ZFLqvL1YKGnGKxdu1ZwdnYWDAwMhMaNGws3btyQOiSVAZDrbfv27eI2KSkpwsiRIwUrKyvBxMRE6N69uxAWFiZd0MXg/YRAW4/5yJEjQu3atQVDQ0OhevXqwq+//qr0c4VCIcyaNUuwt7cXDA0NhTZt2ggBAQESRfvx4uPjhbFjxwrOzs6CkZGRUKlSJWHGjBlKf6C14ZjPnz+f6+/xoEGDBEEo2DFGR0cLffr0EczMzARzc3NhyJAhQkJCggRHUzB5HXNwcPAH/7adP39e3IemHTMR5U2b87XsVJW7PX/+XGjfvr1gbGws2NraChMnThQyMjLUfDTFoyh5nba9HqrI+bTpPKmqnFCTXxN15Yt+fn5C8+bNBUNDQ8HR0VFYunSpug6xUNSVS6rq9ZAJgiAUbgwQERERERERERGVRJyjh4iIiIiIiIhIS7DQQ0RERERERESkJVjoISIiIiIiIiLSEiz0EBERERERERFpCRZ6iIiIiIiIiIi0BAs9RERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGWYKGHiIiIiIiIiEhLsNBDRColCAIAYO7cuUqPiYiIiEgazM+ISheZwN9yIlKhDRs2QE9PD0+fPoWuri7at2+PVq1aSR0WERERUanF/IyodOGIHiJSqZEjRyIuLg6//PILOnfuXKAkonXr1pDJZJDJZPD19S3+IN8zePBg8fn//fdftT8/ERERUXEqbH5WlNyM+RRRycFCDxGp1KZNm2BhYYExY8bgyJEjuHz5coH6DRs2DGFhYahdu3YxR5jTmjVrEBYWpvbnJSIiIlKl8ePHo0ePHjnai5KfFTY3Yz5FVHLoSR0AEWmX77//HjKZDHPnzsXcuXMLfA24iYkJypUrV8zR5c7CwgIWFhaSPDcRERGRqty6dQsdO3bM0V6U/KywuRnzKaKSgyN6iKhQFi9eLA7LzX5bvXo1AEAmkwH432R/WY8Lq3Xr1hg9ejTGjRsHKysr2NvbY8uWLUhKSsKQIUNQpkwZVKlSBSdOnFBJPyIiIiJNlZ6eDn19fVy7dg0zZsyATCbDJ598Iv5cVfnZ/v37UadOHRgbG8PGxgZt27ZFUlLSR8dPRKrFQg8RFcro0aMRFhYm3oYNGwYXFxd89dVXKn+unTt3wtbWFrdu3cLo0aMxYsQI9OzZE02bNsW9e/fQrl07DBgwAMnJySrpR0RERKSJ9PT0cPXqVQCAr68vwsLCcPLkSZU+R1hYGPr06YOhQ4fi0aNHuHDhAnr06MEVvIhKIBZ6iKhQypQpg3LlyqFcuXJYv349Tp8+jQsXLqBChQoqf666deti5syZqFq1KqZPnw4jIyPY2tpi2LBhqFq1KmbPno3o6Gjcv39fJf2IiIiINJGOjg5ev34NGxsb1K1bF+XKlYOlpaVKnyMsLAyZmZno0aMHXF1dUadOHYwcORJmZmYqfR4i+ngs9BBRkcyePRu///47Lly4AFdX12J5Dnd3d/G+rq4ubGxsUKdOHbHN3t4eABAZGamSfkRERESaysfHB3Xr1i22/detWxdt2rRBnTp10LNnT2zZsgVv374ttucjoqJjoYeICm3OnDnYtWtXsRZ5AEBfX1/psUwmU2rLur5coVCopB8RERGRpvL19S3WQo+uri7OnDmDEydOoGbNmli7di3c3NwQHBxcbM9JREXDQg8RFcqcOXOwc+fOYi/yEBEREVHB+fv7o169esX6HDKZDM2aNcO8efPg4+MDAwMDHDx4sFifk4gKj8urE1GBLVy4EBs3bsThw4dhZGSE8PBwAICVlRUMDQ0ljo6IiIio9FIoFAgICMDr169hamqq8qXOb968CW9vb7Rr1w52dna4efMmoqKiUKNGDZU+DxF9PI7oIaICEQQBK1asQFRUFDw9PeHg4CDeOKkxERERkbQWLlyIHTt2wNHREQsXLlT5/s3NzXHp0iV06NAB1apVw8yZM7Fy5Uq0b99e5c9FRB+HI3qIqEBkMhni4uLU9nwXLlzI0fb8+fMcbe8v6VnUfkRERESarH///ujfv3+x7b9GjRoqX7KdiIoHR/QQUYmwYcMGmJmZwd/fX+3PPXz4cC4NSkRERJRNYXMz5lNEJYdM4NfaRCSx0NBQpKSkAACcnZ1hYGCg1uePjIxEfHw8AMDBwQGmpqZqfX4iIiKikqQouRnzKaKSg4UeIiIiIiIiIiItwUu3iIiIiIiIiIi0BAs9RERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGWYKGHiIiIiIiIiEhLsNBDRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqIiIiIiIiIiLQECz1ERERERERERFqChR4iIiIiIiIiIi3xf1HyohSso0QSAAAAAElFTkSuQmCC", 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" ] @@ -612,7 +626,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -676,7 +690,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -729,7 +743,7 @@ "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -828,7 +842,7 @@ ], "metadata": { "kernelspec": { - "display_name": "venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -842,7 +856,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.6" + "version": "3.12.3" }, "toc": { "base_numbering": 1, @@ -864,5 +878,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/source/examples/notebooks/models/saving_models.ipynb b/docs/source/examples/notebooks/models/saving_models.ipynb index 57bda3ef85..ca9d99b6dd 100644 --- a/docs/source/examples/notebooks/models/saving_models.ipynb +++ b/docs/source/examples/notebooks/models/saving_models.ipynb @@ -96,12 +96,12 @@ "evalue": "No variables to plot", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/Users/pipliggins/Documents/repos/pybamm-local/docs/source/examples/notebooks/models/saving_models.ipynb Cell 7\u001b[0m line \u001b[0;36m8\n\u001b[1;32m 5\u001b[0m plot_sim\u001b[39m.\u001b[39msolve([\u001b[39m0\u001b[39m, \u001b[39m3600\u001b[39m])\n\u001b[1;32m 6\u001b[0m sims\u001b[39m.\u001b[39mappend(plot_sim)\n\u001b[0;32m----> 8\u001b[0m pybamm\u001b[39m.\u001b[39;49mdynamic_plot(sims, time_unit\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mseconds\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n", - "File \u001b[0;32m~/Documents/repos/pybamm-local/pybamm/plotting/dynamic_plot.py:20\u001b[0m, in \u001b[0;36mdynamic_plot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 9\u001b[0m \u001b[39mCreates a :class:`pybamm.QuickPlot` object (with arguments 'args' and keyword\u001b[39;00m\n\u001b[1;32m 10\u001b[0m \u001b[39marguments 'kwargs') and then calls :meth:`pybamm.QuickPlot.dynamic_plot`.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[39m The 'QuickPlot' object that was created\u001b[39;00m\n\u001b[1;32m 18\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 19\u001b[0m kwargs_for_class \u001b[39m=\u001b[39m {k: v \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m kwargs\u001b[39m.\u001b[39mitems() \u001b[39mif\u001b[39;00m k \u001b[39m!=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mtesting\u001b[39m\u001b[39m\"\u001b[39m}\n\u001b[0;32m---> 20\u001b[0m plot \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39;49mQuickPlot(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs_for_class)\n\u001b[1;32m 21\u001b[0m plot\u001b[39m.\u001b[39mdynamic_plot(kwargs\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mtesting\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mFalse\u001b[39;00m))\n\u001b[1;32m 22\u001b[0m \u001b[39mreturn\u001b[39;00m plot\n", - "File \u001b[0;32m~/Documents/repos/pybamm-local/pybamm/plotting/quick_plot.py:146\u001b[0m, in \u001b[0;36mQuickPlot.__init__\u001b[0;34m(self, solutions, output_variables, labels, colors, linestyles, shading, figsize, n_rows, time_unit, spatial_unit, variable_limits)\u001b[0m\n\u001b[1;32m 144\u001b[0m \u001b[39m# check variables have been provided after any serialisation\u001b[39;00m\n\u001b[1;32m 145\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39many\u001b[39m(\u001b[39mlen\u001b[39m(m\u001b[39m.\u001b[39mvariables) \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m \u001b[39mfor\u001b[39;00m m \u001b[39min\u001b[39;00m models):\n\u001b[0;32m--> 146\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mNo variables to plot\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 148\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_rows \u001b[39m=\u001b[39m n_rows \u001b[39mor\u001b[39;00m \u001b[39mint\u001b[39m(\n\u001b[1;32m 149\u001b[0m \u001b[39mlen\u001b[39m(output_variables) \u001b[39m/\u001b[39m\u001b[39m/\u001b[39m np\u001b[39m.\u001b[39msqrt(\u001b[39mlen\u001b[39m(output_variables))\n\u001b[1;32m 150\u001b[0m )\n\u001b[1;32m 151\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_cols \u001b[39m=\u001b[39m \u001b[39mint\u001b[39m(np\u001b[39m.\u001b[39mceil(\u001b[39mlen\u001b[39m(output_variables) \u001b[39m/\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_rows))\n", - "\u001b[0;31mAttributeError\u001b[0m: No variables to plot" + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mAttributeError\u001B[0m Traceback (most recent call last)", + "\u001B[1;32m/Users/pipliggins/Documents/repos/pybamm-local/docs/source/examples/notebooks/models/saving_models.ipynb Cell 7\u001B[0m line \u001B[0;36m8\n\u001B[1;32m 5\u001B[0m plot_sim\u001B[39m.\u001B[39msolve([\u001B[39m0\u001B[39m, \u001B[39m3600\u001B[39m])\n\u001B[1;32m 6\u001B[0m sims\u001B[39m.\u001B[39mappend(plot_sim)\n\u001B[0;32m----> 8\u001B[0m pybamm\u001B[39m.\u001B[39;49mdynamic_plot(sims, time_unit\u001B[39m=\u001B[39;49m\u001B[39m\"\u001B[39;49m\u001B[39mseconds\u001B[39;49m\u001B[39m\"\u001B[39;49m)\n", + "File \u001B[0;32m~/Documents/repos/pybamm-local/pybamm/plotting/dynamic_plot.py:20\u001B[0m, in \u001B[0;36mdynamic_plot\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m 8\u001B[0m \u001B[39m\u001B[39m\u001B[39m\"\"\"\u001B[39;00m\n\u001B[1;32m 9\u001B[0m \u001B[39mCreates a :class:`pybamm.QuickPlot` object (with arguments 'args' and keyword\u001B[39;00m\n\u001B[1;32m 10\u001B[0m \u001B[39marguments 'kwargs') and then calls :meth:`pybamm.QuickPlot.dynamic_plot`.\u001B[39;00m\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 17\u001B[0m \u001B[39m The 'QuickPlot' object that was created\u001B[39;00m\n\u001B[1;32m 18\u001B[0m \u001B[39m\"\"\"\u001B[39;00m\n\u001B[1;32m 19\u001B[0m kwargs_for_class \u001B[39m=\u001B[39m {k: v \u001B[39mfor\u001B[39;00m k, v \u001B[39min\u001B[39;00m kwargs\u001B[39m.\u001B[39mitems() \u001B[39mif\u001B[39;00m k \u001B[39m!=\u001B[39m \u001B[39m\"\u001B[39m\u001B[39mtesting\u001B[39m\u001B[39m\"\u001B[39m}\n\u001B[0;32m---> 20\u001B[0m plot \u001B[39m=\u001B[39m pybamm\u001B[39m.\u001B[39;49mQuickPlot(\u001B[39m*\u001B[39;49margs, \u001B[39m*\u001B[39;49m\u001B[39m*\u001B[39;49mkwargs_for_class)\n\u001B[1;32m 21\u001B[0m plot\u001B[39m.\u001B[39mdynamic_plot(kwargs\u001B[39m.\u001B[39mget(\u001B[39m\"\u001B[39m\u001B[39mtesting\u001B[39m\u001B[39m\"\u001B[39m, \u001B[39mFalse\u001B[39;00m))\n\u001B[1;32m 22\u001B[0m \u001B[39mreturn\u001B[39;00m plot\n", + "File \u001B[0;32m~/Documents/repos/pybamm-local/pybamm/plotting/quick_plot.py:146\u001B[0m, in \u001B[0;36mQuickPlot.__init__\u001B[0;34m(self, solutions, output_variables, labels, colors, linestyles, shading, figsize, n_rows, time_unit, spatial_unit, variable_limits)\u001B[0m\n\u001B[1;32m 144\u001B[0m \u001B[39m# check variables have been provided after any serialisation\u001B[39;00m\n\u001B[1;32m 145\u001B[0m \u001B[39mif\u001B[39;00m \u001B[39many\u001B[39m(\u001B[39mlen\u001B[39m(m\u001B[39m.\u001B[39mvariables) \u001B[39m==\u001B[39m \u001B[39m0\u001B[39m \u001B[39mfor\u001B[39;00m m \u001B[39min\u001B[39;00m models):\n\u001B[0;32m--> 146\u001B[0m \u001B[39mraise\u001B[39;00m \u001B[39mAttributeError\u001B[39;00m(\u001B[39m\"\u001B[39m\u001B[39mNo variables to plot\u001B[39m\u001B[39m\"\u001B[39m)\n\u001B[1;32m 148\u001B[0m \u001B[39mself\u001B[39m\u001B[39m.\u001B[39mn_rows \u001B[39m=\u001B[39m n_rows \u001B[39mor\u001B[39;00m \u001B[39mint\u001B[39m(\n\u001B[1;32m 149\u001B[0m \u001B[39mlen\u001B[39m(output_variables) \u001B[39m/\u001B[39m\u001B[39m/\u001B[39m np\u001B[39m.\u001B[39msqrt(\u001B[39mlen\u001B[39m(output_variables))\n\u001B[1;32m 150\u001B[0m )\n\u001B[1;32m 151\u001B[0m \u001B[39mself\u001B[39m\u001B[39m.\u001B[39mn_cols \u001B[39m=\u001B[39m \u001B[39mint\u001B[39m(np\u001B[39m.\u001B[39mceil(\u001B[39mlen\u001B[39m(output_variables) \u001B[39m/\u001B[39m \u001B[39mself\u001B[39m\u001B[39m.\u001B[39mn_rows))\n", + "\u001B[0;31mAttributeError\u001B[0m: No variables to plot" ] } ], @@ -354,7 +354,7 @@ ], "metadata": { "kernelspec": { - "display_name": "dev", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -368,10 +368,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.6" - }, - "orig_nbformat": 4 + "version": "3.12.3" + } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/source/examples/notebooks/models/tortuosity_models.ipynb b/docs/source/examples/notebooks/models/tortuosity_models.ipynb new file mode 100644 index 0000000000..51023008cd --- /dev/null +++ b/docs/source/examples/notebooks/models/tortuosity_models.ipynb @@ -0,0 +1,348 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Transport efficiency and the models for tortuosity factor" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PyBaMM models utilize a ratio that we refer to as \"transport efficiency\" $\\mathcal{B}$ which can be applied to transport co-efficients such as the diffusivity in the electrolyte that relates the effective transport property through a porous media comprised of a conducting and non-conducting phase to that of the transport through the bulk of the conducting phase:\n", + "$$\n", + "\\mathcal{B} = \\frac{X_{eff}}{X_0} = \\frac{\\epsilon}{\\tau},\n", + "$$\n", + "\n", + "Where $\\epsilon$ is the volume fraction of the conducting phase, the porosity of the electrode for diffusion within the electrolyte, and $\\tau$ is the tortuosity factor. A measure of the effect of the increased pathlength that transported species traverse due to the presence of obstacles.\n", + "\n", + "The tortuosity and tortuosity factor are often used interchangably but this can lead to confusion. Tortusosity is a purely geometric concept relating the length of a winding capillary pathway through a medium with the length of that medium, whereas tortuosity factor relates the the ratio of the transport property which may also depend on other factors such as anisotropic obstacles, boundary conditions of flow and also other physical phenomena such as the average pore size which could induce Knudsen effects. \n", + "\n", + "Many studies have been devoted to understanding relations between $\\tau$ and $\\epsilon$ including those summarized by [Shen & Chen](https://www.sciencedirect.com/science/article/abs/pii/S0009250907003144). By far the most common is the Bruggeman relation which is explored more recently by [Tjaden et al.](http://dx.doi.org/10.1016/j.coche.2016.02.006) in the context of materials commonly found in batteries and fuel cells." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A recent study by [Usseglio-Viretta et al.](https://iopscience.iop.org/article/10.1149/2.0731814jes) found that Bruggeman and similar relations can significantly underpredict the tortuosity factors. If used at all these relations are often more suitable for the cathode where particles are more spherical but should be used with caution for the anode. A more recent trend is to use numerical methods to calculate tortuosity factors directly from image data gathered for electrodes in which case a straight-forward relation with porosity may not exist and is not necessary if factors can be directly supplied." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The remainder of the notebook demonstrates how to use the different options for transport efficiency and supply your own tortuosity factor" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pybamm\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Bruggeman', 'ordered packing', 'hyperbola of revolution', 'overlapping spheres', 'tortuosity factor', 'random overlapping cylinders', 'heterogeneous catalyst', 'cation-exchange membrane']\n" + ] + } + ], + "source": [ + "sols = []\n", + "te_opts = pybamm.BatteryModelOptions({}).possible_options[\"transport efficiency\"]\n", + "parameter_values = pybamm.ParameterValues(\"Marquis2019\")\n", + "print(te_opts)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Negative electrode porosity\t0.3\n", + "Positive electrode porosity\t0.3\n", + "Separator porosity\t1.0\n" + ] + } + ], + "source": [ + "parameter_values.search(\"porosity\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Negative electrode Bruggeman coefficient (electrode)\t1.5\n", + "Negative electrode Bruggeman coefficient (electrolyte)\t1.5\n", + "Positive electrode Bruggeman coefficient (electrode)\t1.5\n", + "Positive electrode Bruggeman coefficient (electrolyte)\t1.5\n", + "Separator Bruggeman coefficient (electrolyte)\t1.5\n" + ] + } + ], + "source": [ + "parameter_values.search(\"Bruggeman\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Add tortuosity factors that replicate the Bruggeman values" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "parameter_values.update(\n", + " {\n", + " \"Negative electrode tortuosity factor (electrolyte)\": 0.3 ** (-0.5),\n", + " \"Positive electrode tortuosity factor (electrolyte)\": 0.3 ** (-0.5),\n", + " \"Negative electrode tortuosity factor (electrode)\": 0.7 ** (-0.5),\n", + " \"Positive electrode tortuosity factor (electrode)\": 0.7 ** (-0.5),\n", + " \"Separator tortuosity factor (electrolyte)\": 1.0,\n", + " },\n", + " check_already_exists=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "N.B the differences in the exponent constants used to modify the porosity and solid volume fraction. The existing Bruggeman model applies the exponent directly to the porosity $\\mathcal{B}=\\epsilon^{b}=\\epsilon^{3/2}$, the tortuosity factor model applies the tortuosity factor with includes the relation on porosity in this case $\\mathcal{B}=\\epsilon / \\tau = \\epsilon / \\epsilon^{-1/2} = \\epsilon^{3/2}$." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "for t_label in te_opts:\n", + " model = pybamm.lithium_ion.DFN(\n", + " options={\"transport efficiency\": t_label}\n", + " ) # Doyle-Fuller-Newman model\n", + " sim = pybamm.Simulation(model, parameter_values=parameter_values)\n", + " sols.append(sim.solve([0, 3600])) # solve for 1 hour" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2a0d1356fdaf495c8898429e27e30478", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pybamm.dynamic_plot(sols, labels=te_opts)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Bruggeman and tortuosity factor results should be identical" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.allclose(sols[0][\"Terminal voltage [V]\"].data, sols[4][\"Terminal voltage [V]\"].data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now increase the tortuosity factors. N.B this will need to be calculated for specific electrodes with given porosity. Changing porosity in the model will not update the tortuosity factor unless a function is supplied for the parameter." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "parameter_values.update(\n", + " {\n", + " \"Negative electrode tortuosity factor (electrolyte)\": 4.0,\n", + " \"Positive electrode tortuosity factor (electrolyte)\": 4.0,\n", + " \"Negative electrode tortuosity factor (electrode)\": 3.0,\n", + " \"Positive electrode tortuosity factor (electrode)\": 3.0,\n", + " \"Separator tortuosity factor (electrolyte)\": 1.5,\n", + " },\n", + " check_already_exists=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "model = pybamm.lithium_ion.DFN(\n", + " options={\"transport efficiency\": \"tortuosity factor\"}\n", + ") # Doyle-Fuller-Newman model\n", + "sim = pybamm.Simulation(model, parameter_values=parameter_values)\n", + "sols.append(sim.solve([0, 3600]))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5292e8b8c6884c199cf5023cf42ea3ac", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pybamm.dynamic_plot(sols, labels=[*te_opts, \"higher tortuosity factor\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The higher tortuosity leads to greater overpotential in the electrolyte and lower terminal voltage" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] KA Akanni, JW Evans, and IS Abramson. Effective transport coefficients in heterogeneous media. Chemical Engineering Science, 42(8):1945–1954, 1987.\n", + "[2] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", + "[3] JW Beeckman. Mathematical description of heterogeneous materials. Chemical engineering science, 45(8):2603–2610, 1990.\n", + "[4] Von DAG Bruggeman. Berechnung verschiedener physikalischer konstanten von heterogenen substanzen. i. dielektrizitätskonstanten und leitfähigkeiten der mischkörper aus isotropen substanzen. Annalen der physik, 416(7):636–664, 1935.\n", + "[5] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", + "[6] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", + "[7] JS Mackie and P Meares. The diffusion of electrolytes in a cation-exchange resin membrane i. theoretical. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 232(1191):498–509, 1955.\n", + "[8] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", + "[9] EE Petersen. Diffusion in a pore of varying cross section. AIChE Journal, 4(3):343–345, 1958.\n", + "[10] Lihua Shen and Zhangxin Chen. Critical review of the impact of tortuosity on diffusion. Chemical Engineering Science, 62(14):3748–3755, 2007.\n", + "[11] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", + "[12] Manolis M Tomadakis and Stratis V Sotirchos. Transport properties of random arrays of freely overlapping cylinders with various orientation distributions. The Journal of chemical physics, 98(1):616–626, 1993.\n", + "[13] Harold L Weissberg. Effective diffusion coefficient in porous media. Journal of Applied Physics, 34(9):2636–2639, 1963.\n", + "\n" + ] + } + ], + "source": [ + "pybamm.print_citations()" + ] + } + ], + "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": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/source/examples/notebooks/models/using-submodels.ipynb b/docs/source/examples/notebooks/models/using-submodels.ipynb index d02e5489c7..cce32fcefd 100644 --- a/docs/source/examples/notebooks/models/using-submodels.ipynb +++ b/docs/source/examples/notebooks/models/using-submodels.ipynb @@ -142,10 +142,12 @@ "metadata": {}, "outputs": [], "source": [ - "model.submodels[\n", - " \"negative primary particle\"\n", - "] = pybamm.particle.XAveragedPolynomialProfile(\n", - " model.param, \"negative\", options={**model.options, \"particle\": \"uniform profile\"}\n", + "model.submodels[\"negative primary particle\"] = (\n", + " pybamm.particle.XAveragedPolynomialProfile(\n", + " model.param,\n", + " \"negative\",\n", + " options={**model.options, \"particle\": \"uniform profile\"},\n", + " )\n", ")" ] }, @@ -278,7 +280,7 @@ "{Variable(0x3825da4a5fc4eb0b, Discharge capacity [A.h], children=[], domains={}): Multiplication(0x7678edd47e530eec, *, children=['0.0002777777777777778', 'Current function [A]'], domains={}),\n", " Variable(-0x7fb8d0e6e9632372, Throughput capacity [A.h], children=[], domains={}): Multiplication(-0x7c65e8600b424661, *, children=['0.0002777777777777778', 'abs(Current function [A])'], domains={}),\n", " Variable(0x69f725db1a464db8, Average negative particle concentration [mol.m-3], children=[], domains={'primary': ['current collector']}): MatrixMultiplication(0xf98a766c86b2483, @, children=['mass(Average negative particle concentration [mol.m-3])', '-3.0 * Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Negative electrode thickness [m] / x-average(3.0 * Negative electrode active material volume fraction / Negative particle radius [m]) / Faraday constant [C.mol-1] / x-average(Negative particle radius [m])'], domains={'primary': ['current collector']}),\n", - " Variable(0x48143b39c7603013, X-averaged positive particle concentration [mol.m-3], children=[], domains={'primary': ['positive particle'], 'secondary': ['current collector']}): Divergence(0x17c75a81711ad510, div, children=['Positive electrode diffusivity [m2.s-1] * grad(X-averaged positive particle concentration [mol.m-3])'], domains={'primary': ['positive particle'], 'secondary': ['current collector']})}" + " Variable(0x48143b39c7603013, X-averaged positive particle concentration [mol.m-3], children=[], domains={'primary': ['positive particle'], 'secondary': ['current collector']}): Divergence(0x17c75a81711ad510, div, children=['Positive particle diffusivity [m2.s-1] * grad(X-averaged positive particle concentration [mol.m-3])'], domains={'primary': ['positive particle'], 'secondary': ['current collector']})}" ] }, "execution_count": 9, @@ -431,19 +433,19 @@ "outputs": [], "source": [ "options = {**model.options, \"particle\": \"uniform profile\"}\n", - "model.submodels[\n", - " \"negative primary particle\"\n", - "] = pybamm.particle.XAveragedPolynomialProfile(model.param, \"negative\", options)\n", - "model.submodels[\n", - " \"positive primary particle\"\n", - "] = pybamm.particle.XAveragedPolynomialProfile(model.param, \"positive\", options)\n", + "model.submodels[\"negative primary particle\"] = (\n", + " pybamm.particle.XAveragedPolynomialProfile(model.param, \"negative\", options)\n", + ")\n", + "model.submodels[\"positive primary particle\"] = (\n", + " pybamm.particle.XAveragedPolynomialProfile(model.param, \"positive\", options)\n", + ")\n", "\n", - "model.submodels[\n", - " \"negative total particle concentration\"\n", - "] = pybamm.particle.TotalConcentration(model.param, \"negative\", options)\n", - "model.submodels[\n", - " \"positive total particle concentration\"\n", - "] = pybamm.particle.TotalConcentration(model.param, \"positive\", options)" + "model.submodels[\"negative total particle concentration\"] = (\n", + " pybamm.particle.TotalConcentration(model.param, \"negative\", options)\n", + ")\n", + "model.submodels[\"positive total particle concentration\"] = (\n", + " pybamm.particle.TotalConcentration(model.param, \"positive\", options)\n", + ")" ] }, { @@ -459,15 +461,15 @@ "metadata": {}, "outputs": [], "source": [ - "model.submodels[\n", - " \"negative open-circuit potential\"\n", - "] = pybamm.open_circuit_potential.SingleOpenCircuitPotential(\n", - " model.param, \"negative\", \"lithium-ion main\", options=model.options\n", + "model.submodels[\"negative open-circuit potential\"] = (\n", + " pybamm.open_circuit_potential.SingleOpenCircuitPotential(\n", + " model.param, \"negative\", \"lithium-ion main\", options=model.options\n", + " )\n", ")\n", - "model.submodels[\n", - " \"positive open-circuit potential\"\n", - "] = pybamm.open_circuit_potential.SingleOpenCircuitPotential(\n", - " model.param, \"positive\", \"lithium-ion main\", options=model.options\n", + "model.submodels[\"positive open-circuit potential\"] = (\n", + " pybamm.open_circuit_potential.SingleOpenCircuitPotential(\n", + " model.param, \"positive\", \"lithium-ion main\", options=model.options\n", + " )\n", ")\n", "model.submodels[\"negative interface\"] = pybamm.kinetics.InverseButlerVolmer(\n", " model.param, \"negative\", \"lithium-ion main\", options=model.options\n", @@ -475,15 +477,15 @@ "model.submodels[\"positive interface\"] = pybamm.kinetics.InverseButlerVolmer(\n", " model.param, \"positive\", \"lithium-ion main\", options=model.options\n", ")\n", - "model.submodels[\n", - " \"negative interface current\"\n", - "] = pybamm.kinetics.CurrentForInverseButlerVolmer(\n", - " model.param, \"negative\", \"lithium-ion main\"\n", + "model.submodels[\"negative interface current\"] = (\n", + " pybamm.kinetics.CurrentForInverseButlerVolmer(\n", + " model.param, \"negative\", \"lithium-ion main\"\n", + " )\n", ")\n", - "model.submodels[\n", - " \"positive interface current\"\n", - "] = pybamm.kinetics.CurrentForInverseButlerVolmer(\n", - " model.param, \"positive\", \"lithium-ion main\"\n", + "model.submodels[\"positive interface current\"] = (\n", + " pybamm.kinetics.CurrentForInverseButlerVolmer(\n", + " model.param, \"positive\", \"lithium-ion main\"\n", + " )\n", ")\n", "model.submodels[\"negative interface utilisation\"] = pybamm.interface_utilisation.Full(\n", " model.param, \"negative\", model.options\n", @@ -545,12 +547,12 @@ "metadata": {}, "outputs": [], "source": [ - "model.submodels[\n", - " \"electrolyte diffusion\"\n", - "] = pybamm.electrolyte_diffusion.ConstantConcentration(model.param)\n", - "model.submodels[\n", - " \"electrolyte conductivity\"\n", - "] = pybamm.electrolyte_conductivity.LeadingOrder(model.param)" + "model.submodels[\"electrolyte diffusion\"] = (\n", + " pybamm.electrolyte_diffusion.ConstantConcentration(model.param)\n", + ")\n", + "model.submodels[\"electrolyte conductivity\"] = (\n", + " pybamm.electrolyte_conductivity.LeadingOrder(model.param)\n", + ")" ] }, { diff --git a/docs/source/examples/notebooks/parameterization/change-input-current.ipynb b/docs/source/examples/notebooks/parameterization/change-input-current.ipynb index 28fc476e16..8189033f2b 100644 --- a/docs/source/examples/notebooks/parameterization/change-input-current.ipynb +++ b/docs/source/examples/notebooks/parameterization/change-input-current.ipynb @@ -7,7 +7,7 @@ "source": [ "# Changing the input current when solving PyBaMM models\n", "\n", - "This notebook shows you how to change the input current when solving PyBaMM models. It also explains how to load in current data from a file, and how to add a user-defined current function. For more examples of different drive cycles see [here](https://github.com/pybamm-team/PyBaMM/tree/develop/pybamm/input/drive_cycles).\n", + "This notebook shows you how to change the input current when solving PyBaMM models. It also explains how to load in current data from a file, and how to add a user-defined current function. For more examples of different drive cycles see [here](https://github.com/pybamm-team/pybamm-data/releases/tag/v1.0.0).\n", "\n", "### Table of Contents\n", "1. Constant current\n", @@ -38,6 +38,13 @@ "text": [ "Note: you may need to restart the kernel to use updated packages.\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" + ] } ], "source": [ @@ -76,7 +83,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aef64b871f1346a4b42c722c7eecfe38", + "model_id": "174048e3ccf74930a39a249dc0723975", "version_major": 2, "version_minor": 0 }, @@ -86,6 +93,16 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -110,13 +127,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6447d4f706374208b5cbd283577b5da5", + "model_id": "bd1dbdfee61f4e518c3534b44e0ce879", "version_major": 2, "version_minor": 0 }, @@ -126,6 +143,16 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -148,17 +175,26 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading file 'US06.csv' from 'https://github.com/pybamm-team/pybamm-data/releases/download/v1.0.0/US06.csv' to '/home/santa/.cache/pybamm'.\n" + ] + } + ], "source": [ "import pandas as pd # needed to read the csv data file\n", "\n", "model = pybamm.lithium_ion.DFN()\n", "\n", "# import drive cycle from file\n", + "data_loader = pybamm.DataLoader()\n", "drive_cycle = pd.read_csv(\n", - " \"pybamm/input/drive_cycles/US06.csv\", comment=\"#\", header=None\n", + " f\"{data_loader.get_data(\"US06.csv\")}\", comment=\"#\", header=None\n", ").to_numpy()\n", "\n", "# load parameter values\n", @@ -185,13 +221,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "84f87c3d21644c20bafdac8e9b69247d", + "model_id": "513c59118a6144a8a205510aaa9e9527", "version_major": 2, "version_minor": 0 }, @@ -201,6 +237,16 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -233,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -258,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -283,13 +329,13 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6c06d938eca0491d88cdcbb29c59cd2a", + "model_id": "40a6ad712e184ba1a161932440d7a99d", "version_major": 2, "version_minor": 0 }, @@ -299,6 +345,16 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -352,7 +408,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.13 ('conda_jl')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -366,7 +422,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.12.3" }, "toc": { "base_numbering": 1, @@ -388,5 +444,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/source/examples/notebooks/parameterization/parameterization.ipynb b/docs/source/examples/notebooks/parameterization/parameterization.ipynb index 9c060ed1ff..dabb5e5f76 100644 --- a/docs/source/examples/notebooks/parameterization/parameterization.ipynb +++ b/docs/source/examples/notebooks/parameterization/parameterization.ipynb @@ -28,11 +28,11 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 47, "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T12:14:18.822760400Z", - "start_time": "2023-12-10T12:14:16.732217100Z" + "end_time": "2024-03-05T17:18:14.064867Z", + "start_time": "2024-03-05T17:18:12.213989Z" } }, "outputs": [ @@ -64,11 +64,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 48, "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T12:14:18.832156400Z", - "start_time": "2023-12-10T12:14:18.822760400Z" + "end_time": "2024-03-05T17:18:14.069938Z", + "start_time": "2024-03-05T17:18:14.066269Z" } }, "outputs": [], @@ -94,11 +94,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 49, "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T12:14:18.841423200Z", - "start_time": "2023-12-10T12:14:18.827008900Z" + "end_time": "2024-03-05T17:18:14.080836Z", + "start_time": "2024-03-05T17:18:14.071995Z" } }, "outputs": [], @@ -135,11 +135,11 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 50, "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T12:14:18.843095800Z", - "start_time": "2023-12-10T12:14:18.841423200Z" + "end_time": "2024-03-05T17:18:14.086084Z", + "start_time": "2024-03-05T17:18:14.082772Z" } }, "outputs": [], @@ -170,11 +170,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 51, "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T12:14:18.852037800Z", - "start_time": "2023-12-10T12:14:18.845139Z" + "end_time": "2024-03-05T17:18:14.091142Z", + "start_time": "2024-03-05T17:18:14.087498Z" } }, "outputs": [ @@ -182,11 +182,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "| Parameter | Type of parameter |\n", - "| =================================== | ========================================================== |\n", - "| Initial concentration [mol.m-3] | Parameter |\n", - "| Interfacial current density [A.m-2] | InputParameter |\n", - "| Diffusion coefficient [m2.s-1] | FunctionParameter with inputs(s) 'Concentration [mol.m-3]' |\n", + "┌─────────────────────────────────────┬────────────────────────────────────────────────────────────┐\n", + "│ Parameter │ Type of parameter │\n", + "├─────────────────────────────────────┼────────────────────────────────────────────────────────────┤\n", + "│ Initial concentration [mol.m-3] │ Parameter │\n", + "│ Interfacial current density [A.m-2] │ InputParameter │\n", + "│ Diffusion coefficient [m2.s-1] │ FunctionParameter with inputs(s) 'Concentration [mol.m-3]' │\n", + "└─────────────────────────────────────┴────────────────────────────────────────────────────────────┘\n", + "\n", "Particle radius [m] (Parameter)\n" ] } @@ -216,11 +219,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 52, "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T12:14:18.854076300Z", - "start_time": "2023-12-10T12:14:18.849343800Z" + "end_time": "2024-03-05T17:18:14.095958Z", + "start_time": "2024-03-05T17:18:14.093079Z" } }, "outputs": [], @@ -246,19 +249,19 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 53, "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T12:14:18.889781200Z", - "start_time": "2023-12-10T12:14:18.853120600Z" + "end_time": "2024-03-05T17:18:14.102002Z", + "start_time": "2024-03-05T17:18:14.097656Z" } }, "outputs": [ { "data": { - "text/plain": "{'Boltzmann constant [J.K-1]': 1.380649e-23,\n 'Diffusion coefficient [m2.s-1]': ,\n 'Electron charge [C]': 1.602176634e-19,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Initial concentration [mol.m-3]': 2.5,\n 'Particle radius [m]': 2}" + "text/plain": "{'Boltzmann constant [J.K-1]': 1.380649e-23,\n 'Diffusion coefficient [m2.s-1]': ,\n 'Electron charge [C]': 1.602176634e-19,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Initial concentration [mol.m-3]': 2.5,\n 'Particle radius [m]': 2}" }, - "execution_count": 7, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -281,19 +284,19 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 54, "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T12:14:18.890819200Z", - "start_time": "2023-12-10T12:14:18.859679800Z" + "end_time": "2024-03-05T17:18:14.107054Z", + "start_time": "2024-03-05T17:18:14.103370Z" } }, "outputs": [ { "data": { - "text/plain": "{'Boltzmann constant [J.K-1]': 1.380649e-23,\n 'Diffusion coefficient [m2.s-1]': ,\n 'Electron charge [C]': 1.602176634e-19,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Initial concentration [mol.m-3]': 1.5,\n 'Particle radius [m]': 2}" + "text/plain": "{'Boltzmann constant [J.K-1]': 1.380649e-23,\n 'Diffusion coefficient [m2.s-1]': ,\n 'Electron charge [C]': 1.602176634e-19,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Initial concentration [mol.m-3]': 1.5,\n 'Particle radius [m]': 2}" }, - "execution_count": 8, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -323,20 +326,20 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 55, "metadata": { + "scrolled": true, "ExecuteTime": { - "end_time": "2023-12-10T12:14:18.891821400Z", - "start_time": "2023-12-10T12:14:18.864911Z" - }, - "scrolled": true + "end_time": "2024-03-05T17:18:14.112188Z", + "start_time": "2024-03-05T17:18:14.108312Z" + } }, "outputs": [ { "data": { - "text/plain": "[Parameter(-0x60748912cbf94f86, Initial concentration [mol.m-3], children=[], domains={}),\n InputParameter(0x650425db234f99f4, Interfacial current density [A.m-2], children=[], domains={}),\n FunctionParameter(-0x302b1e5afcbfd4d9, Diffusion coefficient [m2.s-1], children=['Concentration [mol.m-3]'], domains={'primary': ['negative particle']})]" + "text/plain": "[FunctionParameter(0x5151bd3d66db3d0a, Diffusion coefficient [m2.s-1], children=['Concentration [mol.m-3]'], domains={'primary': ['negative particle']}),\n Parameter(0x1643caef07df6bfd, Initial concentration [mol.m-3], children=[], domains={}),\n InputParameter(-0x8e0791036f53f59, Interfacial current density [A.m-2], children=[], domains={})]" }, - "execution_count": 9, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -356,11 +359,11 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 56, "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T12:14:18.891821400Z", - "start_time": "2023-12-10T12:14:18.868969800Z" + "end_time": "2024-03-05T17:18:14.118160Z", + "start_time": "2024-03-05T17:18:14.114560Z" } }, "outputs": [], @@ -379,11 +382,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 57, "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T12:14:18.951625100Z", - "start_time": "2023-12-10T12:14:18.875173500Z" + "end_time": "2024-03-05T17:18:14.133217Z", + "start_time": "2024-03-05T17:18:14.119400Z" } }, "outputs": [], @@ -407,18 +410,18 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 58, "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T12:14:19.168402100Z", - "start_time": "2023-12-10T12:14:18.890819200Z" + "end_time": "2024-03-05T17:18:14.419240Z", + "start_time": "2024-03-05T17:18:14.134441Z" } }, "outputs": [ { "data": { - "image/png": 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4NuPXQzGXuZacxr7Tiew7nfmpQ08Xh0wFQ39vV6p5lcTeVk8bikjxZDAYstTmWxDlZotxVpaM8/b2Jjk5mfj4+ExPEf57zLZt226Z4+Z7d+Lj48OhQ4dYtWoVISEhPPfcc7z99tusX78eOzvrLqVROP90FBDDhw8vkC3FWWFnY+SJVn70aFCeN5cf5LfwM3z710mW743hxc416Rvoo7ZjERERESkQ3JzsaFqlFE2rlLIcS083E33pmqVgGHHj1xNx17hw2cSFyyY2Ho61jLc1GqjmVfJGm/LfTxx6uThkafF8ERHJe/b29qSlZd5MJTdbjLOyZFxgYCB2dnasXr2a3r17A3Do0CFOnjxJUFAQAEFBQbzxxhucP3/esvNxSEgIrq6u1K5d+64xODk50aNHD3r06MGwYcPw9/dn7969NGrUKMvfIy+oQFjMlXV1ZPbDATzcpBKTl+wj8twVXvxlLz9si+a1nnWpV1FtxyIiIiJS8BiNBsvah53r/v20xlVTKpHnLhMRc5mIs4kcvPFrYlJqxrGYyxB+xjLew9kuo0W5nAsBlTxo6lsKbzdHa3wlEZFiz9fXl61bt3L8+HFKlixJqVKlstVinJyczIEDByz/fPr0acLDwylZsqRlnv9aMs7NzY2hQ4cyZswYSpUqhaurK88//zxBQUE0b94cgI4dO1K7dm0ef/xxZsyYQUxMDC+//DLDhg3DweHOy1zMnz+ftLQ0mjVrhrOzM99++y1OTk5Urlw5p79luUYFQgEy2o6XjchoO5616jDh0fHc/+EmHmlaiRc6qe1YRERERAqHEg62BFTyyLTeodls5mxC0j9alDOKhsdir3LpWgqhxy4SeuwiX24+DkClUs408S1F0yoeNK1SGt/SznrKUEQkH4wbN46BAwdSu3Ztrl+/TlRUFL6+vlk+/8yZM5l2EH7nnXd45513aNOmDevWrQOytmTczJkzMRqN9O7dG5PJRKdOnfjoo48s79vY2PD777/z7LPPEhQURIkSJRg4cKBl9+U7cXd3Z9q0aYwZM4a0tDTq1avH0qVLKV26dJa/Y14xmM1ms7WDKI4SExNxc3MjISEhW4/D5odziUmWtmPIuKv6Ymd/HmqstmMREZGCriDnGJJB16jgSEpJ48j5K0TEXGbf6QS2n4jjwJlE0v/1N6QyJR0yioW+pWhSpRT+3q7YKC8WkQIqKSmJqKgoqlSpgqOjnoi2lilTprB48eL/bJH+t+PHj1OlShV27dpFw4YNbzvmbtc4p3mGniCUW9xsO+7ftBKTf9vPoXOXGb9oLz+ERfO62o5FREREpIhwtLOhbgU36lZwo09gRSBjR+UdJy4RdjyObVFx7I5OIPaKieV7Y1i+NwYAF0dbGlf2oEmVUjT1LUW9im442N7bLp0iIlL07N27l5IlSzJjxgyee+65/xzfpUsXNmzYkA+R3UpPEFpJYblznJKWztehJ5gZEskVUyoGA2o7FhERKcAKS45RnOkaFS5JKWnsOZVA2PE4tkbFsfPEJa6YUjONcbA10tDHnWZVMp4wbFTJgxIOehZDRKxDTxAWDHFxccTFxQHg6emJm9t/P2x1+vRprl+/DkClSpWwt7993SUvniBUgdBKCltieD4xibf+iODXXaeBjLbj/+vsTz+1HYuIiBQohS3HKI50jQq31LR0ImIuszUqjrCoOMKOx3HxanKmMTZGA3XLu9LkRktyE99SlCqhm+sikj9UICz6VCAsQgprYrj12EUm3Wg7Bmjg485rPetQv6K7dQMTERERoPDmGMWJrlHRYjabOXrhKmHHMwqGW6PiOB1//ZZx1b1K0qRKKZpVKUXr6p54qGAoInlEBcKiTwXCIqQwJ4a3azvu37QSL3SsqURHRETEygpzjlFc6BoVfWfir1taksOi4jh8/kqm940GaFy5FO1reRFcy4uqniW1S7KI5JqbxSNfX1+cnJysHY7kgevXr1s2M1GBsJArConhv9uO3W/sdqy2YxEREespCjlGUadrVPzEXU22PGG46UgsETGXM71fubQz7f29CK5Vlia+pbC3NVopUhEpCtLS0oiMjMTLy4vSpUtbOxzJAxcvXuT8+fPUqFEDG5vMm2SpQFjIFKXEcFtUHJN+22dJdBpUdGNqz7o08HG3bmAiIiLFUFHKMYoqXSM5dekaayLOs+rgef46epHktHTLey4OtrSu6UkHfy/a1fRSh46I5MjZs2eJj4/Hy8sLZ2dnPaVcRJjNZq5du8b58+dxd3enXLlyt4xRgbCQKWqJYeo/2o4v32g7friJDy908teCzCIiIvmoqOUYRZGukfzTFVMqmw7HsvrgOdYeOk/slb83PDEaILCyBx1qlaWDvxfVvNSKLCJZYzabiYmJIT4+3tqhSB5wd3fH29v7tv9PUIGwkCmqieH5y0lMWx7Bon+0Hb/QqSYPN6mEjdqORURE8lxRzTGKEl0juZP0dDO7T8Wz+uB5Vh08d0srcqVSznSo5UUH/7I0raJWZBH5b2lpaaSkpFg7DMlFdnZ2t7QV/5MKhIVMUU8Mw47H8criv9uO699oO26otmMREZE8VdRzjKJA10iy6tSla6y90YocertW5BqedKjlRduaXuraERERQAXCQqc4JIapael8+9cJ3v3z77bjfo19+L/OajsWERHJK8UhxyjsdI0kJ66aUtl4OJY1EedYE3FrK3KjSjdakWt5UV2tyCIixZYKhIVMcUoML1w2Me2PCH7ZeQoAN6eMtuP+TdV2LCIiktuKU45RWOkayb262Yp8c6OTg2cTM73vU8qJjrW96dWwAnUruKpYKCJSjKhAWMgUx8Rw+/E4XvltvyWBqVfBjak96xBQycPKkYmIiBQdxTHHKGx0jSS3nY6/zpqD51gdcZ4tRy+SnPp3K3JVzxI8EFCBng0r4FPK2YpRiohIflCBsJApromhpe04JJLLSalAxm7HajsWERHJHcU1xyhMdI0kL91sRf59zxlCDpzD9I9iYRNfD3oFVKBbvXK4Oyv3FhEpilQgLGSKe2J44bKJ6Ssi+HnH323H4zrV5BG1HYuIiNyT4p5jFAa6RpJfLielsGJfDIvDT7Pl6EVu/s3PzsZAu5pePNioAm1reuFod+fdMEVEpHBRgbCQUWKYYceJOF5ZvJ8DN9qO61ZwZWrPujRS27GIiEiOKMco+HSNxBpiEpJYsvs0v+46k2nNQldHW7rVL0evhhVo4lsKo27Wi4gUajnNM4x5GJPIfwqsXIolw1vw6v11cHG0Zd/pRB78aAv/9/NuLl4xWTs8ERERyWeRkZH07NmTMmXK4OrqSsuWLVm7dm2mMSNGjCAwMBAHBwcaNmx423lWrlxJ8+bNcXFxwdPTk969e3P8+HHL+4MGDcJgMNzyqlOnzl3j+695RQoqbzdHnmpdlT9GtmLFqFY83cYPb1dHEpNS+WFbNP0+/YtWM9YyY0UEh89dtna4IiKSz1QgFKuztTEy8D5f1o5rS9/AigD8tP0U7d5Zxzehx0lL10OuIiIixUX37t1JTU1lzZo17NixgwYNGtC9e3diYmIyjRsyZAj9+vW77RxRUVH07NmT9u3bEx4ezsqVK4mNjeXBBx+0jJk9ezZnz561vKKjoylVqhR9+/a9Y2xZmVekMPD3dmVCl1psGd+e759sxkONK+LiYMvp+Ot8tO4o/5u5gW7vb+Tzjcc4n5hk7XBFRCQfqMXYStRacmc7TlzilcX7MrUdv3p/XQIrq+1YRETkvxTmHCM2NhZPT082bNhAq1atALh8+TKurq6EhIQQHBycafyUKVNYvHgx4eHhmY7//PPP9O/fH5PJhNGYcT986dKl9OzZE5PJhJ2d3S2fvXjxYh588EGioqKoXLnybePLyby3U5ivkRRdSSlprD54nl93nWbdofOk3rhJbzRAi2pl6NWwAp3qelPSwdbKkYqIyN2oxViKjMDKHix9viWv9ayD6422494fb+GFhbuJVduxiIhIkVW6dGlq1qzJ119/zdWrV0lNTeWTTz7By8uLwMDALM8TGBiI0Wjkyy+/JC0tjYSEBL755huCg4PvWMSbN28ewcHBdywO5nReAJPJRGJiYqaXSEHjaGdDt/rl+HxgY7ZNDOa1Xhk36NPNsPFwLGMX7qbx6yGM+GEXayPOk5KW/t+TiohIoaEnCK1Ed46zJvaKiRkrIvhpe8Zux66OtozrVJNHm1XWbsciIiK3UdhzjFOnTtGrVy927tyJ0WjEy8uLZcuWERAQcMvYOz1BCLB+/XoeeughLl68SFpaGkFBQSxfvhx3d/dbxp45c4ZKlSrx/fff89BDD901vuzM+884X3311VuOF9ZrJMXLiYtX+S38DIt3neZY7FXL8dIl7OnRoDx9G1ekTnk3K0YoIiL/pCcIpUgqU9KBGX0a8Muz91GnvCuJSalM+m0/98/ZxI4Tl6wdnoiIiGTB+PHjb7shyD9fERERmM1mhg0bhpeXFxs3bmTbtm306tWLHj16cPbs2Sx/XkxMDE8++SQDBw4kLCyM9evXY29vT58+fbjdvfGvvvoKd3d3evXqlavz3jRhwgQSEhIsr+jo6Cx/FxFrq1y6BCM6VGf12Db8NqwFg+7zpXQJey5eTWb+luN0e38TvT/ewm/hp0lO1VOFIiKFlZ4gtJLCfnffGtLSzXy/7SRvr4ggMSkVgD6BFRnfxZ8yJR2sHJ2IiEjBUBBzjAsXLnDx4sW7jvHz82Pjxo107NiRS5cuZYq9evXqDB06lPHjx2c6505PEL7yyiusWLGCsLAwy7FTp07h4+NDaGgozZs3txw3m83UqFGD7t27M3PmzLvGmJ1576YgXiOR7EhJS2fTkVh+3nGKlftiLOsVlinpQP+mPjzSrBLl3JysHKWISPGU0zxDK8xKoWFjNPB488p0revNjBWH+HF7dEZSsj+GcR1r8mizStja6KFYERGRgsbT0xNPT8//HHft2jUAywYgNxmNRtLTs/5k0rVr126Zw8bGBuCWedavX8+RI0cYOnRors4rUpTZ2RhpV9OLdjW9OJ+YxA/bovlu6wnOXzbxwZojfLTuKB1rl+XxoMoE+ZXGYNDSQCIiBZ2qKVLolC7pwPQ+9Vn03H3UreDK5aRUJi/ZT485m9l+PM7a4YmIiEgOBQUF4eHhwcCBA9m9ezeRkZG88MILREVF0a1bN8u4I0eOEB4eTkxMDNevXyc8PJzw8HCSk5MB6NatG2FhYUydOpXDhw+zc+dOBg8eTOXKlW9Zy3DevHk0a9aMunXr3hLPnDlz6NChg+Xn7MwrUlx4uToyMrg6m8e358NHGtGsSinS0s38sS+GRz7bSseZG/gm9DhXTKnWDlVERO4iSy3Ge/bsyfbEtWvXxtZWDyjeiVpLcsfNtuN3Vh4i4XoKAL0bZbQde7qo7VhERIqfwp5jbN++nYkTJ7J9+3ZSUlKoU6cOkyZNokuXLpYxbdu2Zf369becGxUVha+vLwALFixgxowZREZG4uzsTFBQENOnT8ff398yPiEhgXLlyjF79myefPLJW+abMmUK8+fP5/jx45ZjWZn3vxT2ayTyXw7FXObr0OP8uus015LTACjpYEvvRhV4PKgy1bxcrByhiEjRldM8I0sFQqPRiMFguOviy/8eHxkZiZ+fX5YDKW6UGOauuKvJzFgRwYKwjEW/XRxtGfu/GjzWvLLajkVEpFhRjlHw6RpJcZGYlMIvO07xTeiJTDsgt6hWmseb+xJcy0u5uohILsvzAuG2bduytHaM2Wymbt267NmzRwXCu1BimDd2nbzEpN/2s/d0AgD+3i681qsuTXxLWTkyERGR/KEco+DTNZLiJj3dzOajsXwdeoLVB89xY08Tyrs58mjzyvRr4qNNB0VEckmeFgjbtWvHr7/+iru7e5Ym7dq1K/PmzaNcuXJZDqS4UWKYd9LSzSwIO8mMFX+3HT/YqAITutRS27GIiBR5yjEKPl0jKc5OXbrGd1tP8mNYNHFXM9YNtbcx0rWeNwPu8yXAx12bmoiI3IM8LRBK7lNimPfiribz9sqMtmOzGVwcbBnTsQaPq+1YRESKMOUYBZ+ukQgkpaSxbM9Zvv7rBLuj4y3H61ZwZUCQL/c3KI+jnY31AhQRKaRUICxklBjmn/DoeCb9to89p/5uO57asy5Nq6jtWEREih7lGAWfrpFIZruj4/k69ARL95whOTUdAHdnOx5q7MNjzSpTqbSzlSMUESk88qVAGBISwqZNm2jTpg3t27dnw4YNvPXWW5hMJh5//HEGDx6co+CLIyWG+Sst3cyPYdHMWBlB/LUbbccBFRjf1R8vF0crRyciIpJ7lGMUfLpGIrcXdzWZH8Oi+favE5yOvw6AwQDtanrxVGs/mlUppfZjEZH/kOcFwm+//ZbBgwdTv359IiMj+eCDDxg9ejR9+vQhPT2db7/9lu+++44+ffrk+EsUJ0oMrePS1WRmrDzEgrCTlrbj0f+rwYAgtR2LiEjRoByj4NM1Erm7tHQzayPO81XocTYejrUcb1zZg2HtqtG2pqcKhSIid5DnBcKAgAAGDx7MiBEjWL16NT169OCNN95g9OjRALz77rv8+uuvbNq0KWffoJhRYmhdu2+0He9W27GIiBQxyjEKPl0jkaw7duEK8zZFsXDHKUv7cZ3yrgxrV41OdbyxMapQKCLyT3leICxZsiR79+6lSpUqANjb27N9+3bq168PQEREBC1btiQ2NvZu08gNSgytLz3dzI/bo5m+4u+24wcCKjChiz9ermo7FhGRwkk5RsGnaySSfecTk/hs4zG+23qSa8lpAFT1LMGzbavRs2F57NQNJCIC5DzPyPJ/Re3s7EhOTrb87ODgQMmSJTP9fP369Sx/cF7w9fXFYDBkek2bNi3TmD179tCqVSscHR3x8fFhxowZt8yzcOFC/P39cXR0pF69eixfvjzT+2azmUmTJlGuXDmcnJwIDg7m8OHDefrdJPcZjQb6N63E2rFteaRZJQwG+HXXadq/u555m6JITUu3dogiIiIiIgJ4uToysVttNr/YnhEdquPqaMvRC1cZt3A37d5Zxzd/nSApJc3aYYqIFFpZLhBWq1aNiIgIy8+nT5+2PE0IcPToUSpWrJi70eXA1KlTOXv2rOX1/PPPW95LTEykY8eOVK5cmR07dvD2228zZcoUPv30U8uYLVu20L9/f4YOHcquXbvo1asXvXr1Yt++fZYxM2bM4P3332fu3Lls3bqVEiVK0KlTJ5KSkvL1u0ru8Chhz5sP1OO3YS1oUNGNK6ZUXvv9AN3e38TWYxetHZ6IiIiIiNzgUcKeMf+rwebx7Xmxsz9lStpz6tJ1Xlm8j1Yz1vLphqNcNaVaO0wRkUInyy3Gv/76K6VLl6Z169a3fX/atGlcvXqV1157LVcDzA5fX19GjRrFqFGjbvv+xx9/zMSJE4mJicHe3h6A8ePHs3jxYkvxs1+/fly9epXff//dcl7z5s1p2LAhc+fOxWw2U758ecaOHcu4ceMASEhIoGzZssyfP5+HH344S7GqtaRgSk8389ONtuNLN9qOezUsz0tda6ntWERECgXlGAWfrpFI7klKSePHsGg+WX+UMwkZD2y4O9sx+L4qDLyvMu7O9laOUEQkf+X5GoSFga+vL0lJSaSkpFCpUiUeeeQRRo8eja2tLQADBgwgMTGRxYsXW85Zu3Yt7du3Jy4uDg8PDypVqsSYMWMyFRknT57M4sWL2b17N8eOHaNq1ars2rWLhg0bWsa0adOGhg0bMnv27NvGZjKZMJlMlp8TExPx8fFRYlhAxV9L5u2Vh/h+W8ZuxyUdbBkVXJ2B9/lqfRMRESnQVHwq+HSNRHJfcmo6i3ed5uP1R4mKvQpACXsbHguqzBMt/fB0cbByhCIi+SPP1yC8nWnTphEfH38vU+SqESNGsGDBAtauXcvTTz/Nm2++yf/93/9Z3o+JiaFs2bKZzrn5c0xMzF3H/PP9f553uzG389Zbb+Hm5mZ5+fj45PBbSn5wd7bnjZttxz7uXDGl8vqyg3R7fyN/qe1YRERERKRAsbc18lATH1aNacMH/QPw93bhanIan6w/Rsvpa5j02z5Ox1t3zXwRkYLsngqEb775JnFxcbkVy22NHz/+lo1H/v262R48ZswY2rZtS/369XnmmWd49913+eCDDzI9uWctEyZMICEhwfKKjo62dkiSBfUruvPrs/cxvXc9PJztiDx3hYc//YuRC3ZxLlFrToqIiIiIFCQ2RgM9GpTnj5GtmDewMQGV3DGlpvN16AnazFjLCwt3c+zCFWuHKSJS4Njey8n50Z08duxYBg0adNcxfn5+tz3erFkzUlNTOX78ODVr1sTb25tz585lGnPzZ29vb8uvtxvzz/dvHitXrlymMf9sOf43BwcHHBz0WHthZDQa6NekEp3qePPOn4f4butJfgs/w6oD5xgVXINBLdR2LCIiIiJSkBgMBjrUKkt7fy9Cj17kw3VH2HzkIgt3nOLnnafoWq8cw9pWo3Z5tfmLiMA9PkGYHzw9PfH397/r6+aGI/8WHh6O0WjEy8sLgKCgIDZs2EBKSoplTEhICDVr1sTDw8MyZvXq1ZnmCQkJISgoCIAqVarg7e2daUxiYiJbt261jJGiyd3Zntd71WPJsJY09HHnanIabyw/SNfZGwk9qrZjEREREZGCxmAwcF+1Mnz3RHMWPXcfwbW8MJth2Z6zdH1/I0Pmh7HjxCVrhykiYnX3tElJdHQ05cuXx8bGJjdjypHQ0FC2bt1Ku3btcHFxITQ0lNGjR9OlSxe++uorIGO34Zo1a9KxY0defPFF9u3bx5AhQ5g5cyZPPfUUAFu2bKFNmzZMmzaNbt26sWDBAt5880127txJ3bp1AZg+fTrTpk3jq6++okqVKrzyyivs2bOHAwcO4OiYtZ1utTh14ZaebubnHaeYtiKCuKvJANzfoDwTu9WirHY7FhERK1KOUfDpGolY18GziXy07ijL9pwh/cbfhoP8SjO8fTVaVCtj3eBERO6RVXYxvnLlCunp6ZmOWSvJ2blzJ8899xwRERGYTCaqVKnC448/zpgxYzK19u7Zs4dhw4YRFhZGmTJleP7553nxxRczzbVw4UJefvlljh8/TvXq1ZkxYwZdu3a1vG82m5k8eTKffvop8fHxtGzZko8++ogaNWpkOV4lhkVD/LVk3v0zkm+3nsBsztgpTW3HIiJiTcoxCj5dI5GCISr2KnPXHWXRrlOkpGX8tbhltTK82NmfehXdrBydiEjO5FuBMCoqiuHDh7Nu3TqSkv7epMFsNmMwGEhLS8vOdMWWEsOiZd/pBF75bR+7TsYDUN2rJK/2rMN9VXUHUkRE8pdyjIJP10ikYDkTf51P1h/lh23RJKdlPADTo0F5xnWsQeXSJawcnYhI9uRbgbBFixaYzWZGjhxJ2bJlMRgMmd5v06ZNdqYrtpQYFj3p6WZ+3nmK6X9EcPFG23GPBuWZ2LUW3m5qOxYRkfyhHKPg0zUSKZii464xMySSX8NPYzaDrdHAo80q8XyH6pQpqQ0nRaRwyLcCYcmSJdmxYwc1a9bMdpDyNyWGRVfCtRTeDTnEt3+dIP1G2/HI4OoMblFFbcciIpLnlGMUfLpGIgXbgTOJzFgZwbpDF4CMfP7J1n480cqPkg62Vo5OROTucppnZLta0aRJE6Kjo7N7mkix4eZsx9SedVkyvCWNKmXsdvzm8gi6zN7IliOx1g5PRERERETuonZ5V+YPbsr3TzajQUU3rianMWvVYdq+vZavQ4+TnJr+35OIiBQy2X6C8OjRozzzzDM89thj1K1bFzs7u0zv169fP1cDLKp057h4SE8388vOU0z7R9tx9/rlmNitFuXcnKwcnYiIFEXKMQo+XSORwsNsNrN8bwxvr4zg+MVrAFQu7cy4jjXpVq8cRqPhP2YQEclf+dZi/Ndff/HII49w/PjxvycxGLRJSTYpMSxeEq6n8N6fh/jmRtuxs70NIzpUZ0iLKtjbqu1YRERyj3KMgk/XSKTwSUlLZ0FYNLNXHSb2igmAehXcGN/FnxbVtDGhiBQc+VYgrF27NrVq1eL//u//brtJSeXKlbMzXbGlxLB42n8mgUm/7WfHiUsAVPUswdSedZVUiIhIrlGOUfDpGokUXldNqczbFMUn649yNTnj4ZjWNTx5sXNN6pR3s3J0IiL5WCAsUaIEu3fvplq1atkOUv6mxLD4Sk83s2jXad5aftDSdtytfjleVtuxiIjkAuUYBZ+ukUjhd/GKiQ/WHOG7rSdIScv4K3WvhuUZ27EmPqWcrRydiBRn+VYg7NGjB4MGDaJ3797ZDlL+psRQEq6nMDMkkq9Dj1vajp9vX52hLdV2LCIiOXcvOUapUqWyNd5gMLBz5051kGST8kCRouPkxWu8G3KI38LPAGBnY+Cx5pUZ3q4apUs6WDk6ESmO8q1A+Omnn/L6668zZMgQ6tWrd8smJffff392piu2lBjKTQfOJDLpt31sv9F27OdZgqn316VldbUdi4hI9t1LjmE0Gpk1axZubv/dJmc2m3nuuefYt28ffn5+OQ23WFIeKFL07DudwPQVEWw8HAtASQdbnm7tx9BWVXC2t7VydCJSnORbgdBovPOTTdqkJOuUGMo/mc1mFu08zVt/HCT2yo2243oZux2Xd1fbsYiIZN29FghjYmLw8vLK0ngXFxd2796tAmE2KQ8UKbo2HY5l+ooI9p5OAMDTxYGRHarTr4kPdjbqEhKRvJdvBULJHUoM5Xb+3XbsZJex27HajkVEJKuUYxR8ukYiRVt6uplle8/y9spDnIy7BoBfmRKM61STLnW9b9noU0QkN6lAWMgoMZS7OXAmkclL9hF2/O+241fvr0Or6p5WjkxERAo65RgFn66RSPGQnJrOgrCTzF512LI5YQMfd17q4k8zv9JWjk5EiiqrFwi3b9/OtWvXaN26dW5MV+QpMZT/Yjab+XXXad5cHkHsFRMAXep683L32lRQ27GIiNxBbuQYFy9eZM+ePTRo0IBSpUoRGxvLvHnzMJlM9O3bl1q1auVy1MWL8kCR4uWKKZXPNx7j0w3HuJacsSRXz4blealrLcq6Olo5OhEpaqxeIKxVqxaRkZFagzCLlBhKViUm3Ww7PkFauhknOxuGt6/GE62q4GBrY+3wRESkgLnXHGPbtm107NiRxMRE3N3dCQkJoW/fvtja2pKens6ZM2fYtGkTjRo1yoPoiwflgSLF04XLJmatiuT7bScxm6GEvQ2jgmswqIWv1icUkVyT0zwj1/4rtHr1ao4dO5Zb04nIDa6OdkzuUYffn29JE18Prqek8fbKQ3SZtZENkResHZ6IiBQxEydOpG/fviQkJPDSSy/Rq1cvOnToQGRkJEeOHOHhhx/mtddey7PPj4yMpGfPnpQpUwZXV1datmzJ2rVrM40ZMWIEgYGBODg40LBhw9vOs3LlSpo3b46Liwuenp707t2b48ePZxrz3Xff0aBBA5ydnSlXrhxDhgzh4sWLd43v5MmTdOvWDWdnZ7y8vHjhhRdITU29l68sIsWEp4sDbzxQj6XDWxJQyZ2ryWm8sfwgXWdvJPTo3f/bIyKS13KtQFi+fHkqV66cW9OJyL/UKufKT08HMbNfAzxdHDgWe5UBX2zjmW92cDr+urXDExGRImLHjh2MGTMGFxcXRo4cyZkzZ3jyySct7w8fPpywsLA8+/zu3buTmprKmjVr2LFjBw0aNKB79+7ExMRkGjdkyBD69et32zmioqLo2bMn7du3Jzw8nJUrVxIbG8uDDz5oGbN582YGDBjA0KFD2b9/PwsXLmTbtm2Zvuu/paWl0a1bN5KTk9myZQtfffUV8+fPZ9KkSbnz5UWkWKhbwY1fnrmPGX3qU6qEPYfPX6H/Z38x4oddnEtMsnZ4IlJMZanFODExMcsTqk0ia9RaIvficlIKs1YdZv6W46Slm3G0M/J8++pqOxYRkXvOMUqWLMm+ffvw9fUFwMXFhd27d+Pn5wdkPEFXs2ZNrl/P/ZtTsbGxeHp6smHDBlq1agXA5cuXcXV1JSQkhODg4Ezjp0yZwuLFiwkPD890/Oeff6Z///6YTCaMxoz74UuXLqVnz56YTCbs7Ox45513+Pjjjzl69KjlvA8++IDp06dz6tSp28b3xx9/0L17d86cOUPZsmUBmDt3Li+++CIXLlzA3t4+S99TeaCI3JRwLYV3Qw7x7V8nSL/RdjwyuDqDW1RR27GI5Eiethi7u7vj4eFx19fNMSKS91wc7Xile22WjWhJ0yqlSEpJ5+2Vh+g8ayPr1XYsIiL3wMfHJ9OyMQsWLKBcuXKWn8+ePUuZMmXy5LNLly5NzZo1+frrr7l69Sqpqal88skneHl5ERgYmOV5AgMDMRqNfPnll6SlpZGQkMA333xDcHAwdnZ2AAQFBREdHc3y5csxm82cO3eOn3/+ma5du95x3tDQUOrVq2cpDgJ06tSJxMRE9u/ff8fzTCYTiYmJmV4iIgBuznZM7VmXJcNb0uhG2/GbyyPoOnsjW47GWjs8ESlGbLMy6N/rvohIweDv7cqPTzXnt/AzvLH8IFGxVxn4xTY61/HmlR7a7VhERLLv4Ycf5vz585afu3Xrlun9JUuW0LRp0zz5bIPBwKpVq+jVqxcuLi4YjUa8vLxYsWJFtm5EV6lShT///JOHHnqIp59+mrS0NIKCgli+fLllTIsWLfjuu+/o168fSUlJpKam0qNHDz788MM7zhsTE5OpOAhYfv53C/Q/vfXWW7z66qtZjl9Eip+6Fdz4+Zn7+HnnKab/EcHh81d45LOt9GhQnolda+Htpt2ORSRv5douxpI9ai2R3Ha7tuPh7arxZGs/tR2LiBQjeZ1jXLt2DRsbGxwcHLJ8zvjx45k+ffpdxxw8eJCaNWvSq1cvUlJSmDhxIk5OTnz++ecsWbKEsLCwTE8ywp1bjGNiYmjdujW9evWif//+XL58mUmTJmFra0tISAgGg4EDBw4QHBzM6NGj6dSpE2fPnuWFF16gSZMmzJs377YxPvXUU5w4cYKVK1dm+v0oUaIEy5cvp0uXLrc9z2QyYTKZLD8nJibi4+OjPFBEbut2bccjOmS0Hdvbqu1YRO4up7lgjgqE8fHxzJs3j4MHDwJQp04dhgwZgpubW3anKrZUIJS8cijmMpN+28fWqDgAfEs7M+X+OrSt6WXlyEREJD/kRY6xefNmGjdunK2i4D9duHDhP3cH9vPzY+PGjXTs2JFLly5lir169eoMHTqU8ePHZzrnTgXCV155hRUrVmTaTOXUqVP4+PgQGhpK8+bNefzxx0lKSmLhwoWWMZs2baJVq1acOXPmlmIkwKRJk1iyZEmmz4uKisLPz4+dO3cSEBCQld8O5YEikiX7zyQw6bf97DhxCYCqniWY2rMuLarlzTIPIlI05OkahP+0fft2qlatysyZM4mLiyMuLo733nuPqlWrsnPnzuxOJyK5rKa3Cwueas7shxvi5eLA8YvXGPRlGE99vZ3ouGvWDk9ERAqhLl26cPr06Ryf7+npib+//11f9vb2XLuW8f+pmxuL3GQ0GklPT8/y5127du2WOWxsMp6mvznP3cbc6f55UFAQe/fuzdSCHRISgqurK7Vr185yfCIiWVGnvBsLnw7i7T71KV3CnqMXrvLo51sZ9v1Ozibk/kZRIlK8ZbtAOHr0aO6//36OHz/OokWLWLRoEVFRUXTv3p1Ro0blQYgikl0Gg4GeDSuwemwbnmhZBRujgT8PnON/M9fzwerDJKWkWTtEEREpRPJrRZqgoCA8PDwYOHAgu3fvJjIykhdeeIGoqKhMayEeOXKE8PBwYmJiuH79OuHh4YSHh5OcnAxkrJsYFhbG1KlTOXz4MDt37mTw4MFUrlzZ8pRfjx49WLRoER9//DHHjh1j8+bNjBgxgqZNm1K+fHkAfv31V/z9/S2f27FjR2rXrs3jjz/O7t27WblyJS+//DLDhg3L8dOVIiJ3YzQa6NvYhzXj2jIwqDJGAyzbc5YO765n7vqjJKdm/eaJiMjdZLvF2MnJiV27dmVKlgAOHDhA48aNLXd+5e7UWiL5KfJcRtvxX8f+bjuefH8d2qntWESkyMmLHMPFxYXdu3fj5+eXK/Pdzfbt25k4cSLbt28nJSWFOnXqMGnSpEzr+7Vt25b169ffcm5UVBS+vr5Axu7LM2bMIDIyEmdnZ4KCgpg+fXqmHPaDDz5g7ty5REVF4e7uTvv27Zk+fToVKlQAYP78+QwePDhTgfTEiRM8++yzrFu3jhIlSjBw4ECmTZuGrW2W9v4DlAeKSM6p7VhE/ku+rUFYtmxZvvnmGzp27Jjp+MqVKxkwYADnzp3LznTFlhJDyW9ms5mle87y+u8HOH85Y6H0/9Uuy6TutfEp5Wzl6EREJLfkRY7x/fff07NnT0qUKJEr8xV3ygNF5F6kp5tZtOs0by0/yMWrN56crleOl7vXopybk5WjExFry7c1CPv168fQoUP58ccfiY6OJjo6mgULFvDEE0/Qv3//7E4nIvnEYDBwf4PyrBnXlqda+2FrNBBy4BzB763nfbUdi4jIXTzyyCMqDoqIFBBGo4E+gRVZM64tg+7zzWg73pvRdvzxOrUdi0jOZPsJwuTkZF544QXmzp1LamoqAHZ2djz77LNMmzZN669kke4ci7UdPneZSb/tJ/RYxq6SlUs7M6VHHdr5q+1YRKQwy60cIykpiQ8++IC1a9dy/vz5WzYJ0eZ0Oac8UERy0/4zCUz+bT/bb7Qd+3mW4NX769CquqeVIxMRa8i3FuObrl27xtGjRwGoWrUqzs5qUcwOJYZSENxsO35j2QHOJWa0HQfXKsvkHmo7FhEprHIrx3j00Uf5888/6dOnD2XLlsVgMGR6f/LkyfcaarGlPFBEcpvZbGbRztO89cdBYq9ktB3f36A8U+6vQ6kS9laOTkTyU74XCOXeKDGUguSKKZUPVh9m3qYoUtPNONgaea5tNZ5u44ejnY21wxMRkWzIrRzDzc2N5cuX06JFi1yMTkB5oIjknYTrKcwMieTr0OOkm6F0CXum9qxLt/rlrB2aiOSTfCsQqt0kdygxlILo8LnLTF6yny1HM9qOK5VyZsr9tWnvX9bKkYmISFblVo5Ru3ZtFixYQP369XMxOgHlgSKS93ZHx/PCz7uJPHcFgC51vZnasy6eLloSTKSoy7cCodpNcocSQymozGYzv+85y+uZ2o69mNyjjtqORUQKgdzKMf744w/ef/995s6dS+XKlXMxQlEeKCL5wZSaxpw1R/ho3VHS0s24O9sxpUcdejYsf8vf40Wk6Mi3AqHaTXKHEkMp6G7Xdvxs26o806aq2o5FRAqw3MoxLly4wEMPPcSGDRtwdnbGzs4u0/txcXH3GmqxpTxQRPLTvtMJ/N/PezhwNhHIuPn/xgP1KOvqaOXIRCQv5DTPsM3uB1WoUAEXF5fsniYihUxJB1smdK1F38YVmbxkP5uPXGTWqsP8svMUU3rUoUMttR2LiBRl/fv35/Tp07z55pu37RoREZHCoW4FN34b3oKP1x3lgzWHWXXwPNui1vNK99r0Cayo/76LCJCDJwjVbpI7dOdYChOz2czyvTG89vsBYhKTAOjgn9F2XKm02o5FRAqS3MoxnJ2dCQ0NpUGDBrkYnYDyQBGxnkMxl3nh593sOZUAQJsanrz1YD3KuztZOTIRyS05zTOM2f2gxo0bk5SUhJ+fHy4uLpQqVSrTS0SKHoPBQLf65Vg9tg3PtKmKrdHA6ojzBM9cz8yQSJJS0qwdooiI5DJ/f3+uX79u7TBERCQX1fR2YdGz9/FiZ3/sbY2sj7xAx5kb+H7rSbL57JCIFDHZfoIwODiYkydPMnTo0Nu2mwwcODBXAyyqdOdYCrMj568wZcl+Nh2JBcCnlBOTu9chuLbajkVErC23cow///yTV199lTfeeIN69erdsgah8pecUx4oIgXBkfNX+L+fd7PzZDwA91UtzfTe9bUxoUghl2+blKjdJHcoMZTCzmw288e+jLbjswkZbcft/b2Y3KM2lUuXsHJ0IiLFV27lGEZjRqPJv28Gm81mDAYDaWl6ejynlAeKSEGRlm7my81RvPPnIZJS0nG2t+HFzv483rwyRqPWJhQpjPJtkxK1m4gIZPyFsWu9crSp4cmctUf4fOMx1kScZ9ORWJ5pU5Xn2mq3YxGRwmzt2rXWDkFERPKYjdHAE638CK5Vlv/7ZQ/bouKYvGQ/y/acZXqf+lQpoxv/IsVFtp8gVLtJ7tCdYylqjl7IaDveeDij7biihxOTutfmf7W186WISH5SjlHw6RqJSEGUnm7m260nmPZHBNeS03C0MzKuY00Gt6iCjZ4mFCk08m2Tks6dOxMaGkqHDh3w8vLCw8MDDw8P3N3d8fDwyO50WfbGG29w33334ezsjLu7+23HnDx5km7duuHs7IyXlxcvvPACqampmcasW7eORo0a4eDgQLVq1Zg/f/4t83z44Yf4+vri6OhIs2bN2LZtW6b3k5KSGDZsGKVLl6ZkyZL07t2bc+fO5dZXFSmUqnqW5OshTfno0UaUd3Pk1KXrPPXNDobMD+N47FVrhyciIiIiIndhNBoYEOTLylGtaVGtNEkp6by+7CB9527hyPkr1g5PRPJYtluMrdVukpycTN++fQkKCmLevHm3vJ+Wlka3bt3w9vZmy5YtnD17lgEDBmBnZ8ebb74JQFRUFN26deOZZ57hu+++Y/Xq1TzxxBOUK1eOTp06AfDjjz8yZswY5s6dS7NmzZg1axadOnXi0KFDeHl5ATB69GiWLVvGwoULcXNzY/jw4Tz44INs3rw5/35DRAqgm23HbWt6MmfNET7beIy1hy6w+cgGnmnjx7Ntq+Fkr7ZjEZHCrFatWkRGRmoNQhGRIsqnlDPfDm3GgrBo3lh2kJ0n4+n6/kZGBVfnqVZ+2Npk+zkjESkEst1ibG3z589n1KhRxMfHZzr+xx9/0L17d86cOUPZshk7qc6dO5cXX3yRCxcuYG9vz4svvsiyZcvYt2+f5byHH36Y+Ph4VqxYAUCzZs1o0qQJc+bMASA9PR0fHx+ef/55xo8fT0JCAp6ennz//ff06dMHgIiICGrVqkVoaCjNmzfP0vdQa4kUB/9uO67g7sTkHmo7FhHJS3mdYyxevJiEhAQGDhyY63MXF8oDRaSwOBN/nQmL9rI+8gIA9Su6MaNPffy99d8ukYIqT1uM9+zZQ3p6epYn3b9//y2tvXktNDSUevXqWYqDAJ06dSIxMZH9+/dbxgQHB2c6r1OnToSGhgIZTynu2LEj0xij0UhwcLBlzI4dO0hJSck0xt/fn0qVKlnG3I7JZCIxMTHTS6Sou9l2PPexjLbj0/EZbceD1XYsIlJo9erVS8VBEZFiory7E/MHN+Gdvg1wdbRlz6kEenywidmrDpOSlvUagYgUfFkqEAYEBHDx4sUsTxoUFMTJkydzHFROxMTEZCoOApafY2Ji7jomMTGR69evExsbS1pa2m3H/HMOe3v7W9ZB/OeY23nrrbdwc3OzvHx8fHL0PUUKG4PBQOe65Vg1tg3D2lXF3sbIukMX6DhzA+/+eYjryWpRExEREREpqAwGA30CKxIypg3BtcqSkmZm5qpI7p+zmX2nE6wdnojkkiytQWg2m3nllVdwdnbO0qTJyclZGjd+/HimT59+1zEHDx7E398/S/MVZBMmTGDMmDGWnxMTE1UklGLF2d6WFzr507tRRaYsPcCGyAt8sOYIi3aeZlKP2nRU27GIiNUFBARk+b/FO3fuzONoRESkICnr6shnAwJZsvsMU5bs5+DZRHp9uJlRwdV5tm017XQsUshlqUDYunVrDh06lOVJg4KCcHJy+s9xY8eOZdCgQXcd4+fnl6XP9Pb2vmW34Zs7C3t7e1t+/fduw+fOncPV1RUnJydsbGywsbG57Zh/zpGcnEx8fHympwj/OeZ2HBwccHBwyNJ3ESnK/DxL8tXgJqzcf47Xfj/A6fjrPP3NDtrU8GTK/XWoUqaEtUMUESm2evXqZe0QRESkADMYDPRsWIH7qpZh8pJ9LN8bwzt/RrLxcCwz+zWkvPt/1wFEpGDKUoFw3bp1efLhnp6eeHp65spcQUFBvPHGG5w/f96y23BISAiurq7Url3bMmb58uWZzgsJCSEoKAgAe3t7AgMDWb16tSVBTk9PZ/Xq1QwfPhyAwMBA7OzsWL16Nb179wbg0KFDnDx50jKPiNxdRtuxN21qePLh2iN8uuEY6yMv0GnmBp5sXYVh7arhbJ/tTdZFROQeTZ482dohiIhIIeDp4sCHjzTK6Ab6bR9bo+LoMnsj03vXo3PdctYOT0RyoNDsYnzy5Eni4uJYsmQJb7/9Nhs3bgSgWrVqlCxZkrS0NBo2bEj58uWZMWMGMTExPP744zzxxBO8+eabAERFRVG3bl2GDRvGkCFDWLNmDSNGjGDZsmV06tQJgB9//JGBAwfyySef0LRpU2bNmsVPP/1ERESEZW3CZ599luXLlzN//nxcXV15/vnnAdiyZUuWv492rxP5W1TsVaYs2W/ZHa2CuxOvdK9FpzreajsWEcmm3M4xduzYwcGDBwGoU6cOAQEB9zxncac8UESKkuOxVxmxYBd7TmWsR9i/qQ+vdK+tG/4iVpLTPKPQFAgHDRrEV199dcvxtWvX0rZtWwBOnDjBs88+y7p16yhRogQDBw5k2rRp2Nr+/R+mdevWMXr0aA4cOEDFihV55ZVXbmlznjNnDm+//TYxMTE0bNiQ999/n2bNmlneT0pKYuzYsfzwww+YTCY6derERx99dNcW439TYiiSmdls5s8D55i6NKPtGKB1DU+m9KiNn2dJK0cnIlJ45FaOcf78eR5++GHWrVtnWVYlPj6edu3asWDBglzrAimOlAeKSFGTnJrOzFWRzF1/FLMZ/DxL8P7DAdSt4Gbt0ESKnSJfICxqlBiK3N715DQ+WneET9YfIzktHXsbo9qORUSyIbdyjH79+nHs2DG+/vpratWqBcCBAwcYOHAg1apV44cffsitkIsd5YEiUlRtORLL6J/COZdowt7GyP91rsmQFlUwagMTkXyjAmEho8RQ5O6iYq/y6tL9rDuU0XZc3s2RV7rXpnNdtR2LiNxNbuUYbm5urFq1iiZNmmQ6vm3bNjp27Eh8fPw9Rlp8KQ8UkaLs0tVkXvxlD38eyNj8s3UNT97pWx8vF0crRyZSPOQ0zzDmYUwiIjlWpUwJvhzUhE8fD6SCuxNnEpJ49rudDPhiG8cuXLF2eCIiRV56ejp2dna3HLezsyM9Pd0KEYmISGHgUcKeTx4P5I0H6uJoZ2RD5AW6zNrI2ojz1g5NRO4iR08QHj58mLVr13L+/PlbEsRJkyblWnBFme4ci2Td9eQ0Pl53hLkbjpGcmo6djYEnW/kxvL3ajkVE/i23coyePXsSHx/PDz/8QPny5QE4ffo0jz76KB4eHvz666+5FXKxozxQRIqLw+cu8/wPu4iIuQzAoPt8Gd/FH0c7GytHJlJ05VuL8Weffcazzz5LmTJl8PbO3OpnMBjYuXNndqYrtpQYimTf8Rttx2vVdiwicke5lWNER0dz//33s3//fnx8fCzH6taty5IlS6hYsWJuhVzsKA8UkeIkKSWNGSsO8cXmKAD8vV14v38ANcq6WDkykaIp3wqElStX5rnnnuPFF1/MdpDyNyWGIjljNptZdfA8ry7dz6lLGbsdt6pehin316GqdjsWEcnVHMNsNrNq1SoiIiIAqFWrFsHBwbkRZrGmPFBEiqO1h87zwsLdxF5JxsHWyMvda/NYs0q60S+Sy/KtQOjq6kp4eDh+fn7ZDlL+psRQ5N4kpaTx0bqjzF1/1NJ2PLSlH8+3r0YJB7Udi0jxpRyj4NM1EpHi6sJlE+MW7mZ9ZEZHUHCtsszoU59SJeytHJlI0ZFvBcKhQ4fSpEkTnnnmmWwHKX9TYiiSO05cvMqrSw+w5saix+XcHHm5W2261lPbsYgUT7mZY4SFhd1x3en33nvvnuYuzpQHikhxlp5u5sstx5n+RwTJael4uTgws19DWlQrY+3QRIqEnOYZ2X7Mplq1arzyyiv89ddf1KtX75bd7UaMGJHdKUVEcqxy6RJ8MagJqw6cY8qNtuNh3++kZbWMtuNqXmo7FhHJiTfffJOXX36ZmjVrUrZs2VvWnRYREckJo9HA0JZVaO5XihE/7OLohas8Nm8rT7euypj/1cDe1mjtEEWKpWw/QVilSpU7T2YwcOzYsXsOqjjQnWOR3JeUksbH647y8T/ajoe0rMKI9tXVdiwixUZu5Rhly5Zl+vTpDBo0KPeCy4LIyEheeOEFNm/eTHJyMvXr1+e1116jXbt2ljEjRoxg8+bN7Nu3j1q1ahEeHn7LPCtXrmTy5Mns378fR0dHWrduzbvvvouvr69lzHfffceMGTM4fPgwbm5udOnShbfffpvSpUvfNrbdu3czbdo0Nm3aRGxsLL6+vjzzzDOMHDkyW99ReaCISIbryWm8tuwA3289CUD9im7MfjiAKmVKWDkykcIrp3lGtkvzUVFRd3ypOCgi1uRoZ8Po/9UgZHRrOvh7kZJm5pP1x+jw7np+33OGbN4PEREp1oxGIy1atMj3z+3evTupqamsWbOGHTt20KBBA7p3705MTEymcUOGDKFfv363nSMqKoqePXvSvn17wsPDWblyJbGxsTz44IOWMZs3b2bAgAEMHTqU/fv3s3DhQrZt28aTTz55x9h27NiBl5cX3377Lfv372fixIlMmDCBOXPm5M6XFxEpZpzsbXjzgXrMfSwQd2c79pxKoNv7G1m4PVq5u0g+y/YThP9081S1mWSf7hyL5L3VBzPajqPjMnY7blGtNK/eX4dqXi5WjkxEJO/kVo4xY8YMzpw5w6xZs3IvuP8QGxuLp6cnGzZsoFWrVgBcvnwZV1dXQkJCbtlBecqUKSxevPiWJwh//vln+vfvj8lkwmjMuB++dOlSevbsiclkws7OjnfeeYePP/6Yo0ePWs774IMPmD59OqdOncpyzMOGDePgwYOsWbMmy+coDxQRudXZhOuM/jGcv47FAdC9fjneeKAebk52/3GmiPxTvj1BCPD1119Tr149nJyccHJyon79+nzzzTc5mUpEJM90qFWWkNFtGBVcHQdbI5uPXKTzrI289cdBrppSrR2eiEiBNm7cOA4dOkTVqlXp0aMHDz74YKZXXihdujQ1a9bk66+/5urVq6SmpvLJJ5/g5eVFYGBglucJDAzEaDTy5ZdfkpaWRkJCAt988w3BwcGW9bODgoKIjo5m+fLlmM1mzp07x88//0zXrl2zFXNCQgKlSpW66xiTyURiYmKml4iIZFbOzYnvnmjO/3Wuia3RwO97ztJ19kbCjsdZOzSRYiHbBcL33nuPZ599lq5du/LTTz/x008/0blzZ5555hlmzpyZFzGKiOSYo50No4JrEDK6DcG1vEhN/7vteOlutR2LiNzJiBEjWLt2LTVq1KB06dK4ublleuUFg8HAqlWr2LVrFy4uLjg6OvLee++xYsUKPDw8sjxPlSpV+PPPP3nppZdwcHDA3d2dU6dO8dNPP1nGtGjRgu+++45+/fphb2+Pt7c3bm5ufPjhh1n+nC1btvDjjz/y1FNP3XXcW2+9len3zsfHJ8ufISJSnNgYDTzXtho/P3sflUs7czr+Ov0+CWVmSCRp6crbRfJSjjYpefXVVxkwYECm41999RVTpkwhKioqVwMsqtRaImIdayLOMWXJAU7GXQPgvqoZbcfVy6rtWESKhtzKMVxcXFiwYAHdunW755jGjx/P9OnT7zrm4MGD1KxZk169epGSksLEiRNxcnLi888/Z8mSJYSFhVGuXLlM59ypxTgmJobWrVvTq1cv+vfvz+XLl5k0aRK2traEhIRgMBg4cOAAwcHBjB49mk6dOnH27FleeOEFmjRpwrx58/7zO+3bt4927doxcuRIXn755buONZlMmEwmy8+JiYn4+PgoDxQRuYsrplQm/baPRTtPA9CqehlmPxxAqRL2Vo5MpGDLaS6Y7QKho6Mj+/bto1q1apmOHz58mHr16pGUlJSd6YotFQhFrCcpJY1P1h/jo3VHMKWmY2u8sdtxh+qU1G7HIlLI5VaOUblyZVauXIm/v/89x3ThwgUuXrx41zF+fn5s3LiRjh07cunSpUyxV69enaFDhzJ+/PhM59ypQPjKK6+wYsUKwsLCLMdOnTqFj48PoaGhNG/enMcff5ykpCQWLlxoGbNp0yZatWrFmTNnbilG/tOBAwdo164dTzzxBG+88UZWfgsyUR4oIpJ1v+46xUuL9nE9JY0K7k589GgjGvi4WzsskQIr39YgrFatWqb2jJt+/PFHqlevnt3pRETynaOdDSODq7NqTBuCa5UlNd3MpxuO0eHddSxR27GICJBRfJs8eTLXrl2757k8PT3x9/e/68ve3t7yWTc3FrnJaDSSnp6e5c+7du3aLXPY2NgAWOa525i7/X9g//79tGvXjoEDB+aoOCgiItnzQEBFfh12H743Wo77zg3l+60nlbOL5LJsP0H4yy+/0K9fP4KDg2nRogUAmzdvZvXq1fz000888MADeRJoUaM7xyIFx7/bjoP8SvNqzzrUUNuxiBRCuZVjBAQEcPToUcxmM76+vpbNPW7auXPnvYZ6i9jYWPz9/WnTpg2TJk3CycmJzz77jNmzZxMWFkaDBg0AOHLkCFeuXGHu3LmsXbuWH3/8EYDatWtjb2/PmjVrCA4OZsqUKZYW45deeomIiAgOHjyIk5MT8+fP58knn+T999+3tBiPGjUKo9HI1q1bAfj111+ZMGECERERQEZbcfv27enUqRNvv/22JW4bGxs8PT2z/D2VB4qIZF9iUgpjf9pNyIFzAPQNrMhrveriaGdj5chECpZ8azEG2LFjBzNnzuTgwYMA1KpVi7FjxxIQEJDdqYotJYYiBUtSShqfbjjGh2v/bjse3MKXkcE11HYsIoVKbuUYr7766l3fnzx5co7nvpvt27czceJEtm/fTkpKCnXq1GHSpEl06dLFMqZt27asX7/+lnOjoqLw9fUFYMGCBcyYMYPIyEicnZ0JCgpi+vTpmVqmP/jgA+bOnUtUVBTu7u60b9+e6dOnU6FCBQDmz5/P4MGDLU+pTJky5ba/L5UrV+b48eNZ/o7KA0VEciY93czcDUd5Z+Uh0s1Qp7wrHz8aSKXSztYOTaTAyNcCodw7JYYiBVN03DVe+/0Af964M1nW1YGXutbi/gblMRgMVo5OROS/Kcco+HSNRETuzeYjsYz4YRcXrybj5mTHrH4NaefvZe2wRAqEPF2DMDExMdM/3+0lIlKY+ZRy5tMBjflycBMql3bmXKKJkQvC6f/ZX0Seu2zt8EREREREir0W1cqw9PmWNPRxJ+F6CkO+CuO9kEjS0vX8k0hOZalA6OHhwfnz5wFwd3fHw8PjltfN4yIiRUG7ml6sHNWasf+rgaOdkb+OxdF19kZe//0Al5NSrB2eiEieKFWqFLGxsVkeX6lSJU6cOJGHEYmIiNxeeXcnfny6OY83r4zZDO+vPsyQ+WFcupps7dBECqUsLay1Zs0aSpUqBcDatWvzNCARkYLC0c6G5ztUp1dABUvb8eeboliy+wwTu6ntWESKnvj4eP744w/c3NyyNP7ixYukpaXlcVQiIiK352Brw2u96hJQyZ2Xft3L+sgLdP9gE3MfC6Rexaz9v0xEMmR7DcKTJ0/i4+Nzy1+KzWYz0dHRVKpUKVcDLKq09oxI4bPu0HmmLNnP8YsZux03q1KKqT3rUtNbux2LSMFxLzmG0Zil5pJMjhw5gp+fX7bPK86UB4qI5L6DZxN55tsdnLh4DXtbI6/1rEO/JqpPSPGTb5uU2NjYcPbsWby8Mi8AevHiRby8vHQXOYuUGIoUTqbUND7bcIw5a4+QlJKOjdHAoPt8GRVcHRdHO2uHJyKiHKMQ0DUSEckbCddTGPtTOKsOZiyR1q+xD6/2rIOjnY2VIxPJP3m6Sck/mc3m27bUXblyBUdHx+xOJyJSqDjY2jC8fXVWjWlDpzplSUs3M29TFO3fXc/iXafRxvAiIiIiItbh5mTHp4835oVONTEa4Mft0fSdG0p03DVrhyZS4GX5CcIxY8YAMHv2bJ588kmcnZ0t76WlpbF161ZsbGzYvHlz3kRaxOjOsUjRsD7yAlOW7Ccq9ioATauUYmrPOvh7699rEbEO5RgFn66RiEje23Q4lhELdhF3NRl3Zztm9WtI25pe/32iSCGX5y3G7dq1A2D9+vUEBQVhb29vec/e3h5fX1/GjRtH9erVsxl68aTEUKToMKWm8fnGKD5Yc9jSdjwwyJdR/6uOq9qORSSfKcco+HSNRETyx+n46zz37Q52n0rAYIBRHWrwfPtqGI3aaFCKrnxbg3Dw4MHMnj1bycw9UmIoUvScjr/O678f4I99MQCUKenAxG7+9GpYQbsdi0i+UY5R8OkaiYjkH1NqGq8uPcD3W08C0K6mJ7P6BeDmrBv5UjTlW4FQcocSQ5Gia8ONtuNjN9uOfUvxas861Cqnf9dFJO8pxyj4dI1ERPLfzztOMfHXvZhS0/Ep5cTHjwZSt4KbtcMSyXX5WiDcvn07P/30EydPniQ5OTnTe4sWLcrudMWSEkORou1m2/GcNUe4npKGjdHAgKDKjP5fDbUdi0ieys0cIz09nSNHjnD+/HnS09Mzvde6det7mrs4Ux4oImId+88k8Oy3OzkZdw0HWyOv96pL38Y+1g5LJFfl2y7GCxYs4L777uPgwYP8+uuvpKSksH//ftasWYObm6rvIiKQsdvxsHbVWDW2DV3reZOWbubLzcdp/856Fu08pd2ORaTA++uvv6hWrRq1atWidevWtG3b1vK6uTa1iIhIYVKnvBtLh7ekvb8XptR0Xvh5DxMW7cWUmmbt0ESsLtsFwjfffJOZM2eydOlS7O3tmT17NhERETz00ENUqlQpL2IUESm0Krg78dGjgXwztCl+ZUoQe8XEmJ9289AnoRw4k2jt8ERE7uiZZ56hcePG7Nu3j7i4OC5dumR5xcXFWTs8ERGRHHFztuPzAY0Z+78aGAzww7aTPDQ3lNPx160dmohVZbvFuESJEuzfvx9fX19Kly7NunXrqFevHgcPHqR9+/acPXs2r2ItUtRaIlL8mFLTmLcpig9WZ7QdGw0wIMiX0f+rgZuT2o5FJHfkVo5RokQJdu/eTbVq1XIxOgHlgSIiBcX6yAuMXLCL+GspeDjb8UH/RrSsXsbaYYnck3xrMfbw8ODy5csAVKhQgX379gEQHx/PtWvXsjudiEix4WBrw3Ntq7F6bBu61StHuhnmbzlOh3fX8csOtR2LSMHSrFkzjhw5Yu0wRERE8kybGp78/nxL6ld049K1FAZ+uY2vQ49bOywRq7DN7gmtW7cmJCSEevXq0bdvX0aOHMmaNWsICQmhQ4cOeRGjiEiRUt7diQ8fbUT/w7FMWrKPYxeuMnbhbn7YdpKpPetSu7yeJhER63v++ecZO3YsMTEx1KtXDzu7zE86169f30qRiYiI5J6KHs789HQQE3/dxy87TzHpt/0cPneFyT1qY2uT7WeqRAqtbLcYx8XFkZSURPny5UlPT2fGjBls2bKF6tWr8/LLL+Ph4ZFXsRYpai0REYDk1HS+2BzF+6sPcy1Zbccicu9yK8cwGm/9S5HBYMBsNmMwGEhL04LuOaU8UESk4DGbzXyy4RjTV0RgNkOr6mWY80gj5eRS6OQ0z8hWgTA1NZXvv/+eTp06UbZs2RwFKhmUGIrIP51NuM7ryw6ybE/GOq5lStozvkstHgyogNFosHJ0IlKY5FaOceLEibu+X7ly5RzPXdwpDxQRKbhW7o9h1IJwrqekUdWzBPMGNsG3TAlrhyWSZflSIARwdnbm4MGDSgrvkRJDEbmdTYdjmbxkH0cvXAUgsLIHU3vWoU55NytHJiKFhXKMgk/XSESkYNt/JoEnvtrO2YQk3J3tmPtYIM39Sls7LJEsybdNSpo2bUp4eHh2TxMRkSxoWb0Mf4xszYQu/jjb27DjxCV6fLCJyb/tI+F6irXDE5Fi5ujRozz//PMEBwcTHBzMiBEjOHr0qLXDEhERyVN1yrvx27AWNPBxJ/5aCo/P28qPYSetHZZInsp2gfC5555jzJgxzJkzh9DQUPbs2ZPplVfeeOMN7rvvPpydnXF3d7/tGIPBcMtrwYIFmcasW7eORo0a4eDgQLVq1Zg/f/4t83z44Yf4+vri6OhIs2bN2LZtW6b3k5KSGDZsGKVLl6ZkyZL07t2bc+fO5dZXFZFizt7WyNNtqrJ6bBu618/Y7fir0BO0f2cdP22PJj1dux2LSN5buXIltWvXZtu2bdSvX5/69euzdetW6tSpQ0hIiLXDExERyVNero78+FRzejQoT0qamRd/2csbyw6Qplxciqhstxhba8HqyZMn4+7uzqlTp5g3bx7x8fG3jePLL7+kc+fOlmPu7u44OjoCEBUVRd26dXnmmWd44oknWL16NaNGjWLZsmV06tQJgB9//JEBAwYwd+5cmjVrxqxZs1i4cCGHDh3Cy8sLgGeffZZly5Yxf/583NzcGD58OEajkc2bN2f5+6i1RESyavORWCYv2c+R81cAaFTJnak961K3gtqOReRWuZVjBAQE0KlTJ6ZNm5bp+Pjx4/nzzz/ZuXPnvYZabCkPFBEpPMxmM7NXH2bWqsMAdPD3Ynb/AEo62Fo5MpHby7c1CK29YPX8+fMZNWrUHQuEv/76K7169brtuS+++CLLli1j3759lmMPP/ww8fHxrFixAoBmzZrRpEkT5syZA0B6ejo+Pj48//zzjB8/noSEBDw9Pfn+++/p06cPABEREdSqVYvQ0FCaN29+2882mUyYTCbLz4mJifj4+CgxFJEsSU5N58vNUcz+x27HjzWvzNj/1cTNWTuricjfcqv45OjoyN69e6levXqm45GRkdSvX5+kpKR7DbXYUoFQRKTwWbr7DOMW7saUmo6/twufD2xMRQ9na4clcot8W4PwxIkTVKhQgcqVK2d6VahQ4T+Lh/lh2LBhlClThqZNm/LFF1/wz/pnaGgowcHBmcZ36tSJ0NBQAJKTk9mxY0emMUajkeDgYMuYHTt2kJKSkmmMv78/lSpVsoy5nbfeegs3NzfLy8fHJ1e+r4gUDzfbjteMbUuPBuVJN8PXoSdo/67ajkUkb3h6et523enw8HBLV4WIiEhx0aNBeX58OghPFwciYi7T68PN7DhxydphieSabBcI27VrR1xc3C3HExISaNeuXa4ElVNTp07lp59+IiQkhN69e/Pcc8/xwQcfWN6PiYmhbNmymc4pW7YsiYmJXL9+ndjYWNLS0m47JiYmxjKHvb39Lesg/nPM7UyYMIGEhATLKzo6+h6/rYgUR95ujnzQP4Dvn2xGNa+SXLyazP/9vIfec7ew73SCtcMTkSLkySef5KmnnmL69Ols3LiRjRs3Mm3aNJ5++mmefPJJa4cnIiKS7xr6uPPbsBbULudK7JVk+n/2F4t3nbZ2WCK5IttN8zfXGvy3ixcvUqJEiWzNNX78eKZPn37XMQcPHsTf3z9L873yyiuWfw4ICODq1au8/fbbjBgxIltx5QUHBwccHBysHYaIFBH3VS3DHyNbMX/zcWatimTXyXh6zNnEY80qM66j2o5F5N698soruLi48O677zJhwgQAypcvz5QpUwpEbiUiImIN5d2dWPhMEKN/DOfPA+cY9WM4R85fYcz/amA03lorESksslwgfPDBB4GMdf4GDRqUqdiVlpbGnj17uO+++7L14WPHjmXQoEF3HePn55etOf+pWbNmvPbaa5hMJhwcHPD29r5lt+Fz587h6uqKk5MTNjY22NjY3HaMt7c3AN7e3iQnJxMfH5/pKcJ/jhERyQ92NkaebO1HjwbleXP5QZbsPsM3f51g2d6zjO/sT5/AikpSRCTHDAYDo0ePZvTo0Vy+fBkAFxcXK0clIiJifSUcbJn7WCBv/3mIj9cdZc7aIxyLvcK7fRviZG9j7fBEciTLBUI3t4zdMs1mMy4uLjg5OVnes7e3p3nz5tluN/H09MTT0zNb52RHeHg4Hh4elmJmUFAQy5cvzzQmJCSEoKAgION7BAYGsnr1astGJ+np6axevZrhw4cDEBgYiJ2dHatXr6Z3794AHDp0iJMnT1rmERHJT95ujrzfP4D+TSsx6bd9HD5/hf/7ZQ/fbzvJ672027GI3DsVBkVERDIzGg282Nmfqp4lmbBoD8v3xhAdF8pnAxrj7eZo7fBEsi3LBcIvv/wSAF9fX8aNG5ftduJ7dfLkSeLi4jh58iRpaWmWRbOrVatGyZIlWbp0KefOnaN58+Y4OjoSEhLCm2++ybhx4yxzPPPMM8yZM4f/+7//Y8iQIaxZs4affvqJZcuWWcaMGTOGgQMH0rhxY5o2bcqsWbO4evUqgwcPBjIKpUOHDmXMmDGUKlUKV1dXnn/+eYKCgu64g7GISH4Iqlqa5SNb8dWW48wMiSQ8OqPt+NFmlRjXsSbuzvbWDlFECrhGjRqxevVqPDw8CAgIuO2yMjft3LkzHyMTEREpmPoEVqRyaWee/mYHe08n0PPDTXw+oAn1KuomvRQu2V6DcPLkyXkRx3+aNGkSX331leXngIAAANauXUvbtm2xs7Pjww8/ZPTo0ZjNZqpVq8Z7772X6anGKlWqsGzZMkaPHs3s2bOpWLEin3/+OZ06dbKM6devHxcuXGDSpEnExMTQsGFDVqxYkWnjkpkzZ2I0Gunduzcmk4lOnTrx0Ucf5cPvgojI3dnZGHmi1d9tx7+Fn+Hbv06ybM9ZXuzsz0ONfdR2LCJ31LNnT0vnRc+ePe9aIBQREZEMTXxLsfi5Fgz9KozD56/Q95MtvPdQQ7rWK2ft0ESyzGA2m83ZOeHcuXOMGzeO1atXc/78ef59elpaWq4GWFQlJibi5uZGQkICrq6u1g5HRIqo0KMXmbxkH5HnrgAZO6+91rOu7miKFGHKMQo+XSMRkaIpMSmF57/fxfrICwCM61iDYe2q6Yab5Kuc5hnZLhB26dKFkydPMnz4cMqVK3fLH/SePXtmZ7piS4mhiOSXlLR0vtpynFmrDnPFlIrBAI80rcQLndR2LFIU5VaO4efnR1hYGKVLl850PD4+nkaNGnHs2LF7DbXYUh4oIlJ0paal88byg3y5+TgAvRqWZ1rv+jjaafMSyR/5ViB0cXFh48aNNGzYMLsxyj8oMRSR/HY+MYk3lx9kcfgZADyc7fi/zv70U9uxSJGSWzmG0WgkJiYGLy+vTMfPnTuHj48PycnJ9xpqsaU8UESk6Ptu6wkm/baftHQzjSq588njjfF0cbB2WFIM5DTPMGb3g3x8fG5pKxYRkYLPy9WRWQ8HsOCp5tQs68KlaylMWLSXBz7ewp5T8dYOT0QKiCVLlrBkyRIAVq5cafl5yZIl/Prrr7z22mtUqVIlzz4/MjKSnj17UqZMGVxdXWnZsiVr167NNGbEiBEEBgbi4OBwx5vWK1eupHnz5ri4uODp6Unv3r05fvx4pjHfffcdDRo0wNnZmXLlyjFkyBAuXryYpTgvXrxIxYoVMRgMxMfH5+CbiohIUfZos8p8PaQpro627DwZT68PN3PwbKK1wxK5o2w/Qfjnn3/y7rvv8sknn+Dr65tHYRV9unMsItaUkpbO16EnmBkSaWk77t+0Ei90rIlHCbUdixRm95pjGI0Z948NBsMtN4Xt7Ozw9fXl3XffpXv37rkS77/VqFGD6tWr89Zbb+Hk5MSsWbOYP38+R48exdvbG8goENasWZOtW7eyZ88ewsPDM80RFRVFrVq1GDNmDEOHDiUhIYHRo0dz+fJly+7LmzdvpnXr1sycOZMePXpw+vRpnnnmGWrUqMGiRYv+M85evXqRnJzMH3/8waVLl3B3d8/yd1QeKCJSfBy9cIUnvtpOVOxVStjbMPvhAIJrl/3vE0VyKN9ajD08PLh27Rqpqak4OztjZ2eX6f24uLjsTFdsKTEUkYLgfGISb/0Rwa+7TgPg7mzHi2o7FinUcivHqFKlCmFhYZQpUyYXo7u72NhYPD092bBhA61atQLg8uXLuLq6EhISQnBwcKbxU6ZMYfHixbcUCH/++Wf69++PyWSyFDyXLl1Kz549MZlM2NnZ8c477/Dxxx9z9OhRy3kffPAB06dP59SpU3eN8+OPP+bHH39k0qRJdOjQQQVCERG5q/hryTz33U62HL2IwQCTutdmcIu8expfirec5hm22f2gWbNmZfcUEREpoLxcHZnZryEPN/Fh8pL9RMRcZsKivSzYdpKpPevSwMfd2iGKiJVERUXl+2eWLl2amjVr8vXXX9OoUSMcHBz45JNP8PLyIjAwMMvzBAYGYjQa+fLLLxk0aBBXrlzhm2++ITg42HJzOygoiJdeeonly5fTpUsXzp8/z88//0zXrl3vOveBAweYOnUqW7duzfJGLSaTCZPJZPk5MVEtZiIixYm7sz1fDWnKpN/288O2k7y69ADnEk282LmmdjiWAiPbBcKBAwfmRRwiImJFzfxK8/vzLS1tx7tPJdDro8083MSHFzr5U0ptxyLF0tWrV1m/fj0nT568ZVOSESNG5PrnGQwGVq1aRa9evXBxccFoNOLl5cWKFSvw8PDI8jxVqlThzz//5KGHHuLpp58mLS2NoKAgli9fbhnTokULvvvuO/r160dSUhKpqan06NGDDz/88I7zmkwm+vfvz9tvv02lSpWyXCB86623ePXVV7Mcv4iIFD12NkbefKAuFT2ceHvlIeauP8r5y0lM710fO5tsbw8hkuty9Kfw6NGjvPzyy/Tv35/z588D8Mcff7B///5cDU5ERPKPrY2RIS2rsHpcGx4MqIDZDD9si6b9u+v4busJ0tK1QZVIcbJr1y6qVatG//79GT58OK+//jqjRo3ipZdeynZHyfjx4zEYDHd9RUREYDabGTZsGF5eXmzcuJFt27bRq1cvevTowdmzZ7P8eTExMTz55JMMHDiQsLAw1q9fj729PX369LGsq3jgwAFGjhzJpEmT2LFjBytWrOD48eM888wzd5x3woQJ1KpVi8ceeyxb33/ChAkkJCRYXtHR0dk6X0REigaDwcCwdtWY0ac+NkYDi3ae5omvtnPVlGrt0ESyvwbh+vXr6dKlCy1atGDDhg0cPHgQPz8/pk2bxvbt2/n555/zKtYiRWvPiEhBty0qjkm/7SMi5jIA9Su6MbVnXRqq7VikQMutHKNt27bUqFGDuXPn4ubmxu7du7Gzs+Oxxx5j5MiRPPjgg1me68KFC/+5O7Cfnx8bN26kY8eOXLp0KVPs1atXZ+jQoYwfPz7TOXdag/CVV15hxYoVhIWFWY6dOnUKHx8fQkNDad68OY8//jhJSUksXLjQMmbTpk20atWKM2fOUK5cuVtibNiwIXv37rW0g5nNZtLT07GxsWHixIlZfkpQeaCIiKyJOMdz3+0kKSWdBhXd+GJQE0qXdLB2WFIE5NsahOPHj+f1119nzJgxuLi4WI63b9+eOXPmZHc6EREpoJpWKcXvz7fkm79O8N6fkew5lcADH22mX2Mf/q+z2o5Firrw8HA++eQTjEYjNjY2mEwm/Pz8mDFjBgMHDsxWgdDT0xNPT8//HHft2jXg752UbzIajaSnp2f5865du3bLHDY2NgCWea5du4atre1tx9zp/vkvv/zC9evXLT+HhYUxZMgQNm7cSNWqVbMcn4iISHv/svzwZHOGzA9j96kEen+8ha+HNKNSaWdrhybFVLZbjPfu3csDDzxwy3EvLy9iY2NzJSgRESkYbG2MDG5xo+24UUbb8YKwaNq9s45v/1LbsUhRZmdnZymyeXl5cfLkSQDc3NzyrEU2KCgIDw8PBg4cyO7du4mMjOSFF14gKiqKbt26WcYdOXKE8PBwYmJiuH79OuHh4YSHh1vWSezWrRthYWFMnTqVw4cPs3PnTgYPHkzlypUJCAgAoEePHixatIiPP/6YY8eOsXnzZkaMGEHTpk0pX748AL/++iv+/v6Wz61atSp169a1vKpUydiBslatWnh5eeXJ74mIiBRdAZU8+PnZ+6jg7sTxi9d48OMt7DudYO2wpJjKdoHQ3d39tmvA7Nq1iwoVKuRKUCIiUrB4uTjy3kMNWfhMEP7eLiRcT+Hlxfvo9eFmdp28ZO3wRCQPBAQEWFp027Rpw6RJk/juu+8YNWoUdevWzZPPLFOmDCtWrODKlSu0b9+exo0bs2nTJn777TcaNGhgGffEE08QEBDAJ598QmRkJAEBAQQEBHDmzBkgo7Pl+++/Z/HixQQEBNC5c2ccHBxYsWIFTk5OAAwaNIj33nuPOXPmULduXfr27UvNmjVZtGiR5XMSEhI4dOhQnnxXERERgKqeJVn03H3UKudK7BUT/T4JZdNhPXwl+S/baxCOGzeOrVu3snDhQmrUqMHOnTs5d+4cAwYMYMCAAUyePDmvYi1StPaMiBRWqWnpfPvXCd4NieRyUsaCyg83UduxSEGRWznG9u3buXz5Mu3ateP8+fMMGDCALVu2UL16db744otMBTvJHuWBIiLyb4lJKTz99Q5Cj13EzsbAO30b0LOhHsKS7MtpnpHtAmFycjLDhg1j/vz5pKWlYWtrS1paGo888gjz58+3rN0id6fEUEQKuwuXTUz7I4Jfdp4CwM3JjnGdavJI00rYGA1Wjk6k+MqNHMNsNhMdHY2XlxeOjo65HKEoDxQRkdsxpaYx5qfdLNuT0bX5crdaPNHKz8pRSWGTbwXCm6Kjo9m7dy9XrlwhICCA6tWr52SaYkuJoYgUFduPx/HKb/s5eDYRgLoVXJnasy6NKnlYOTKR4ik3coz09HQcHR3Zv3+/crw8oDxQRETuJD3dzNTfDzB/y3EAnmrtx/jO/hh1A16yKN92Mb7Jx8cHHx+fnJ4uIiJFRGPfUiwd3oLvtp7knT8Pse90Ig9+tIWHGlfkxc7+lC7pYO0QRSSbjEYj1atX5+LFiyoQioiI5COj0cDkHrXxdnNk2h8RfLrhGOcTk5jRpwH2ttneRkIky7L9p6t3795Mnz79luMzZsygb9++uRKUiIgULrY2Rgbe58vacW3pE1gRgJ+2n6LdO+v4JvS4djsWKYSmTZvGCy+8wL59+6wdioiISLFiMBh4pk1V3u3bABujgcXhZxj6VRhXTKnWDk2KsGy3GHt6erJmzRrq1auX6fjevXsJDg7m3LlzuRpgUaXWEhEpynaciOOVxfs5cKPtuE75jLbjwMpqOxbJa7mVY3h4eHDt2jVSU1Oxt7e37P57U1xc3L2GWmwpDxQRkaxae+g8z327k+spadSr4MYXg5rg6aIOHbmzfGsxvnLlCvb2t+5SaWdnR2JiYnanExGRIiiwcimWPt+S77ae4O2Vh9h/JpHeH2+hb2BFXuziTxm1HYsUeDNnzsRg0HpHIiIi1tSuphc/PNWcIfPD2Hs6gT5zt/D1kKZULl3C2qFJEZPtJwibNm1K9+7dmTRpUqbjU6ZMYenSpezYsSNXAyyqdOdYRIqL2Csmpv8RwcIdGbsduzraMq5TTR5tVlm7HYvkAeUYBZ+ukYiIZFdU7FUGfLGV6LjrlClpz5eDmlKvopu1w5ICKN92MV66dCkPPvggjzzyCO3btwdg9erV/PDDDyxcuJBevXplK/DiSomhiBQ3O05cYtJv+9h/JuNp89rlXHmtVx0CK5eycmQiRUtu5Rg2NjacPXsWLy+vTMcvXryIl5cXaWlp9xpqsaU8UEREcuL85SQGfxnG/jOJONvbMPexQFrX8LR2WFLA5DTPyPYmJT169GDx4sUcOXKE5557jrFjx3Lq1ClWrVql4qCIiNxRYGUPlgxvyWs96+DqaMuBs4n0/jiUcQt3E3vFZO3wRORf7nQP2WQy3Xa5GREREclbXi6OLHiqOS2qleZachpD5ofx665T1g5LiohsP0EouUN3jkWkOLt4xcT0FRH8tD0joXFxtGVcx5o82qwStjbZvnclIv9wrznG+++/D8Do0aN57bXXKFmypOW9tLQ0NmzYwPHjx9m1a1euxVzcKA8UEZF7kZyazriFu1my+wwAL3X158lWflo7WIB8bDG+KTk5mfPnz5Oenp7peKVKlXIyXbGjxFBEBHaezGg73nc6o+24VjlXXutZh8a+ajsWyal7zTGqVKkCwIkTJ6hYsSI2NjaW9+zt7fH19WXq1Kk0a9Ys12IubpQHiojIvUpPN/PG8oPM2xQFwNCWVZjYtRZGrfFd7OVbgfDw4cMMGTKELVu2ZDpuNpsxGAxajyaLlBiKiGRISzfz/baTvLPyEAnXUwDo3agi47v44+mi3Y5Fsiu3cox27dqxaNEiPDw8cjE6AeWBIiKSez7bcIw3lh8E4P4G5Xm7b30cbG3+4ywpyvKtQNiiRQtsbW0ZP3485cqVu+UR1gYNGmRnumJLiaGISGYXr5iYseIQP26PBjLajsf+rwaPNa+stmORbFCOUfDpGomISG5avOs04xbuJjXdTItqpZn7WCAujnbWDkusJN8KhCVKlGDHjh34+/tnO0j5mxJDEZHb+3fbsb+3C6/1qksTtR2LZElu5RhpaWnMnz+f1atX33ZZmTVr1txrqMWW8kAREcltGyIv8Oy3O7ianEbtcq7MH9IELxdHa4clVpBvuxjXrl2b2NjY7J4mIiKSJY0qefDbsJa83qsubk52RMRcpu/cUMb8FM6Fy9rtWCS/jBw5kpEjR5KWlkbdunVp0KBBppeIiIgUHK1reLLgqSDKlLTnwNlEen+8hRMXr1o7LClEsv0E4Zo1a3j55Zd58803qVevHnZ2mR9b1V3QrNGdYxGR/xZ3NZm3V0awICwasxlcHGwZ07EGj6vtWOSOcivHKFOmDF9//TVdu3bNxegElAeKiEjeOXHxKgO+2MaJi9co6+rAd080p5pXSWuHJfko31qMjcaMv5D9e+1BbVKSPUoMRUSyLjw6nkm/7WPPqQQgo+14as+6NK2itmORf8utHKN8+fKsW7eOGjVq5GJ0AsoDRUQkb52/nMRjn28l8twVSpew55uhzahdXv+/KS7yrUC4fv36u77fpk2b7ExXbCkxFBHJnrR0MwvCTvL2ykPEX8vY7fjBgAqM7+qv9VVE/iG3cox3332XY8eOMWfOnFtuDMu9UR4oIiJ5Le5qMgO+2Mq+04m4Odnx1ZCmNPRxt3ZYkg/yrUAouUOJoYhIztyu7Xj0/2owIEhtxyKQeznGAw88wNq1aylVqhR16tS5ZVmZRYsW3WuoxZbyQBERyQ8J11MY/OU2dp6Mp6SDLV8MaqIOnGIgXwuE8fHxzJs3j4MHDwJQp04dhgwZgpubW3anKraUGIqI3Jvd0fG88q+241fvr0Mzv9JWjkzEunIrxxg8ePBd3//yyy9zPHdxpzxQRETyy1VTKkO/CuOvY3E42hn5fEATWlYvY+2wJA/lW4Fw+/btdOrUCScnJ5o2bQpAWFgY169f588//6RRo0bZi7yYUmIoInLv0tLN/BgWzYyVEZa24wcCKjChiz9ermo7luJJOUbBp2skIiL5KSkljae/2cH6yAvY2xr5+NFGdKhV1tphSR7JtwJhq1atqFatGp999hm2trYApKam8sQTT3Ds2DE2bNiQvciLKSWGIiK559LVZGasPMSCsJOYzVDyRtvxQLUdSzGUmzlGamoq69at4+jRozzyyCO4uLhw5swZXF1dKVlSOyLmlPJAERHJb6bUNEb8sIuV+89hazQw++EAutUvZ+2wJA/kW4HQycmJXbt24e/vn+n4gQMHaNy4MdeuXcvOdMWWEkMRkdy3+8Zux7tvtB3XLOvC1J5qO5biJbdyjBMnTtC5c2dOnjyJyWQiMjISPz8/Ro4ciclkYu7cubkYdfGiPFBERKwhJS2dsT/tZsnuMxgN8HafBvQOrGjtsCSX5TTPyPZjFa6urpw8efKW49HR0bi4uGR3OhERkVzTwMedX59rwbQH6+HhbMehc5fp9+lfjFqwi/OJSdYOT6RQGTlyJI0bN+bSpUs4OTlZjj/wwAOsXr3aipGJiIhITtjZGJnZryH9GvuQboaxC3fz3dYT1g5LCohsFwj79evH0KFD+fHHH4mOjiY6OpoFCxbwxBNP0L9//7yIUUREJMuMRgMPN63EmrFtebRZJQwGWBx+hvbvrufzjcdISUu3dogihcLGjRt5+eWXsbe3z3Tc19eX06dPWykqERERuRc2RgNvPViPQff5AjDx1318vvGYdYOSAiHbBcJ33nmHBx98kAEDBuDr64uvry+DBg2iT58+TJ8+PS9i5Pjx4wwdOpQqVarg5ORE1apVmTx5MsnJyZnG7dmzh1atWuHo6IiPjw8zZsy4Za6FCxfi7++Po6Mj9erVY/ny5ZneN5vNTJo0iXLlyuHk5ERwcDCHDx/ONCYuLo5HH30UV1dX3N3dGTp0KFeuXMn9Ly4iIjnmUcKeNx6ox2/DWtDAx50rplReX3aQbu9v5K9jF60dnkiBl56eTlpa2i3HT506pa4RERGRQsxoNDC5R22eaVMVgNeXHWTOmsP/cZYUddkuENrb2zN79mwuXbpEeHg44eHhxMXFMXPmTBwcHPIiRiIiIkhPT+eTTz5h//79zJw5k7lz5/LSSy9ZxiQmJtKxY0cqV67Mjh07ePvtt5kyZQqffvqpZcyWLVvo378/Q4cOZdeuXfTq1YtevXqxb98+y5gZM2bw/vvvM3fuXLZu3UqJEiXo1KkTSUl/t6Y9+uij7N+/n5CQEH7//Xc2bNjAU089lSffXURE7k39iu78+ux9lrbjyHNXePjTvxi5YBfn1HYsckcdO3Zk1qxZlp8NBgNXrlxh8uTJdO3a1XqBiYiIyD0zGAy82LkmY/5XA4B3/oxkxooIsrlNhRQh2d6kJCEhgbS0NEqVKpXpeFxcHLa2tvm20PLbb7/Nxx9/zLFjGY/Cfvzxx0ycOJGYmBhLK8z48eNZvHgxERERQEZ79NWrV/n9998t8zRv3pyGDRsyd+5czGYz5cuXZ+zYsYwbNw7I+L5ly5Zl/vz5PPzwwxw8eJDatWsTFhZG48aNAVixYgVdu3bl1KlTlC9fPkvxa3FqEZH8F38tmXf+PMR3WzN2Oy5hb8Oo4BoMauGLnXY7liIit3KMU6dO0alTJ8xmM4cPH6Zx48YcPnyYMmXKsGHDBry8vHIx6uJFeaCIiBQkn204xhvLDwIw6D5fJveojcFgsHJUklP5tknJww8/zIIFC245/tNPP/Hwww9nd7ocS0hIyFSkDA0NpXXr1pnWyenUqROHDh3i0qVLljHBwcGZ5unUqROhoaEAREVFERMTk2mMm5sbzZo1s4wJDQ3F3d3dUhwECA4Oxmg0snXr1jvGazKZSExMzPQSEZH85e5sz+u96rFkWEsa+rhzNTmNN5YfpOvsjYQeVduxyD9VrFiR3bt3M3HiREaPHk1AQADTpk1j165dKg6KiIgUIU+29uO1nnUAmL/lOC/9upe0dD1JWNxku0C4detW2rVrd8vxtm3b3rVAlpuOHDnCBx98wNNPP205FhMTQ9myZTONu/lzTEzMXcf88/1/nnenMf9Oim1tbSlVqpRlzO289dZbuLm5WV4+Pj5Z/r4iIpK76lV0Y9Gz9zG9dz1KlbDn8Pkr9P/sL0b8oLZjkX+ytbXl0UcfZcaMGXz00Uc88cQTmXY0FhERkaLh8SBf3u5TH6MBftgWzdifwknV5n7FSrYLhCaTidTU1FuOp6SkcP369WzNNX78eAwGw11fN9uDbzp9+jSdO3emb9++PPnkk9kN32omTJhAQkKC5RUdHW3tkEREijWj0UC/JpVYM7YNjzevjMEAS3afof076/hsg3Y7Fnnrrbf44osvbjn+xRdf5NnGdCIiImI9fRv7MPvhAGyNBhaHn+H5H3aRnKqcuLjIdoGwadOmmTb+uGnu3LkEBgZma66xY8dy8ODBu778/Pws48+cOUO7du247777bonB29ubc+fOZTp282dvb++7jvnn+/88705jzp8/n+n91NRU4uLiLGNux8HBAVdX10wvERGxPndne17rVZelw1sSUClz2/GWo7HWDk/Eaj755BP8/f1vOV6nTh3mzp1rhYhEREQkr/VoUJ6PHwvE3sbIH/tiePqb7SSlpFk7LMkHttk94fXXXyc4OJjdu3fToUMHAFavXk1YWBh//vlntuby9PTE09MzS2NPnz5Nu3btCAwM5Msvv8RozFzbDAoKYuLEiaSkpGBnZwdASEgINWvWxMPDwzJm9erVjBo1ynJeSEgIQUFBAFSpUgVvb29Wr15Nw4YNgYzFHbdu3cqzzz5rmSM+Pp4dO3ZYCqJr1qwhPT2dZs2aZev7i4hIwVG3ghu/PHMfP+88xbQ/Ijh8/gqPfLaVHg3KM7FrLbzdHK0doki+iomJoVy5crcc9/T05OzZs1aISERERPLD/2qX5fOBjXnqm+2sPXSBIfPD+GxAY0o4ZLuEJIVItp8gbNGiBaGhofj4+PDTTz+xdOlSqlWrxp49e2jVqlVexMjp06dp27YtlSpV4p133uHChQvExMRkWvPvkUcewd7enqFDh7J//35+/PFHZs+ezZgxYyxjRo4cyYoVK3j33XeJiIhgypQpbN++neHDhwMZ23yPGjWK119/nSVLlrB3714GDBhA+fLl6dWrFwC1atWic+fOPPnkk2zbto3NmzczfPhwHn744SzvYCwiIgWT0WjgocY+rB3blgFBlTEaYOnuM3R4dx2fbjiqtmMpVnx8fNi8efMtxzdv3pynOU9kZCQ9e/akTJkyuLq60rJlS9auXZtpzIgRIwgMDMTBwcFyU/ffVq5cSfPmzXFxccHT05PevXtz/PjxTGO+++47GjRogLOzM+XKlWPIkCFcvPjfGxbNnz+f+vXr4+joiJeXF8OGDcvp1xURESmQWtfw5KvBTSlhb8OWoxcZ8MU2EpNSrB2W5CVzIfDll1+agdu+/mn37t3mli1bmh0cHMwVKlQwT5s27Za5fvrpJ3ONGjXM9vb25jp16piXLVuW6f309HTzK6+8Yi5btqzZwcHB3KFDB/OhQ4cyjbl48aK5f//+5pIlS5pdXV3NgwcPNl++fDlb3ykhIcEMmBMSErJ1noiI5J+9p+LND3y4yVz5xd/NlV/83dzh3XXmzYcvWDsskbvKrRxj+vTp5tKlS5u/+OIL8/Hjx83Hjx83z5s3z1y6dGnzm2++mUvR3qp69ermrl27mnfv3m2OjIw0P/fcc2ZnZ2fz2bNnLWOef/5585w5c8yPP/64uUGDBrfMcezYMbODg4N5woQJ5iNHjph37Nhhbt26tTkgIMAyZtOmTWaj0WiePXu2+dixY+aNGzea69SpY37ggQfuGt+7775rLl++vPm7774zHzlyxLx7927zb7/9lq3vqDxQREQKi50n4sz1Jq8wV37xd3P39zea466YrB2S/Iec5hkGs9msvautIDExETc3NxISErQeoYhIAZaebuaXG23HF68mA9C9fjkmdqtFOTft5ioFT27lGGazmfHjx/P++++TnJzxZ9/R0ZEXX3yRSZMm5Va4mcTGxuLp6cmGDRssnSmXL1/G1dWVkJAQgoODM42fMmUKixcvJjw8PNPxn3/+mf79+2MymSzL0ixdupSePXtiMpmws7PjnXfe4eOPP+bo0aOW8z744AOmT5/OqVOnbhvfpUuXqFChAkuXLrUstZMTygNFRKQw2X8mgcfnbSPuajI1y7rw7RPN8HRxsHZYcgc5zTOy3WIsIiL/3959h0V1tG0Av5fei3QiIihSLIgNsRcU7LxvYotR7JrYo0ZNomLMFzVREzXGLmhiiRp7wYZdBEVRQUFQ7IAF6dLn+4OwbzaAAgIL7P27rr10z5mdfWYY2DmzZ2ZIkSgpSdC/hRUCpneC99/Tjg/fikXXZeew9tx97uxGNZZEIsGSJUvw8uVLXLlyBTdv3kRCQkKFDQ4CgJGREezt7bF161akpaUhJycH69atg6mpaak2w2vevDmUlJTg6+uL3NxcJCUl4ffff4e7u7t0rWo3Nzc8efIER48ehRAC8fHx2LNnD3r27FlsvidPnkReXh6ePXsGR0dH1K5dGwMGDMCTJ0/eGU9mZiaSk5NlHkRERNVFQ0t9/Dm2NUx11REZn4KB6wIRm/RW3mFROeMAIRERUQnoa6liQb9GODSpHZpbGyI9KxeLj0Wgx4rzuBTN3Y6p5tLR0UHLli3RqFEjqKtX7N0CEokEp06dwo0bN6CrqwsNDQ0sX74c/v7+0k3nSsLGxgYnTpzA119/DXV1dRgYGODp06fYtWuXNE3btm2xbds2DBw4EGpqajA3N4e+vj5Wr15dbL4PHjxAXl4efvjhB/zyyy/Ys2cPEhIS0K1bN+ldlkVZtGgR9PX1pQ8rK6sSl4WIiKgqsDPTxa5xbvjIQBMPXqVhwLpAPElIl3dYVI44QEhERFQKDS31sXucG376pAmMtNVw/2UahmwMwoTt1/lNKtUoaWlpmDt3Ltq0aYP69evD1tZW5lEas2fPhkQieecjIiICQghMmDABpqamuHDhAoKDg+Hl5YU+ffqUaufkuLg4jBkzBt7e3rh69SrOnTsHNTU1fPLJJyhYXefOnTuYMmUK5s2bh5CQEPj7++Phw4cYP358sfnm5eUhOzsbK1euhIeHB1q3bo0dO3YgKiqq0EYq/zRnzhwkJSVJH++745CIiKgqqmusjT/HtYa1kRaeJLxF/7WBuP8yVd5hUTkp8x7V0dHRuH//Pjp06ABNTU0IISCRSMozNiIioiqpYNpx94bm+PnkPWwNfIgjt2JxJuIFJnWxw6h2NlBT4XdwVL2NHj0a586dw9ChQ2FhYfFB/bzp06dj+PDh70xja2uLgIAAHD58GG/evJGumfPbb7/h5MmT2LJlC2bPnl2i91u9ejX09fXx448/So/98ccfsLKyQlBQEFq3bo1Fixahbdu2mDlzJgCgSZMm0NbWRvv27fH999/DwsKiUL4Fx5ycnKTHTExMYGxsjMePHxcbj7q6eoXffUlERFQZahtqYdc4NwzZGIToF6kYuO4Kdo5tjfqmOvIOjT5QqQcIX79+jYEDByIgIAASiQRRUVGwtbXFqFGjYGhoiGXLllVEnERERFWOvqYqfPo2RP8WtTH/QDiuPXqDJf4R2B3yBN/1bYR2dsbyDpGozI4dO4YjR46gbdu2H5yXiYkJTExM3psuPT1/qlLBxiIFlJSUkJdX8vU+09PTC+WhrKwMANJ80tPToaKiUmSa4vbwK6iLyMhI1K5dGwCQkJCAV69ewdrausTxERERVWdmehr4c2xrfLYpGHdjk/Hphiv4c5wbbIy15R0afYBS394wbdo0qKio4PHjx9DS0pIeHzhwIPz9/cs1OCIiouqgoaU+do93w7L+zjDWUcODl2n4bFMQJmy7jueJnHZM1ZOhoSFq1apVqe/p5uYGQ0NDeHt74+bNm7h37x5mzpyJmJgY9OrVS5ouOjoaoaGhiIuLw9u3bxEaGorQ0FDpOoC9evXC1atX8d133yEqKgrXr1/HiBEjYG1tDRcXFwBAnz59sHfvXqxZswYPHjzApUuXMHnyZLRq1QqWlpYAgH379sHBwUH6vg0aNEC/fv0wZcoUXL58GWFhYfD29oaDgwM6d+5ciTVFREQkX0Y66tg22hX2Zrp4kZKJweuv4NHrNHmHRR+g1AOEJ06cwJIlS6Tfmhaws7PDo0ePyi0wIiKi6kQikeDj5rVxenonDG9TF0oS4Mjt/N2Ofzsbzd2OqdpZuHAh5s2bJ72rrzIYGxvD398fqamp6NKlC1q0aIGLFy/iwIEDcHZ2lqYbPXo0XFxcsG7dOty7dw8uLi5wcXHB8+fPAQBdunTB9u3bsX//fri4uMDT0xPq6urw9/eHpqYmAGD48OFYvnw5fv31VzRq1Aj9+/eHvb099u7dK32fpKQkREZGysS4detWuLq6olevXujYsSNUVVXh7+8v3R2ZiIhIUdTSVsO2Ma6ob6qDuOQMfLohiBuXVGMSUdwcimLo6uri+vXrsLOzg66uLm7evAlbW1tcu3YNHh4eeP36dUXFWqMkJydDX18fSUlJ0jV2iIio5rjzPBnzD4bh6sM3AABbY2349G2IDg3eP82S6EOUVx/DxcUF9+/fhxACdevWLTQAdv369Q8NVWGxH0hERDXJi5QMDFp/BQ9epqG2oSb+/Hu3Y5KPsvYzSr0GYfv27bF161YsXLgQQP4dE3l5efjxxx85tYKIiOhvTpZ62DXODftuPMMPRyPw4FUahm0ORo9G5vi2txM7TVTleXl5yTsEIiIiqgZMdTWwY0xrDFwXiIev0zF4/RX8Oa41LPTZ361OSn0HYVhYGLp27YpmzZohICAAffv2RXh4OBISEnDp0iXUq1evomKtUfjNMRGR4kjOyMbPJ+9hy+WHyBOApqoyJnapj9HtbaCuoizv8KiGYR+j6uPPiIiIaqLniW8xcH0gniS8hY2xNnaObQ0zPQ15h6VwytrPKPUAIZC/Hsuvv/6KmzdvIjU1Fc2aNcOECRNgYWFR2qwUFjuGRESK525sMuYd4LRjqljl3ccICQnB3bt3AQANGzaUbvJBZcd+IBER1VRP36Rj4LoreJb4FvVMtLFjbGuY6nKQsDJV6gAhfTh2DImIFJMQAvtDn+H/jkTgVWomAMCzoTnm9uG0Yyof5dXHePHiBQYNGoSzZ8/CwMAAAJCYmIjOnTtj586dMDHhwHZZsR9IREQ12ZOEdAxcF4jnSRmwM9XBjrGtYayjLu+wFEZZ+xml3sXY19cXu3fvLnR89+7d2LJlS2mzIyIiUigSiQT/camNgBkdMbKtDZSVJPAPj0PXZWex+kw0MnNy5R0iEQBg0qRJSElJkS4lk5CQgLCwMCQnJ2Py5MnyDo+IiIiqKKtaWtg+pjXM9NQR9SIVn20MQkJalrzDovco9QDhokWLYGxsXOi4qakpfvjhh3IJioiIqKbT01DFvD5OODK5HVrVrYWM7Dz8dDwSnr9cwLl7L+UdHhH8/f3x22+/wdHRUXrMyckJq1evxrFjx+QYGREREVV1dY21sWNMa5joqiMiLgWfbQxCYjoHCauyUg8QPn78GDY2NoWOW1tb4/Hjx+USFBERkaJwMNfDn+Na45eBTWGiq46YV2nw3hyMcb9fw9M36fIOjxRYXl4eVFVVCx1XVVVFXl6eHCIiIiKi6sTWRAc7xrSGsY4a7sQmY+imYCS9zZZ3WFSMUg8Qmpqa4tatW4WO37x5E0ZGRuUSFBERkSKRSCTwcvkIAdM7YlS7/GnHx8Pj4b78HH4NiOK0Y5KLLl26YMqUKXj+/Ln02LNnzzBt2jR07dpVjpERERFRdVHfVAfbx7RGLW013H6WhGGbg5GcwUHCqqjUA4SDBw/G5MmTcebMGeTm5iI3NxcBAQGYMmUKBg0aVBExEhERKQRdDVXM7e2Eo5Pbo5VN/rTjpSfuwePn8zgb+ULe4ZGC+fXXX5GcnIy6deuiXr16qFevHmxsbJCcnIxVq1bJOzwiIiKqJhqY6WLbaFcYaKni5pNEDN8cjNTMHHmHRf9S6l2Ms7KyMHToUOzevRsqKioA8qegDBs2DGvXroWamlqFBFrTcPc6IiJ6FyEEDt58ju+P3MXLlPzdjrs7mWFubydY1dKSc3RUlZVnH0MIgVOnTiEiIgIA4OjoCHd39/IIU6GxH0hERIoo7FkSPt1wBckZOWhZ1xB+I1pBW11F3mHVOGXtZ5R6gLDAvXv3cPPmTWhqaqJx48awtrYuSzYKix1DIiIqiZSMbKw4FQXfyw+RmyegoaqECZ3qY0wHW2ioKss7PKqC2Meo+vgzIiIiRXXraSKGbAxCSkYOWtvWgu/wVtBUY5+2PJW1n1HqKcYFGjRogP79+6N3794cHCQiIqoguhqq+Pbvaceuf087XnbyHjx+OY8znHZMFSAgIABOTk5ITk4udC4pKQkNGzbEhQsX5BAZERERVXdNahtg68hW0FFXwZUHCRi99SoysrnedlVQpjsInz59ioMHD+Lx48fIypLdpnr58uXlFlxNxm+OiYiotAqmHf/fkbt48fe0425OZpjHacf0Dx/ax+jbty86d+6MadOmFXl+5cqVOHPmDPbt2/ehoSos9gOJiEjRhTxKwLBNwUjLykWHBiZYP7Q5Z8eUk0qbYnz69Gn07dsXtra2iIiIQKNGjfDw4UMIIdCsWTMEBASUOnhFxI4hERGVVUpGNlaejsLmS/nTjtVVlDChc32M5bRjwof3MaytreHv7w9HR8ciz0dERKB79+54/Pjxh4aqsNgPJCIiAoJjEuC9ORhvs3PR2d4Ea4c2h7oK+7IfqtKmGM+ZMwczZszA7du3oaGhgb/++gtPnjxBx44d0b9//9JmR0RERKWkq6GKb3o54diU9mhtWwuZOXlY/ve044CIeHmHR9VcfHw8VFVViz2voqKCly9fVmJEREREVBO1sqmFzcNbQkNVCWciX2LCthvIysmTd1gKq9QDhHfv3sWwYcMA5HcQ3759Cx0dHXz33XdYsmRJuQdIRERERWtgposdY1pj5WAXmOmp49HrdIz0u4bRW67hSUK6vMOjauqjjz5CWFhYsedv3boFCwuLSoyIiIiIaiq3ekbYOKwl1FWUcOpuPCbvuIHsXA4SykOpBwi1tbWl6w5aWFjg/v370nOvXr0qv8iIiIjovSQSCfo6W+L09E4Y28EWKkoSnLobD/fl57DiVBQXfaZS69mzJ+bOnYuMjIxC596+fYv58+ejd+/ecoiMiIiIaqJ2dsZYP6wF1JSV4B8eh6k7Q5HDQcJKV+o1CL28vNCrVy+MGTMGM2bMwIEDBzB8+HDs3bsXhoaGOHXqVEXFWqNw7RkiIqoIUfEpmHcgHIEPXgMA6tTSgk9fJ3RxMJNzZFRZPrSPER8fj2bNmkFZWRkTJ06Evb09gPy1B1evXo3c3Fxcv34dZmZsU2XFfiAREVFhARHxGPd7CLJzBfo1tcTyAU2hrCSRd1jVTqVtUvLgwQOkpqaiSZMmSEtLw/Tp03H58mXY2dlh+fLlsLa2LnXwiogdQyIiqihCCBy+FYvvj9xBfHL+bsfujqaY17sh6hhxt+Oarjz6GI8ePcLnn3+O48ePo6CrKJFI4OHhgdWrV8PGxqY8Q1Y47AcSEREV7eSdeHz+Rwhy8gT+2+wj/PSJMwcJS6lCBwhXrlyJsWPHQkNDA48fP4aVlRUkEv6APgQ7hkREVNFSM3Ow6nQUNl2MQU6egJqKEr7oVA/jO9bjbsc1WHn2Md68eYPo6GgIIWBnZwdDQ8NyilKxsR9IRERUvGO3YzFxxw3k5gkMaFEbi//bBEocJCyxCh0gVFFRwfPnz2FqagplZWXExsbC1NT0gwJWdOwYEhFRZYl+kT/t+PL9/GnHVrU04dOnIbo6copoTcQ+RtXHnxEREdG7Hb71HJN33ECeAAa3qoP/82rEQcISKms/Q6UkiSwtLfHXX3+hZ8+eEELg6dOnRS5cDQB16tQp8ZsTERFRxatvqotto11x5HYsvj98F08S3mLUlmvo6mCK+X047ZiIiIiIqpbeTSyRmycw7c9Q7Ah+DHUVJczv48TZrBWoRHcQrl+/HpMmTUJOTk6xaYQQkEgkyM3lboklwW+OiYhIHtIyc7AyIAqbLvxv2vHnHevh806cdlxTsI9R9fFnREREVDJ/hTzFjD03IQQwpasdpnVrIO+QqrwK36QkJSUFjx49QpMmTXDq1CkYGRkVmc7Z2bnEb67I2DEkIiJ5in6RCp+D4bgY/QpA/rTj+b0bwt2J046rO/Yxqj7+jIiIiEpua+BDzDsQDgCY38cJI9pys7R3qdApxgCgq6sLR0dH+Pr6wtHRERYWFmUKlIiIiOSvvqkOfh/VCkdvx+H7I3fwJOEtRm+9hi4OppjfxwnWRtryDpGIiIiICMPc6iIxPRvLT97DgkN3oK+piv82qy3vsGocpdIkVlZWxrhx44pdf5CIiIiqD4lEgl5NLHDqy44Y37EeVJUlCIh4gW4/n8fyk/eQkc1lQ4iIiIhI/iZ1qY+Rf985OHPPLZy6Ey/niGqeUg0QAkCjRo3w4MGDioiFiIiI5EBbXQWzezjg2JQOaFffGFk5eVh5Ogruy8/hRHgcSrgaCRERERFRhZBIJPi2lyM+blYbuXkCX2y/jisPXss7rBql1AOE33//PWbMmIHDhw8jNjYWycnJMg8iIiKqngqmHf82pBks9DXw9M1bjP09BCP9ruLhqzR5h0dERERECkxJSYIlHzeGu6MZsnLyMHrLNYQ9S5J3WDVGiTcpKaCk9L8xxX9uL81djEuHi1MTEVFVlp6Vg1UB0dh44QGycwXUlJUwvqMtPu9UH5pq3O24KmMfo+rjz4iIiKjsMrJzMdw3GFceJMBIWw27xruhnomOvMOqMip8F+MC586de+f5jh07liY7hcWOIRERVQf3X+bvdnwhKn+3448MNDG/jxO6OZnJfFFIVQf7GFUff0ZEREQfJiUjG59uCMLtZ0mw1NfAns/bwNJAU95hVQmVNkBI5YMdQyIiqi6EEPAPi8PCw3fwPCl/o7JO9ibw6dMQdY2523FVwz5G1cefERER0Yd7nZqJ/usC8eBlGuqZaGPXODcY6ajLOyy5K2s/o9RrEJ4/f/6dj4rw8OFDjBo1CjY2NtDU1ES9evUwf/58ZGVlyaSRSCSFHleuXJHJa/fu3XBwcICGhgYaN26Mo0ePypwXQmDevHmwsLCApqYm3N3dERUVJZMmISEBQ4YMgZ6eHgwMDDBq1CikpqZWSNmJiIjkTSKRoEdjC5ya3hETOufvdnw28iW6/3wey05E4m0WlxchIiIiosplpKOO30e5wlJfA/dfpmG471WkZGTLO6xqS6W0L+jUqVOhY/+cYlQRaxBGREQgLy8P69atQ/369REWFoYxY8YgLS0NS5culUl76tQpNGzYUPrcyMhI+v/Lly9j8ODBWLRoEXr37o3t27fDy8sL169fR6NGjQAAP/74I1auXIktW7bAxsYGc+fOhYeHB+7cuQMNDQ0AwJAhQxAbG4uTJ08iOzsbI0aMwNixY7F9+/ZyLzsREVFVoaWmgpkeDvi4WW3M/3va8aqAaOy9/gzz+jihO6cdExEREVEl+shAE7+PdkX/tYG4/SwJY7eGwHdES2iocs3s0ir1HYRv3ryRebx48QL+/v5o2bIlTpw4URExwtPTE76+vujevTtsbW3Rt29fzJgxA3v37i2U1sjICObm5tKHqqqq9NyKFSvg6emJmTNnwtHREQsXLkSzZs3w66+/Asi/e/CXX37Bt99+i379+qFJkybYunUrnj9/jv379wMA7t69C39/f2zcuBGurq5o164dVq1ahZ07d+L58+cVUn4iIqKqxNZEB1tHtsLaz5rhIwNNPEt8i3G/h2C471XEcLdj+kD37t1Dv379YGxsDD09PbRr1w5nzpyRSTN58mQ0b94c6urqaNq0aZH5HD9+HK1bt4auri5MTEzw8ccf4+HDhzJptm3bBmdnZ2hpacHCwgIjR47E69ev3xnf1atX0bVrVxgYGMDQ0BAeHh64efPmhxSZiIiIPkA9Ex1sGdEKOuoqCHzwGpN23EBObp68w6p2Sj1AqK+vL/MwNjZGt27dsGTJEnz11VcVEWORkpKSUKtWrULH+/btC1NTU7Rr1w4HDx6UORcYGAh3d3eZYx4eHggMDAQAxMTEIC4uTiaNvr4+XF1dpWkCAwNhYGCAFi1aSNO4u7tDSUkJQUFBxcabmZmJ5ORkmQcREVF1JZFI4NnIAie/7IAJnetBTVkJ5+69hMfP5/HT8QikZ+XIO0Sqpnr37o2cnBwEBAQgJCQEzs7O6N27N+Li4mTSjRw5EgMHDiwyj5iYGPTr1w9dunRBaGgojh8/jlevXuG///2vNM2lS5cwbNgwjBo1CuHh4di9ezeCg4MxZsyYYmNLTU2Fp6cn6tSpg6CgIFy8eBG6urrw8PBAdjanNBEREclL49r62OjdAmoqSjh5Jx6z/rqNvDxuuVEapR4gLI6ZmRkiIyPLK7t3io6OxqpVqzBu3DjpMR0dHSxbtgy7d+/GkSNH0K5dO3h5eckMEsbFxcHMzKxQ3AUdzoJ/35fG1NRU5ryKigpq1apVqOP6T4sWLZIZWLWysipDyYmIiKqWgmnHx6d1QIcGJsjKzcPqM/fRbfl5+IfFgnuhUWm8evUKUVFRmD17Npo0aQI7OzssXrwY6enpCAsLk6ZbuXIlJkyYAFtb2yLzCQkJQW5uLr7//nvUq1cPzZo1w4wZMxAaGiodyAsMDETdunUxefJk2NjYoF27dhg3bhyCg4OLjS8iIgIJCQn47rvvYG9vj4YNG2L+/PmIj4/Ho0ePyrcyiIiIqFRa2xph9afNoKwkwV/Xn+L/jt5lX7QUSj1AeOvWLZnHzZs34e/vj/Hjxxc7xaM4s2fPLnJjkX8+IiIiZF7z7NkzeHp6on///jLf8BobG+PLL7+Eq6srWrZsicWLF+Ozzz7DTz/9VNoiVog5c+YgKSlJ+njy5Im8QyIiIio3Nsba2DKiJdZ+1lw67Xj8H9fh7XsVD15yIy8qGSMjI9jb22Pr1q1IS0tDTk4O1q1bB1NTUzRv3rzE+TRv3hxKSkrw9fVFbm4ukpKS8Pvvv8Pd3V26/IybmxuePHmCo0ePQgiB+Ph47NmzBz179iw2X3t7exgZGWHTpk3IysrC27dvsWnTJjg6OqJu3brFvo4zSYiIiCpHNycz/PhxEwDAposxWH0mWs4RVR+l3qSkadOmkEgkhUZhW7dujc2bN5cqr+nTp2P48OHvTPPPb4afP3+Ozp07o02bNli/fv1783d1dcXJkyelz83NzREfHy+TJj4+Hubm5tLzBccsLCxk0hQMfpqbm+PFixcyeeTk5CAhIUH6+qKoq6tDXZ3bbRMRUc2VP+3YHB0bmGD1mWisP/8A5++9hOcvFzCmgw0mdK4PLbVSdz1IgUgkEpw6dQpeXl7Q1dWFkpISTE1N4e/vD0NDwxLnY2NjgxMnTmDAgAEYN24ccnNz4ebmhqNHj0rTtG3bFtu2bcPAgQORkZGBnJwc9OnTB6tXry42X11dXZw9exZeXl5YuHAhAMDOzg7Hjx+HikrxbXvRokVYsGBBieMnIiKisvu4eW0kvc3Gd4fvYOmJe9DXUsPQ1tbyDqvKK/UdhDExMXjw4AFiYmIQExODR48eIT09HZcvX4aDg0Op8jIxMYGDg8M7H2pqagDy7xzs1KkTmjdvDl9fXygpvT/00NBQmYE+Nzc3nD59WibNyZMn4ebmBiC/M2lubi6TJjk5GUFBQdI0bm5uSExMREhIiDRNQEAA8vLy4OrqWqryExER1USaasqY4WGP49M6oJP9/6Yduy87h2O3Oe1YEZV01ogQAhMmTICpqSkuXLiA4OBgeHl5oU+fPoiNjS3x+8XFxWHMmDHw9vbG1atXce7cOaipqeGTTz6Rtr87d+5gypQpmDdvHkJCQuDv74+HDx9i/Pjxxeb79u1bjBo1Cm3btsWVK1dw6dIlNGrUCL169cLbt2+LfR1nkhAREVWuke1sMLmrHQBg3oEwHAh9JueIqj6JqAa99ILBQWtra2zZsgXKyv/brrrgrr0tW7ZATU0NLi4uAIC9e/di7ty52LhxI0aMGAEAuHz5Mjp27IjFixejV69e2LlzJ3744Qdcv34djRo1AgAsWbIEixcvxpYtW2BjY4O5c+fi1q1buHPnDjQ0NAAAPXr0QHx8PNauXYvs7GyMGDECLVq0wPbt20tcpuTkZOjr6yMpKQl6enrlUk9ERERVjRACJ+/EY8GhO3iWmD+A0t7OGAv6NoStiY6co6uZqmIf4+XLl+/dHdjW1hYXLlxA9+7d8ebNG5nY7ezsMGrUKMyePVvmNT4+Pti/fz9CQ0Nljs+dOxf+/v64evWq9NjTp09hZWWFwMBAtG7dGkOHDkVGRgZ2794tTXPx4kW0b98ez58/l/mSucCmTZvw9ddfIzY2VvpldVZWFgwNDbFp0yYMGjSoRPVRFX9GRERENY0QAj4Hw7El8BFUlCTYMKwFOjuYvv+F1VxZ+xklnucTGBiI169fo3fv3tJjW7duxfz585GWlgYvLy+sWrWqQqbRnjx5EtHR0YiOjkbt2rVlzv1zfHPhwoV49OgRVFRU4ODggD///BOffPKJ9HybNm2wfft2fPvtt/j6669hZ2eH/fv3SwcHAeCrr75CWloaxo4di8TERLRr1w7+/v7SwUEA2LZtGyZOnIiuXbtCSUkJH3/8MVauXFnu5SYiIqruJBIJujc0R3s7E6w5G4215x7gQtQrePxyHmPa22JiF047VgQmJiYwMTF5b7r09HQAKDRTRElJCXl5eSV+v/T09EJ5FHzBXJBPenp6oWnBBWmK+/68IF+JRCITm0QiKVV8REREVPEkEgnm92mIxLfZOBD6HJ9vC8Hvo1zRsm4teYdWJZX4DsIePXqgU6dOmDVrFgDg9u3baNasGYYPHw5HR0f89NNPGDduHHx8fCoy3hqD3xwTEZEievgqDT6HwnE28iUAwFJfA9/2dkKPRuYygy5UdtW5j/Hq1Ss4ODigY8eOmDdvHjQ1NbFhwwasWLECV69ehbOzMwAgOjoaqampWLt2Lc6cOYM///wTAODk5AQ1NTUEBATA3d0dPj4+GDx4MFJSUvD1118jIiICd+/ehaamJvz8/DBmzBisXLkSHh4eiI2NxdSpU6GkpISgoCAAwL59+zBnzhzppnkRERFo2rQpRo4ciUmTJiEvLw+LFy/GoUOHcPfu3SLvOixKdf4ZERERVTfZuXkY93sIAiJeQFdDBX+OdYOTZc39/C1rP6PEaxCGhoaia9eu0uc7d+6Eq6srNmzYgC+//BIrV67Erl27Shc1ERERKZS6xtrwHd4S64c2R21DTTxPysAX265j6KZgRL/gbseKztjYGP7+/khNTUWXLl3QokULXLx4EQcOHJAODgLA6NGj4eLignXr1uHevXtwcXGBi4sLnj9/DgDo0qULtm/fjv3798PFxQWenp5QV1eHv78/NDU1AQDDhw/H8uXL8euvv6JRo0bo378/7O3tsXfvXun7JCUlITIyUvrcwcEBhw4dwq1bt+Dm5iadjuzv71/iwUEiIiKqXKrKSlj9aTO0rGuIlIwcDNscjIev0uQdVpVT4jsINTQ0EBUVBSsrKwBAu3bt0KNHD3zzzTcAgIcPH6Jx48ZISUmpuGhrEH5zTEREii4jOxe/nb2PtefuIysnD6rKEoxqZ4tJXepDW53TjsuKfYyqjz8jIiKiypf0NhuD1l/B3dhk1DbUxJ7xbWCur/H+F1YzFX4HoZmZGWJiYgDkL8Z8/fp1tG7dWno+JSUFqqqqpQiZiIiIFJmGqjK+7NYAJ6d1QGd7E2TnCqw9dx/uy8/hyC3udkxERERE5UdfUxVbR7ZCXSMtPH3zFkM3BeFNWpa8w6oySjxA2LNnT8yePRsXLlzAnDlzoKWlhfbt20vP37p1C/Xq1auQIImIiKjmsjbSxubhLbFhWAvUNtREbFIGJmzntGMiIiIiKl8muur4fZQrzPU0EPUiFSP8riItM0feYVUJJR4gXLhwIVRUVNCxY0ds2LABGzZsgJqamvT85s2b0b179woJkoiIiGo2iUSCbk5mOPVlR0zuagc1FSVcjH6FHivOY9Gxu+y4EREREVG5sKqlhd9HtYKBlipCnyRi3O8hyMzJlXdYclfiNQgLJCUlQUdHB8rKyjLHExISoKOjIzNoSMXj2jNERETFe/Q6Dd8duoPTES8AAOZ6Gvi2tyN6NbbgbsfvwT5G1cefERERkfyFPknEpxuuID0rFz0amePXT5tBWan69zMrfA3CAvr6+oUGBwGgVq1aHBwkIiKicmFtpI1Nw1ti47AWsKqlibjkDEzcfgOfbQpC9AtuiEZEREREH6aplQE2DGsBNWUlHAuLw9d7byv0GtilHiAkIiIiqizuTmY4Oa0jprrnTzu+FP0anr9cwKKjd5HKacdERERE9AHa1jfGysFNoSQB/rz2BEtPRMo7JLnhACERERFVaRqqypjq3gCnpnWEu6MpcvIE1p1/gK7LzuLQzecK/U0vEREREX0Yz0YWWPTfxgCA1WfuY3vQYzlHJB8cICQiIqJqoY6RFjZ6t8Qm7xaoU0sL8cmZmLTjBoZsDEJUPKcdExEREVHZDGxZB1O62gEA5h4Iw5m/18FWJBwgJCIiomqlq6MZTkzrgGnuDaCuooTL91+jx4oL+IHTjomIiIiojKa626F/89rIzROYsP06bj9NkndIlYoDhERERFTtaKgqY4q7HU592RHujmbIyRNY//e044OcdkxEREREpSSRSPDDfxujvZ0x0rNyMcLvKp4kpMs7rErDAUIiIiKqtqxqaWGjdwtsHv6/aceTd9zApxuCcI/TjomIiIioFFSVlfDbkGZwtNDDq9RMePsGIzE9S95hVQoOEBIREVG118Uhf9rxl93ypx0HPniNnisu4P+O3OG0YyIiIiIqMV0NVfgObwkLfQ08eJmGMVuvISM7V95hVTgOEBIREVGNoKGqjMld86cdd3PKn3a84UIMui47iwOhzzjtmIiIiIhKxFxfA34jWkFXQwVXH77B9N03kZdXs/uSHCAkIiKiGsWqlhY2DGsB3+EtYW2UP+14ys5QDN5whdOOiYiIiKhE7M11sW5oc6gqS3DkViwW+0fIO6QKxQFCIiIiqpE6O5ji+NQOmN6tATRUlXDlQQJ6rLiA7w/fQUpGtrzDIyIiIqIqrk09Y/z0iTMAYP35B9hy+aF8A6pAHCAkIiKiGktDVRmTutrh5LSO6O5khtw8gY0XY9B12TlOOyYiIiKi9/Jy+QgzPewBAD6HwnE8PE7OEVUMDhASERFRjWdVSwvrh7WA74iWqGukhRcp+dOOB62/gsg4TjsmIiIiouJ90akePnWtAyGAyTtu4PrjN/IOqdxxgJCIiIgURmd7U/hP7YAZ3fOnHQfFJKDnygtYyGnHRERERFQMiUSC7/o2RBcHU2Tm5GH0lmt4+CpN3mGVKw4QEhERkULRUFXGxC75ux17NMyfdrzpYgy6LDuH/Tc47ZiIiIiIClNRVsKqwS5o/JE+EtKyMNw3GK9TM+UdVrnhACEREREppNqGWlg3tAX8/p52/DIlE1P/DMXA9VcQEZcs7/CIiIiIqIrRVlfBpuEtUNtQEw9fp2P01mt4m5Ur77DKBQcIiYiISKF1sjfF8WkdMNPDHhqqSgiOSUCvlRfx3aE7SOa0YyIiIiL6B1NdDfiNaAV9TVXceJyIKTtvIDev+s9A4QAhERERKTx1FWVM6Fwfp77sCM+G5sjNE9h8KQZdlp7DvhtPOe2YiIiIiKTqm+pgo3cLqKko4cSdeCw8fKfa9xc5QEhERET0t9qGWlg7tDm2jGwFG2NtvErNxLQ/b2Lguiu4G8tpx0RERESUr2XdWvh5QFNIJIDf5YfYeCFG3iF9EA4QEhEREf1LxwYm8J/aHjM97KGpqozghwnoveoiFhwK57RjIiIiIgIA9GpigW96OgIA/u/oXRy+9VzOEZUdBwiJiIiIiiCddjy9I3o0yp927HvpIbosPYe91zntmIiIiIiAUe1sMLxNXQDAl3/eRHBMgnwDKiMOEBIRERG9w0cGmljzWXNsHdkKtn9PO/5y100MWBeIO8857ZiIiIhIkUkkEszt7QSPhmbIys3DmK3XEP0iVd5hlRoHCImIiIhKoEMDExyb2h5feeZPO7768A16r7oAn4PhSHrLacdEREREikpZSYIVg1zgUscASW+zMdw3GC9SMuQdVqlwgJCIiIiohNRVlPFFp/o4Pb0jejY2R57IX5S667Kz+CuE046JiIiIFJWGqjI2DmuBukZaePrmLUb5XUNaZo68wyoxDhASERERlZKlgSZ+G9Icv49qBVsTbbxKzcL03TfRfy2nHRMREREpKiMddfiNaIVa2mq4/SwJE7dfR05unrzDKhEOEBIRERGVUXs7E/hP6YBZng7QVFXGtUecdkxERESkyOoaa2OTdwtoqCrhTORLzD0QXi1mmXCAkIiIiOgDqKko4fNO9XB6ekf0amwhM+14T8hT5OVV/Q4hEREREZUflzqGWDnIBRIJsCP4MX47e1/eIb0XBwiJiIiIyoGlgSZWD2mGP0a5ot7f045n7L6J/usCEf48Sd7hEREREVEl6t7QHD59GgIAfjoeiX03nso5onfjACERERFROWpnZ4xjUzpgdg8HaKkpI+TRG/RZdRHzD4Rx2jERERGRAvFuUxdjO9gCAL7acwuXo1/JOaLicYCQiIiIqJypqShhfMe/px03yZ92vCXwEbosPYtd155w2vE73Lt3D/369YOxsTH09PTQrl07nDlzRibN5MmT0bx5c6irq6Np06ZF5nP8+HG0bt0aurq6MDExwccff4yHDx/KpFm9ejUcHR2hqakJe3t7bN269b3xPX78GL169YKWlhZMTU0xc+ZM5ORUnx0KiYiIqHLN9nRA7yYWyM4VGPd7CCLjUuQdUpE4QEhERERUQSz0NbH602bYNtoV9U118DotC1/tuYVP1l5GXFKGvMOrknr37o2cnBwEBAQgJCQEzs7O6N27N+Li4mTSjRw5EgMHDiwyj5iYGPTr1w9dunRBaGgojh8/jlevXuG///2vNM2aNWswZ84c+Pj4IDw8HAsWLMCECRNw6NChYmPLzc1Fr169kJWVhcuXL2PLli3w8/PDvHnzyqfwREREVOMoKUmwtL8zWtnUQkpmDob7BlfJfqBEVIetVGqg5ORk6OvrIykpCXp6evIOh4iIiCpYVk4efC/FYMXpKJjra8B/SgeoqZT/d7XVuY/x6tUrmJiY4Pz582jfvj0AICUlBXp6ejh58iTc3d1l0vv4+GD//v0IDQ2VOb5nzx4MHjwYmZmZUFLKr+NDhw6hX79+yMzMhKqqKtq0aYO2bdvip59+kr5u+vTpCAoKwsWLF4uM79ixY+jduzeeP38OMzMzAMDatWsxa9YsvHz5EmpqaiUqZ3X+GREREVHZJKZn4eM1lxGfnInNw1uilU2tCnmfsvYzeAchERERUSVQU1HCuI71EDC9E1YOcqmQwcHqzsjISDrVNy0tDTk5OVi3bh1MTU3RvHnzEufTvHlzKCkpwdfXF7m5uUhKSsLvv/8Od3d3qKqqAgAyMzOhoaEh8zpNTU0EBwcjO7votSIDAwPRuHFj6eAgAHh4eCA5ORnh4eHFxpOZmYnk5GSZBxERESkWAy01+I1ohV3j3CpscPBDVJuead++fVGnTh1oaGjAwsICQ4cOxfPnz2XS3Lp1C+3bt4eGhgasrKzw448/Fspn9+7dcHBwgIaGBho3boyjR4/KnBdCYN68ebCwsICmpibc3d0RFRUlkyYhIQFDhgyBnp4eDAwMMGrUKKSmppZ/oYmIiKjGMdfXQKOP9OUdRpUkkUhw6tQp3LhxA7q6utDQ0MDy5cvh7+8PQ0PDEudjY2ODEydO4Ouvv4a6ujoMDAzw9OlT7Nq1S5rGw8MDGzduREhICIQQuHbtGjZu3Ijs7Gy8elX0AuJxcXEyg4MApM//PQX6nxYtWgR9fX3pw8rKqsRlISIioprDqpYWnCyr5uyBajNA2LlzZ+zatQuRkZH466+/cP/+fXzyySfS88nJyejevTusra0REhKCn376CT4+Pli/fr00zeXLlzF48GCMGjUKN27cgJeXF7y8vBAWFiZN8+OPP2LlypVYu3YtgoKCoK2tDQ8PD2Rk/G9++JAhQxAeHo6TJ0/i8OHDOH/+PMaOHVs5FUFERERUzcyePRsSieSdj4iICAghMGHCBJiamuLChQsIDg6Gl5cX+vTpg9jY2BK/X1xcHMaMGQNvb29cvXoV586dg5qaGj755BMUrK4zd+5c9OjRA61bt4aqqir69esHb29vAJBOSy4vc+bMQVJSkvTx5MmTcs2fiIiI6ENV2zUIDx48CC8vL+k6MmvWrME333yDuLg46fovs2fPxv79+xEREQEAGDhwINLS0nD48GFpPq1bt0bTpk2xdu1aCCFgaWmJ6dOnY8aMGQCApKQkmJmZwc/PD4MGDcLdu3fh5OSEq1evokWLFgAAf39/9OzZE0+fPoWlpWWR8WZmZiIzM1P6PDk5GVZWVlx7hoiIiMpVVVzf7uXLl3j9+vU709ja2uLChQvo3r073rx5IxO7nZ0dRo0ahdmzZ8u8prg1COfOnQt/f39cvXpVeuzp06ewsrJCYGAgWrduLT2enZ2N+Ph4WFhYYP369Zg1axYSExOLHCScN28eDh48KPN+MTExsLW1xfXr1+Hi4lKS6qiSPyMiIiKqGRRqDcKEhARs27YNbdq0ka4jExgYiA4dOsgsDu3h4YHIyEi8efNGmubfi1t7eHggMDAQQH4HLy4uTiaNvr4+XF1dpWkCAwNhYGAgHRwEAHd3dygpKSEoKKjYmDm1hIiIiBSViYkJHBwc3vlQU1NDeno6gMJ38CkpKSEvL6/E75eenl4oD2VlZQAolI+qqipq164NZWVl7Ny5E7179y72DkI3Nzfcvn0bL168kB47efIk9PT04OTkVOL4iIiIiKqaajVAOGvWLGhra8PIyAiPHz/GgQMHpOdKsiZMcWn+ef6frysujampqcx5FRUV1KpV651rz3BqCREREdG7ubm5wdDQEN7e3rh58ybu3buHmTNnIiYmBr169ZKmi46ORmhoKOLi4vD27VuEhoYiNDQUWVlZAIBevXrh6tWr+O677xAVFYXr169jxIgRsLa2lt7ld+/ePfzxxx+IiopCcHAwBg0ahLCwMPzwww/S99m3bx8cHBykz7t37w4nJycMHToUN2/exPHjx/Htt99iwoQJUFdXr6RaIiIiIip/ch0gLOl6NAVmzpyJGzdu4MSJE1BWVsawYcNQXWZIq6urQ09PT+ZBRERERP9jbGwMf39/pKamokuXLmjRogUuXryIAwcOwNnZWZpu9OjRcHFxwbp163Dv3j24uLjAxcVFuoFdly5dsH37duzfvx8uLi7w9PSEuro6/P39oampCQDIzc3FsmXL4OzsjG7duiEjIwOXL19G3bp1pe+TlJSEyMhI6XNlZWUcPnwYysrKcHNzw2effYZhw4bhu+++q5wKIiIiIqogKvJ88+nTp2P48OHvTGNrayv9v7GxMYyNjdGgQQM4OjrCysoKV65cgZubG8zNzREfHy/z2oLn5ubm0n+LSvPP8wXHLCwsZNI0bdpUmuaf00oAICcnBwkJCdLXExEREVHZtGjRAsePH39nmrNnz743n0GDBmHQoEHFnnd0dMSNGzfemcfw4cML9VWtra1x9OjR974/ERERUXUi1zsIS7oeTVEK1o8p2PjDzc0N58+fR3Z2tjTNyZMnYW9vD0NDQ2ma06dPy+Rz8uRJuLm5AQBsbGxgbm4ukyY5ORlBQUHSNG5ubkhMTERISIg0TUBAAPLy8uDq6vqhVUJERERERERERFSpqsUahEFBQfj1118RGhqKR48eISAgAIMHD0a9evWkA3effvop1NTUMGrUKISHh+PPP//EihUr8OWXX0rzmTJlCvz9/bFs2TJERETAx8cH165dw8SJEwEAEokEU6dOxffff4+DBw/i9u3bGDZsGCwtLeHl5QUg/9tmT09PjBkzBsHBwbh06RImTpyIQYMGFbuDMRERERERERERUVVVLQYItbS0sHfvXnTt2hX29vYYNWoUmjRpgnPnzkkXhNbX18eJEycQExOD5s2bY/r06Zg3bx7Gjh0rzadNmzbYvn071q9fD2dnZ+zZswf79+9Ho0aNpGm++uorTJo0CWPHjkXLli2RmpoKf39/aGhoSNNs27YNDg4O6Nq1K3r27Il27dph/fr1lVchRERERERERERE5UQiqssuHzVMcnIy9PX1kZSUxA1LiIiIqNywj1H18WdEREREFaWs/YxqcQchERERERERERERVQy57mKsyApu3ExOTpZzJERERFSTFPQtOEmk6mI/kIiIiCpKWfuCHCCUk5SUFACAlZWVnCMhIiKimiglJQX6+vryDoOKwH4gERERVbTS9gW5BqGc5OXl4fnz59DV1YVEIin3/JOTk2FlZYUnT55wbRuwPv6N9SGL9SGL9SGL9fE/rAtZVbU+hBBISUmBpaUllJS4mkxVVNp+YFVta5WF5Wf5Fbn8AOuA5Vfs8gOsg9KWv6x9Qd5BKCdKSkqoXbt2hb+Pnp6eQv4CFYf1IYv1IYv1IYv1IYv18T+sC1lVsT5452DVVtZ+YFVsa5WJ5Wf5Fbn8AOuA5Vfs8gOsg9KUvyx9QX6tTEREREREREREpMA4QEhERERERERERKTAOEBYQ6mrq2P+/PlQV1eXdyhVAutDFutDFutDFutDFuvjf1gXslgfVFkUva2x/Cy/IpcfYB2w/IpdfoB1UFnl5yYlRERERERERERECox3EBIRERERERERESkwDhASEREREREREREpMA4QEhERERERERERKTAOEBIRERERERERESkwDhDWUKtXr0bdunWhoaEBV1dXBAcHyzukcufj4wOJRCLzcHBwkJ7PyMjAhAkTYGRkBB0dHXz88ceIj4+XyePx48fo1asXtLS0YGpqipkzZyInJ6eyi1Im58+fR58+fWBpaQmJRIL9+/fLnBdCYN68ebCwsICmpibc3d0RFRUlkyYhIQFDhgyBnp4eDAwMMGrUKKSmpsqkuXXrFtq3bw8NDQ1YWVnhxx9/rOiilcn76mP48OGF2ounp6dMmppSH4sWLULLli2hq6sLU1NTeHl5ITIyUiZNef1+nD17Fs2aNYO6ujrq168PPz+/ii5eqZWkPjp16lSofYwfP14mTU2pjzVr1qBJkybQ09ODnp4e3NzccOzYMel5RWobwPvrQ5HaBlWe0vbTdu/eDQcHB2hoaKBx48Y4evSozPmSfOZXJaUp/4YNG9C+fXsYGhrC0NAQ7u7uhdKX5DO+qilNHfj5+RUqn4aGhkyamtwGivo7LJFI0KtXL2ma6tQG3tdnLUpJPkOqy/Vfacu/d+9edOvWDSYmJtLP6ePHj8uked91YVVT2jo4e/Zskb8DcXFxMulqahso6vdbIpGgYcOG0jTVqQ2U5NqkKJXSFxBU4+zcuVOoqamJzZs3i/DwcDFmzBhhYGAg4uPj5R1auZo/f75o2LChiI2NlT5evnwpPT9+/HhhZWUlTp8+La5duyZat24t2rRpIz2fk5MjGjVqJNzd3cWNGzfE0aNHhbGxsZgzZ448ilNqR48eFd98843Yu3evACD27dsnc37x4sVCX19f7N+/X9y8eVP07dtX2NjYiLdv30rTeHp6CmdnZ3HlyhVx4cIFUb9+fTF48GDp+aSkJGFmZiaGDBkiwsLCxI4dO4SmpqZYt25dZRWzxN5XH97e3sLT01OmvSQkJMikqSn14eHhIXx9fUVYWJgIDQ0VPXv2FHXq1BGpqanSNOXx+/HgwQOhpaUlvvzyS3Hnzh2xatUqoaysLPz9/Su1vO9Tkvro2LGjGDNmjEz7SEpKkp6vSfVx8OBBceTIEXHv3j0RGRkpvv76a6GqqirCwsKEEIrVNoR4f30oUtugylHaftqlS5eEsrKy+PHHH8WdO3fEt99+K1RVVcXt27elaUrymV9VlLb8n376qVi9erW4ceOGuHv3rhg+fLjQ19cXT58+laYpyWd8VVLaOvD19RV6enoy5YuLi5NJU5PbwOvXr2XKHhYWJpSVlYWvr680TXVqA+/rs/5bST5DqtP1X2nLP2XKFLFkyRIRHBws7t27J+bMmSNUVVXF9evXpWned11Y1ZS2Ds6cOSMAiMjISJky5ubmStPU5DaQmJgoU+4nT56IWrVqifnz50vTVKc2UJJrk3+rrL4ABwhroFatWokJEyZIn+fm5gpLS0uxaNEiOUZV/ubPny+cnZ2LPJeYmChUVVXF7t27pcfu3r0rAIjAwEAhRP4fJiUlJZkO1po1a4Senp7IzMys0NjL27//sObl5Qlzc3Px008/SY8lJiYKdXV1sWPHDiGEEHfu3BEAxNWrV6Vpjh07JiQSiXj27JkQQojffvtNGBoaytTHrFmzhL29fQWX6MMUN0DYr1+/Yl9Tk+vjxYsXAoA4d+6cEKL8fj+++uor0bBhQ5n3GjhwoPDw8KjoIn2Qf9eHEPmDQFOmTCn2NTW5PoQQwtDQUGzcuFHh20aBgvoQgm2Dyl9p+2kDBgwQvXr1kjnm6uoqxo0bJ4Qo2Wd+VfKh/dScnByhq6srtmzZIj32vs/4qqa0deDr6yv09fWLzU/R2sDPP/8sdHV1ZS6mq1sbKFCSwZGSfIZU1+u/kpS/KE5OTmLBggXS5++6LqzqSjNA+ObNm2LTKFIb2Ldvn5BIJOLhw4fSY9W5DRR1bfJvldUX4BTjGiYrKwshISFwd3eXHlNSUoK7uzsCAwPlGFnFiIqKgqWlJWxtbTFkyBA8fvwYABASEoLs7GyZenBwcECdOnWk9RAYGIjGjRvDzMxMmsbDwwPJyckIDw+v3IKUs5iYGMTFxcmUX19fH66urjLlNzAwQIsWLaRp3N3doaSkhKCgIGmaDh06QE1NTZrGw8MDkZGRePPmTSWVpvycPXsWpqamsLe3x+eff47Xr19Lz9Xk+khKSgIA1KpVC0D5/X4EBgbK5FGQpqr/rfl3fRTYtm0bjI2N0ahRI8yZMwfp6enSczW1PnJzc7Fz506kpaXBzc1N4dvGv+ujgCK2DaoYZemnva/9lOQzv6ooj35qeno6srOzC/0Nf9dnfFVS1jpITU2FtbU1rKys0K9fP5m+qqK1gU2bNmHQoEHQ1taWOV5d2kBpve9vgKJd/+Xl5SElJaXQ34DirgtrkqZNm8LCwgLdunXDpUuXpMcVrQ1s2rQJ7u7usLa2ljleXdtAcdcm/1RZfQGV0gROVd+rV6+Qm5src6ECAGZmZoiIiJBTVBXD1dUVfn5+sLe3R2xsLBYsWID27dsjLCwMcXFxUFNTg4GBgcxrzMzMpGs1xMXFFVlPBeeqs4L4iyrfP8tvamoqc15FRQW1atWSSWNjY1Moj4JzhoaGFRJ/RfD09MR///tf2NjY4P79+/j666/Ro0cPBAYGQllZucbWR15eHqZOnYq2bduiUaNGAFBuvx/FpUlOTsbbt2+hqalZEUX6IEXVBwB8+umnsLa2hqWlJW7duoVZs2YhMjISe/fuBVDz6uP27dtwc3NDRkYGdHR0sG/fPjg5OSE0NFQh20Zx9QEoXtugilWWflpx7eef7avgWHFpqory6KfOmjULlpaWMhdB7/uMr0rKUgf29vbYvHkzmjRpgqSkJCxduhRt2rRBeHg4ateurVBtIDg4GGFhYdi0aZPM8erUBkrrfZ8hb968UZjrPwBYunQpUlNTMWDAAOmxd10X6urqyjHa8mFhYYG1a9eiRYsWyMzMxMaNG9GpUycEBQWhWbNmCjUG8Pz5cxw7dgzbt2+XOV5d20Bx1yb/Vll9AQ4QUrXVo0cP6f+bNGkCV1dXWFtbY9euXbzYokIGDRok/X/jxo3RpEkT1KtXD2fPnkXXrl3lGFnFmjBhAsLCwnDx4kV5h1IlFFcfY8eOlf6/cePGsLCwQNeuXXH//n3Uq1evssOscPb29ggNDUVSUhL27NkDb29vnDt3Tt5hyU1x9eHk5KRwbYOoKlu8eDF27tyJs2fPymzSUdM/493c3GTuam7Tpg0cHR2xbt06LFy4UI6RVb5NmzahcePGaNWqlczxmt4GKN/27duxYMECHDhwQOaL/XddF44aNUoeoZYre3t72NvbS5+3adMG9+/fx88//4zff/9djpFVvi1btsDAwABeXl4yx6trG6hq12qcYlzDGBsbQ1lZudCOk/Hx8TA3N5dTVJXDwMAADRo0QHR0NMzNzZGVlYXExESZNP+sB3Nz8yLrqeBcdVYQ/7vagbm5OV68eCFzPicnBwkJCQpRR7a2tjA2NkZ0dDSAmlkfEydOxOHDh3HmzBnUrl1bery8fj+KS6Onp1clB+mLq4+iuLq6AoBM+6hJ9aGmpob69eujefPmWLRoEZydnbFixQqFbRvF1UdRanrboIpVln5ace3nn+2r4FhJ85SXD+mnLl26FIsXL8aJEyfQpEmTd6b992d8VVIefXVVVVW4uLjI/B0qyKOseVaWDyl/Wloadu7cWaKL/arcBkrrfZ8hinL9t3PnTowePRq7du0qNNXy3/55XVhTtWrVSlo+RWkDQghs3rwZQ4cOlVnyqSjVoQ2U5tqksvoCHCCsYdTU1NC8eXOcPn1aeiwvLw+nT5+W+eaxJkpNTcX9+/dhYWGB5s2bQ1VVVaYeIiMj8fjxY2k9uLm54fbt2zKDQidPnoSenp50all1ZWNjA3Nzc5nyJycnIygoSKb8iYmJCAkJkaYJCAhAXl6e9ALYzc0N58+fR3Z2tjTNyZMnYW9vXyWn05bG06dP8fr1a1hYWACoWfUhhMDEiROxb98+BAQEFJoWXV6/H25ubjJ5FKSpan9r3lcfRQkNDQUAmfZRU+qjKHl5ecjMzFS4tlGcgvooiqK1DSpfZemnva/9lOQzv6ooaz/1xx9/xMKFC+Hv7y+zVnBx/v0ZX5WUR189NzcXt2/flpZPEdoAAOzevRuZmZn47LPP3vs+VbkNlNb7/gYowvXfjh07MGLECOzYsQO9evV6b/p/XhfWVKGhodLyKUIbAIBz584hOjq6RF8SVOU2UJZrk0rrC5RicxWqJnbu3CnU1dWFn5+fuHPnjhg7dqwwMDCQ2WGxJpg+fbo4e/asiImJEZcuXRLu7u7C2NhYvHjxQgghxPjx40WdOnVEQECAuHbtmnBzcxNubm7S1+fk5IhGjRqJ7t27i9DQUOHv7y9MTEzEnDlz5FWkUklJSRE3btwQN27cEADE8uXLxY0bN8SjR4+EEPnbnBsYGIgDBw6IW7duiX79+hXa5tzT01O4uLiIoKAgcfHiRWFnZycGDx4sPZ+YmCjMzMzE0KFDRVhYmNi5c6fQ0tIS69atq/Tyvs+76iMlJUXMmDFDBAYGipiYGHHq1CnRrFkzYWdnJzIyMqR51JT6+Pzzz4W+vr44e/asiI2NlT7S09Olacrj9+PBgwdCS0tLzJw5U9y9e1esXr1aKCsrC39//0ot7/u8rz6io6PFd999J65duyZiYmLEgQMHhK2trejQoYM0j5pUH7Nnzxbnzp0TMTEx4tatW2L27NlCIpGIEydOCCEUq20I8e76ULS2QZXjff20oUOHitmzZ0vTX7p0SaioqIilS5eKu3fvivnz5wtVVVVx+/ZtaZqSfOZXFaUt/+LFi4WamprYs2ePzN/wlJQUIYQo8Wd8VVLaOliwYIE4fvy4uH//vggJCRGDBg0SGhoaIjw8XJqmJreBAu3atRMDBw4sdLy6tYH39eFnz54thg4dKk1fks+Q6nT9V9ryb9u2TaioqIjVq1fL/A1ITEyUpnnfdWFVU9o6+Pnnn8X+/ftFVFSUuH37tpgyZYpQUlISp06dkqapyW2gwGeffSZcXV2LzLM6tYGSXKvJqy/AAcIaatWqVaJOnTpCTU1NtGrVSly5ckXeIZW7gQMHCgsLC6GmpiY++ugjMXDgQBEdHS09//btW/HFF18IQ0NDoaWlJf7zn/+I2NhYmTwePnwoevToITQ1NYWxsbGYPn26yM7OruyilEnBdvf/fnh7ewsh8rc6nzt3rjAzMxPq6uqia9euIjIyUiaP169fi8GDBwsdHR2hp6cnRowYIe1wF7h586Zo166dUFdXFx999JFYvHhxZRWxVN5VH+np6aJ79+7CxMREqKqqCmtrazFmzJhCH5g1pT6KqgcAwtfXV5qmvH4/zpw5I5o2bSrU1NSEra2tzHtUFe+rj8ePH4sOHTqIWrVqCXV1dVG/fn0xc+ZMkZSUJJNPTamPkSNHCmtra6GmpiZMTExE165dpYODQihW2xDi3fWhaG2DKs+7+mkdO3aUfpYX2LVrl2jQoIFQU1MTDRs2FEeOHJE5X5LP/KqkNOW3trYu8m/4/PnzhRCixJ/xVU1p6mDq1KnStGZmZqJnz57i+vXrMvnV5DYghBARERECgMznVYHq1gbe14f39vYWHTt2LPSa932GVJfrv9KWv2PHju9ML8T7rwurmtLWwZIlS0S9evWEhoaGqFWrlujUqZMICAgolG9NbQNC5N+ooampKdavX19kntWpDZTkWk1efQHJ3wESERERERERERGRAuIahERERERERERERAqMA4REREREREREREQKjAOERERERERERERECowDhERERERERERERAqMA4REREREREREREQKjAOERERERERERERECowDhERERERERERERAqMA4REREREREREREQKjAOEREQVZPjw4fDy8qr09/Xz84NEIoFEIsHUqVNL9Jrhw4dLX7N///4KjY+IiIioPD18+BASiQShoaElSi+vPlpxfHx80LRpU+nzio7Px8dH2u/75ZdfPjivf8ZeVRWU18DAQN6hEOH8+fPo06cPLC0ty3z9JYTA0qVL0aBBA6irq+Ojjz7C//3f/31QXBwgJCIqg4JORnEPHx8frFixAn5+fnKJT09PD7GxsVi4cGGJ0q9YsQKxsbEVHBUREREpin9++aimpob69evju+++Q05Ozgfn++/BMysrK8TGxqJRo0YflHdVURl9yIYNGyI2NhZjx479oHxmzJiB06dPl1NUFSc2NvaDB0OJyktaWhqcnZ2xevXqMucxZcoUbNy4EUuXLkVERAQOHjyIVq1afVBcKh/0aiIiBfXPwbQ///wT8+bNQ2RkpPSYjo4OdHR05BEagPwBTHNz8xKn19fXh76+fgVGRERERIrG09MTvr6+yMzMxNGjRzFhwgSoqqpizpw5pc4rNzcXEomkyHPKysql6vdUhKysLKipqZVLXpXRJ1NRUSmXOvvQPm92djZUVVU/OI73MTc3Z1+XqowePXqgR48exZ7PzMzEN998gx07diAxMRGNGjXCkiVL0KlTJwDA3bt3sWbNGoSFhcHe3h4AYGNj88Fx8Q5CIqIyMDc3lz709fWlA3IFDx0dnULfcHfq1AmTJk3C1KlTYWhoCDMzM2zYsAFpaWkYMWIEdHV1Ub9+fRw7dkzmvcLCwtCjRw/o6OjAzMwMQ4cOxatXr0od82+//QY7OztoaGjAzMwMn3zyyYdWAxEREVGx1NXVYW5uDmtra3z++edwd3fHwYMHAQDLly9H48aNoa2tDSsrK3zxxRdITU2VvtbPzw8GBgY4ePAgnJycoK6ujpEjR2LLli04cOCA9O7Es2fPFjnFODw8HL1794aenh50dXXRvn173L9/v8g48/LysGjRItjY2EBTUxPOzs7Ys2fPO8tWt25dLFy4EMOGDYOenp70TrxZs2ahQYMG0NLSgq2tLebOnYvs7GyZ1y5evBhmZmbQ1dXFqFGjkJGRIXP+333IunXrFrr7rWnTpvDx8QGQP9XQx8cHderUgbq6OiwtLTF58uR3xl8UiUSCdevWoXfv3tDS0oKjoyMCAwMRHR2NTp06QVtbG23atJGpx6KmGG/evBkNGzaEuro6LCwsMHHiRJn3WLNmDfr27QttbW3plMg1a9agXr16UFNTg729PX7//fdCsW3cuBH/+c9/oKWlBTs7O2lbAoA3b95gyJAhMDExgaamJuzs7ODr61vqOiCqCiZOnIjAwEDs3LkTt27dQv/+/eHp6YmoqCgAwKFDh2Bra4vDhw/DxsYGdevWxejRo5GQkPBB78sBQiKiSrRlyxYYGxsjODgYkyZNwueff47+/fujTZs2uH79Orp3746hQ4ciPT0dAJCYmIguXbrAxcUF165dg7+/P+Lj4zFgwIBSve+1a9cwefJkfPfdd4iMjIS/vz86dOhQEUUkIiIiKpKmpiaysrIAAEpKSli5ciXCw8OxZcsWBAQE4KuvvpJJn56ejiVLlmDjxo0IDw/HypUrMWDAAHh6eiI2NhaxsbFo06ZNofd59uwZOnToAHV1dQQEBCAkJAQjR44sdnrzokWLsHXrVqxduxbh4eGYNm0aPvvsM5w7d+6d5Vm6dCmcnZ1x48YNzJ07FwCgq6sLPz8/3LlzBytWrMCGDRvw888/S1+za9cu+Pj44IcffsC1a9dgYWGB3377rVT1+G9//fUXfv75Z6xbtw5RUVHYv38/GjduXKa8CgY9Q0ND4eDggE8//RTjxo3DnDlzcO3aNQghZAb8/m3NmjWYMGECxo4di9u3b+PgwYOoX7++TBofHx/85z//we3btzFy5Ejs27cPU6ZMwfTp0xEWFoZx48ZhxIgROHPmjMzrFixYgAEDBuDWrVvo2bMnhgwZIh0QmTt3Lu7cuYNjx45J764yNjYuUx0QydPjx4/h6+uL3bt3o3379qhXrx5mzJiBdu3aSQe9Hzx4gEePHmH37t3YunUr/Pz8EBIS8uE3gAgiIvogvr6+Ql9fv9Bxb29v0a9fP+nzjh07inbt2kmf5+TkCG1tbTF06FDpsdjYWAFABAYGCiGEWLhwoejevbtMvk+ePBEARGRkZInj+euvv4Senp5ITk5+Z1kAiH379r0zDREREdH7/LMflJeXJ06ePCnU1dXFjBkziky/e/duYWRkJH3u6+srAIjQ0NBi8y0QExMjAIgbN24IIYSYM2eOsLGxEVlZWe+NLSMjQ2hpaYnLly/LpBk1apQYPHhwseWztrYWXl5exZ4v8NNPP4nmzZtLn7u5uYkvvvhCJo2rq6twdnYuMr6C9/r5559lXuPs7Czmz58vhBBi2bJlokGDBsWW99/mz58v834FAIhvv/1W+jwwMFAAEJs2bZIe27Fjh9DQ0Cg2L0tLS/HNN98U+94AxNSpU2WOtWnTRowZM0bmWP/+/UXPnj2LjS01NVUAEMeOHRNCCNGnTx8xYsSIYt9XiOL77ETy9O/rr8OHDwsAQltbW+ahoqIiBgwYIIQQYsyYMYWuB0NCQgQAERERUeZYuAYhEVElatKkifT/ysrKMDIykvmG18zMDADw4sULAMDNmzdx5syZItd2uX//Pho0aFCi9+3WrRusra1ha2sLT09PeHp6SqdoEBEREVWEw4cPQ0dHB9nZ2cjLy8Onn34qnRZ76tQpLFq0CBEREUhOTkZOTg4yMjKQnp4u7Z+oqanJ9J1KKjQ0FO3bty/R2nbR0dFIT09Ht27dZI5nZWXBxcXlna9t0aJFoWN//vknVq5cifv37yM1NRU5OTnQ09OTnr979y7Gjx8v8xo3N7dCd8uVRv/+/fHLL79I+3k9e/ZEnz59oKJS+sv9f9Z3Qb/0333VjIwMJCcny5QLyO+/Pn/+HF27dn3ne/y73u7evVtos5S2bdtixYoVxcamra0NPT09aZ/5888/x8cffyydkePl5VXk3aVEVV1qaiqUlZUREhICZWVlmXMF14QWFhZQUVGRuRZ0dHQEkH8HYsG6hKXFKcZERJXo3x1ViUQic6xg8e28vDwA+R8Qffr0QWhoqMwjKiqqVFOEdXV1cf36dezYsQMWFhaYN28enJ2dkZiY+OGFIiIiIipC586dpf2Wt2/fYsuWLdDW1sbDhw/Ru3dvNGnSBH/99RdCQkKku3kWTEEG8qckF7cxybtoamqWOG3BuodHjhyR6WvduXPnvesQamtryzwPDAzEkCFD0LNnTxw+fBg3btzAN998I1OmslBSUkL+jUb/8891Da2srBAZGYnffvsNmpqa+OKLL9ChQ4dCax+WRFH90nf1Vf+ppPX+73orS2wFsRTE0aNHDzx69AjTpk2TDlLOmDGjTO9DJE8uLi7Izc3FixcvUL9+fZlHwcZCbdu2RU5Ojsx6oPfu3QMAWFtbl/m9OUBIRFSFNWvWDOHh4ahbt26hD4jSdq5UVFTg7u6OH3/8Ebdu3cLDhw8REBBQQZETERGRotPW1kb9+vVRp04dmbvZQkJCkJeXh2XLlqF169Zo0KABnj9/XqI81dTUkJub+840TZo0wYULF0o0QFawAcrjx48L9bWsrKxKFFOBy5cvw9raGt988w1atGgBOzs7PHr0SCaNo6MjgoKCZI5duXLlnfmamJggNjZW+jw5ORkxMTEyaTQ1NdGnTx+sXLkSZ8+eRWBgIG7fvl2q+D+Urq4u6tati9OnT5fqdY6Ojrh06ZLMsUuXLsHJyalU+ZiYmMDb2xt//PEHfvnlF6xfv75UryeqLKmpqdIvIwAgJiYGoaGhePz4MRo0aIAhQ4Zg2LBh2Lt3L2JiYhAcHIxFixbhyJEjAAB3d3c0a9YMI0eOxI0bNxASEoJx48ahW7duJZ5hVhROMSYiqsImTJiADRs2YPDgwfjqq69Qq1YtREdHY+fOndi4cWOh286Lc/jwYTx48AAdOnSAoaEhjh49iry8vDLffk5ERERUVvXr10d2djZWrVqFPn364NKlS1i7dm2JXlu3bl0cP34ckZGRMDIygr6+fqE0EydOxKpVqzBo0CDMmTMH+vr6uHLlClq1alWo76Orq4sZM2Zg2rRpyMvLQ7t27ZCUlIRLly5BT08P3t7eJS6XnZ0dHj9+jJ07d6Jly5Y4cuQI9u3bJ5NmypQpGD58OFq0aIG2bdti27ZtCA8Ph62tbbH5dunSBX5+fujTpw8MDAwwb948mT6gn58fcnNz4erqCi0tLfzxxx/Q1NT8oDuJysrHxwfjx4+HqakpevTogZSUFFy6dAmTJk0q9jUzZ87EgAED4OLiAnd3dxw6dAh79+7FqVOnSvy+8+bNQ/PmzdGwYUNkZmbi8OHD0imXRFXNtWvX0LlzZ+nzL7/8EgDg7e0NPz8/+Pr64vvvv8f06dPx7NkzGBsbo3Xr1ujduzeA/LuKDx06hEmTJqFDhw7Q1tZGjx49sGzZsg+KiwOERERVmKWlJS5duoRZs2ahe/fuyMzMhLW1NTw9PaGkVPKbwA0MDLB37174+PggIyMDdnZ22LFjBxo2bFiB0RMREREV5uzsjOXLl2PJkiWYM2cOOnTogEWLFmHYsGHvfe2YMWNw9uxZtGjRAqmpqThz5gzq1q0rk8bIyAgBAQGYOXMmOnbsCGVlZTRt2hRt27YtMs+FCxfCxMQEixYtwoMHD2BgYIBmzZrh66+/LlW5+vbti2nTpmHixInIzMxEr169MHfuXOm6iwAwcOBA3L9/H1999RUyMjLw8ccf4/PPP8fx48eLzXfOnDmIiYlB7969oa+vj4ULF8rcQWhgYIDFixfjyy+/RG5uLho3boxDhw7ByMioVPGXB29vb2RkZODnn3/GjBkzYGxs/N6dVb28vLBixQosXboUU6ZMgY2NDXx9fdGpU6cSv6+amhrmzJmDhw8fQlNTE+3bt8fOnTs/sDREFaNTp06Flg34J1VVVSxYsAALFiwoNo2lpSX++uuvco1LIt4VFRERVTt+fn6YOnVqmdYXlEgk2LdvH7y8vMo9LiIiIiKSPx8fH+zfv186vVFRfEgfmUgRcA1CIqIaKCkpCTo6Opg1a1aJ0o8fP77InZKJiIiIqOa5ffs2dHR08Ntvv8k7lEqho6NTaPdoIpLFOwiJiGqYlJQUxMfHA8ifcmJsbPze17x48QLJyckAAAsLizLvLkdEREREVVtCQgISEhIA5G/sUdQ6jjVNdHQ0AEBZWRk2NjZyjoaoauIAIRERERERERERkQLjFGMiIiIiIiIiIiIFxgFCIiIiIiIiIiIiBcYBQiIiIiIiIiIiIgXGAUIiIiIiIiIiIiIFxgFCIiIiIiIiIiIiBcYBQiIiIiIiIiIiIgXGAUIiIiIiIiIiIiIFxgFCIiIiIiIiIiIiBfb/gKUQ75nKPVwAAAAASUVORK5CYII=", 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" }, "metadata": {}, "output_type": "display_data" @@ -467,11 +470,11 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 59, "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T12:14:19.267027300Z", - "start_time": "2023-12-10T12:14:19.197131800Z" + "end_time": "2024-03-05T17:18:14.452120Z", + "start_time": "2024-03-05T17:18:14.420008Z" } }, "outputs": [], @@ -491,11 +494,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 60, "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T12:14:19.268048600Z", - "start_time": "2023-12-10T12:14:19.202421100Z" + "end_time": "2024-03-05T17:18:14.458649Z", + "start_time": "2024-03-05T17:18:14.453447Z" } }, "outputs": [ @@ -503,48 +506,50 @@ "name": "stdout", "output_type": "stream", "text": [ - "| Parameter | Type of parameter |\n", - "| ========================================================= | =========================================================================================================================================================================================================== |\n", - "| Positive electrode Bruggeman coefficient (electrolyte) | Parameter |\n", - "| Electrode width [m] | Parameter |\n", - "| Positive electrode thickness [m] | Parameter |\n", - "| Negative electrode Bruggeman coefficient (electrolyte) | Parameter |\n", - "| Negative electrode Bruggeman coefficient (electrode) | Parameter |\n", - "| Initial concentration in electrolyte [mol.m-3] | Parameter |\n", - "| Number of cells connected in series to make a battery | Parameter |\n", - "| Lower voltage cut-off [V] | Parameter |\n", - "| Ideal gas constant [J.K-1.mol-1] | Parameter |\n", - "| Separator Bruggeman coefficient (electrolyte) | Parameter |\n", - "| Upper voltage cut-off [V] | Parameter |\n", - "| Positive electrode Bruggeman coefficient (electrode) | Parameter |\n", - "| Separator thickness [m] | Parameter |\n", - "| Maximum concentration in negative electrode [mol.m-3] | Parameter |\n", - "| Faraday constant [C.mol-1] | Parameter |\n", - "| Reference temperature [K] | Parameter |\n", - "| Electrode height [m] | Parameter |\n", - "| Nominal cell capacity [A.h] | Parameter |\n", - "| Maximum concentration in positive electrode [mol.m-3] | Parameter |\n", - "| Number of electrodes connected in parallel to make a cell | Parameter |\n", - "| Negative electrode thickness [m] | Parameter |\n", - "| Separator porosity | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]' |\n", - "| Negative electrode active material volume fraction | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]' |\n", - "| Negative electrode OCP [V] | FunctionParameter with inputs(s) 'Negative particle stoichiometry' |\n", - "| Positive electrode porosity | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]' |\n", - "| Negative particle radius [m] | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]' |\n", - "| Positive electrode exchange-current density [A.m-2] | FunctionParameter with inputs(s) 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Maximum positive particle surface concentration [mol.m-3]', 'Temperature [K]' |\n", - "| Positive particle radius [m] | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]' |\n", - "| Positive electrode OCP [V] | FunctionParameter with inputs(s) 'Positive particle stoichiometry' |\n", - "| Negative electrode exchange-current density [A.m-2] | FunctionParameter with inputs(s) 'Electrolyte concentration [mol.m-3]', 'Negative particle surface concentration [mol.m-3]', 'Maximum negative particle surface concentration [mol.m-3]', 'Temperature [K]' |\n", - "| Negative electrode OCP entropic change [V.K-1] | FunctionParameter with inputs(s) 'Negative particle stoichiometry', 'Maximum negative particle surface concentration [mol.m-3]' |\n", - "| Current function [A] | FunctionParameter with inputs(s) 'Time[s]' |\n", - "| Initial concentration in positive electrode [mol.m-3] | FunctionParameter with inputs(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]' |\n", - "| Initial concentration in negative electrode [mol.m-3] | FunctionParameter with inputs(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]' |\n", - "| Positive electrode OCP entropic change [V.K-1] | FunctionParameter with inputs(s) 'Positive particle stoichiometry', 'Maximum positive particle surface concentration [mol.m-3]' |\n", - "| Positive electrode diffusivity [m2.s-1] | FunctionParameter with inputs(s) 'Positive particle stoichiometry', 'Temperature [K]' |\n", - "| Negative electrode porosity | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]' |\n", - "| Positive electrode active material volume fraction | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]' |\n", - "| Negative electrode diffusivity [m2.s-1] | FunctionParameter with inputs(s) 'Negative particle stoichiometry', 'Temperature [K]' |\n", - "| Ambient temperature [K] | FunctionParameter with inputs(s) 'Distance across electrode width [m]', 'Distance across electrode height [m]', 'Time [s]' |\n" + "┌───────────────────────────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ Parameter │ Type of parameter │\n", + "├───────────────────────────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n", + "│ Electrode width [m] │ Parameter │\n", + "│ Number of cells connected in series to make a battery │ Parameter │\n", + "│ Maximum concentration in positive electrode [mol.m-3] │ Parameter │\n", + "│ Positive electrode Bruggeman coefficient (electrode) │ Parameter │\n", + "│ Number of electrodes connected in parallel to make a cell │ Parameter │\n", + "│ Initial concentration in electrolyte [mol.m-3] │ Parameter │\n", + "│ Positive electrode Bruggeman coefficient (electrolyte) │ Parameter │\n", + "│ Nominal cell capacity [A.h] │ Parameter │\n", + "│ Electrode height [m] │ Parameter │\n", + "│ Upper voltage cut-off [V] │ Parameter │\n", + "│ Negative electrode Bruggeman coefficient (electrode) │ Parameter │\n", + "│ Faraday constant [C.mol-1] │ Parameter │\n", + "│ Positive electrode thickness [m] │ Parameter │\n", + "│ Separator Bruggeman coefficient (electrolyte) │ Parameter │\n", + "│ Negative electrode Bruggeman coefficient (electrolyte) │ Parameter │\n", + "│ Reference temperature [K] │ Parameter │\n", + "│ Maximum concentration in negative electrode [mol.m-3] │ Parameter │\n", + "│ Lower voltage cut-off [V] │ Parameter │\n", + "│ Negative electrode thickness [m] │ Parameter │\n", + "│ Separator thickness [m] │ Parameter │\n", + "│ Ideal gas constant [J.K-1.mol-1] │ Parameter │\n", + "│ Current function [A] │ FunctionParameter with inputs(s) 'Time[s]' │\n", + "│ Negative particle radius [m] │ FunctionParameter with inputs(s) 'Through-cell distance (x) [m]' │\n", + "│ Negative electrode active material volume fraction │ FunctionParameter with inputs(s) 'Through-cell distance (x) [m]' │\n", + "│ Positive particle radius [m] │ FunctionParameter with inputs(s) 'Through-cell distance (x) [m]' │\n", + "│ Positive electrode exchange-current density [A.m-2] │ FunctionParameter with inputs(s) 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Maximum positive particle surface concentration [mol.m-3]', 'Temperature [K]' │\n", + "│ Positive electrode porosity │ FunctionParameter with inputs(s) 'Through-cell distance (x) [m]' │\n", + "│ Negative electrode OCP entropic change [V.K-1] │ FunctionParameter with inputs(s) 'Negative particle stoichiometry', 'Maximum negative particle surface concentration [mol.m-3]' │\n", + "│ Positive electrode OCP [V] │ FunctionParameter with inputs(s) 'Positive particle stoichiometry' │\n", + "│ Initial concentration in positive electrode [mol.m-3] │ FunctionParameter with inputs(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]' │\n", + "│ Negative electrode porosity │ FunctionParameter with inputs(s) 'Through-cell distance (x) [m]' │\n", + "│ Positive electrode OCP entropic change [V.K-1] │ FunctionParameter with inputs(s) 'Positive particle stoichiometry', 'Maximum positive particle surface concentration [mol.m-3]' │\n", + "│ Negative electrode OCP [V] │ FunctionParameter with inputs(s) 'Negative particle stoichiometry' │\n", + "│ Positive electrode active material volume fraction │ FunctionParameter with inputs(s) 'Through-cell distance (x) [m]' │\n", + "│ Negative electrode exchange-current density [A.m-2] │ FunctionParameter with inputs(s) 'Electrolyte concentration [mol.m-3]', 'Negative particle surface concentration [mol.m-3]', 'Maximum negative particle surface concentration [mol.m-3]', 'Temperature [K]' │\n", + "│ Initial concentration in negative electrode [mol.m-3] │ FunctionParameter with inputs(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]' │\n", + "│ Ambient temperature [K] │ FunctionParameter with inputs(s) 'Distance across electrode width [m]', 'Distance across electrode height [m]', 'Time [s]' │\n", + "│ Positive particle diffusivity [m2.s-1] │ FunctionParameter with inputs(s) 'Positive particle stoichiometry', 'Temperature [K]' │\n", + "│ Separator porosity │ FunctionParameter with inputs(s) 'Through-cell distance (x) [m]' │\n", + "│ Negative particle diffusivity [m2.s-1] │ FunctionParameter with inputs(s) 'Negative particle stoichiometry', 'Temperature [K]' │\n", + "└───────────────────────────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘\n" ] } ], @@ -570,20 +575,20 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 61, "metadata": { + "scrolled": true, "ExecuteTime": { - "end_time": "2023-12-10T12:14:19.401195400Z", - "start_time": "2023-12-10T12:14:19.232194200Z" - }, - "scrolled": true + "end_time": "2024-03-05T17:18:14.635211Z", + "start_time": "2024-03-05T17:18:14.459932Z" + } }, "outputs": [ { "data": { - "text/plain": "{'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Negative electrode thickness [m]': 0.0001,\n 'Separator thickness [m]': 2.5e-05,\n 'Positive electrode thickness [m]': 0.0001,\n 'Electrode height [m]': 0.137,\n 'Electrode width [m]': 0.207,\n 'Nominal cell capacity [A.h]': 0.680616,\n 'Current function [A]': 0.680616,\n 'Maximum concentration in negative electrode [mol.m-3]': 24983.2619938437,\n 'Negative electrode diffusivity [m2.s-1]': ,\n 'Negative electrode OCP [V]': ,\n 'Negative electrode porosity': 0.3,\n 'Negative electrode active material volume fraction': 0.6,\n 'Negative particle radius [m]': 1e-05,\n 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n 'Negative electrode exchange-current density [A.m-2]': ,\n 'Negative electrode OCP entropic change [V.K-1]': ,\n 'Maximum concentration in positive electrode [mol.m-3]': 51217.9257309275,\n 'Positive electrode diffusivity [m2.s-1]': ,\n 'Positive electrode OCP [V]': ,\n 'Positive electrode porosity': 0.3,\n 'Positive electrode active material volume fraction': 0.5,\n 'Positive particle radius [m]': 1e-05,\n 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n 'Positive electrode exchange-current density [A.m-2]': ,\n 'Positive electrode OCP entropic change [V.K-1]': ,\n 'Separator porosity': 1.0,\n 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n 'Reference temperature [K]': 298.15,\n 'Ambient temperature [K]': 298.15,\n 'Number of electrodes connected in parallel to make a cell': 1.0,\n 'Number of cells connected in series to make a battery': 1.0,\n 'Lower voltage cut-off [V]': 3.105,\n 'Upper voltage cut-off [V]': 4.1,\n 'Initial concentration in negative electrode [mol.m-3]': 19986.609595075,\n 'Initial concentration in positive electrode [mol.m-3]': 30730.7554385565}" + "text/plain": "{'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Negative electrode thickness [m]': 0.0001,\n 'Separator thickness [m]': 2.5e-05,\n 'Positive electrode thickness [m]': 0.0001,\n 'Electrode height [m]': 0.137,\n 'Electrode width [m]': 0.207,\n 'Nominal cell capacity [A.h]': 0.680616,\n 'Current function [A]': 0.680616,\n 'Maximum concentration in negative electrode [mol.m-3]': 24983.2619938437,\n 'Negative particle diffusivity [m2.s-1]': ,\n 'Negative electrode OCP [V]': ,\n 'Negative electrode porosity': 0.3,\n 'Negative electrode active material volume fraction': 0.6,\n 'Negative particle radius [m]': 1e-05,\n 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n 'Negative electrode exchange-current density [A.m-2]': ,\n 'Negative electrode OCP entropic change [V.K-1]': ,\n 'Maximum concentration in positive electrode [mol.m-3]': 51217.9257309275,\n 'Positive particle diffusivity [m2.s-1]': ,\n 'Positive electrode OCP [V]': ,\n 'Positive electrode porosity': 0.3,\n 'Positive electrode active material volume fraction': 0.5,\n 'Positive particle radius [m]': 1e-05,\n 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n 'Positive electrode exchange-current density [A.m-2]': ,\n 'Positive electrode OCP entropic change [V.K-1]': ,\n 'Separator porosity': 1.0,\n 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n 'Reference temperature [K]': 298.15,\n 'Ambient temperature [K]': 298.15,\n 'Number of electrodes connected in parallel to make a cell': 1.0,\n 'Number of cells connected in series to make a battery': 1.0,\n 'Lower voltage cut-off [V]': 3.105,\n 'Upper voltage cut-off [V]': 4.1,\n 'Initial concentration in negative electrode [mol.m-3]': 19986.609595075,\n 'Initial concentration in positive electrode [mol.m-3]': 30730.7554385565}" }, - "execution_count": 15, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -602,19 +607,19 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 62, "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T12:14:19.460184100Z", - "start_time": "2023-12-10T12:14:19.418960800Z" + "end_time": "2024-03-05T17:18:14.680852Z", + "start_time": "2024-03-05T17:18:14.636665Z" } }, "outputs": [ { "data": { - "text/plain": "{'Ambient temperature [K]': 298.15,\n 'Boltzmann constant [J.K-1]': 1.380649e-23,\n 'Current function [A]': 5.0,\n 'Electrode height [m]': 0.065,\n 'Electrode width [m]': 1.58,\n 'Electron charge [C]': 1.602176634e-19,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Initial concentration in electrolyte [mol.m-3]': 1000,\n 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n 'Initial temperature [K]': 298.15,\n 'Lower voltage cut-off [V]': 2.5,\n 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Negative electrode OCP [V]': ('graphite_LGM50_ocp_Chen2020',\n ([array([0. , 0.03129623, 0.03499902, 0.0387018 , 0.04240458,\n 0.04610736, 0.04981015, 0.05351292, 0.05721568, 0.06091845,\n 0.06462122, 0.06832399, 0.07202675, 0.07572951, 0.07943227,\n 0.08313503, 0.08683779, 0.09054054, 0.09424331, 0.09794607,\n 0.10164883, 0.10535158, 0.10905434, 0.1127571 , 0.11645985,\n 0.12016261, 0.12386536, 0.12756811, 0.13127086, 0.13497362,\n 0.13867638, 0.14237913, 0.14608189, 0.14978465, 0.15348741,\n 0.15719018, 0.16089294, 0.1645957 , 0.16829847, 0.17200122,\n 0.17570399, 0.17940674, 0.1831095 , 0.18681229, 0.19051504,\n 0.1942178 , 0.19792056, 0.20162334, 0.2053261 , 0.20902886,\n 0.21273164, 0.2164344 , 0.22013716, 0.22383993, 0.2275427 ,\n 0.23124547, 0.23494825, 0.23865101, 0.24235377, 0.24605653,\n 0.2497593 , 0.25346208, 0.25716486, 0.26086762, 0.26457039,\n 0.26827314, 0.2719759 , 0.27567867, 0.27938144, 0.28308421,\n 0.28678698, 0.29048974, 0.29419251, 0.29789529, 0.30159806,\n 0.30530083, 0.30900361, 0.31270637, 0.31640913, 0.32011189,\n 0.32381466, 0.32751744, 0.33122021, 0.33492297, 0.33862575,\n 0.34232853, 0.34603131, 0.34973408, 0.35343685, 0.35713963,\n 0.36084241, 0.36454517, 0.36824795, 0.37195071, 0.37565348,\n 0.37935626, 0.38305904, 0.38676182, 0.3904646 , 0.39416737,\n 0.39787015, 0.40157291, 0.40527567, 0.40897844, 0.41268121,\n 0.41638398, 0.42008676, 0.42378953, 0.4274923 , 0.43119506,\n 0.43489784, 0.43860061, 0.44230338, 0.44600615, 0.44970893,\n 0.45341168, 0.45711444, 0.46081719, 0.46451994, 0.46822269,\n 0.47192545, 0.47562821, 0.47933098, 0.48303375, 0.48673651,\n 0.49043926, 0.49414203, 0.49784482, 0.50154759, 0.50525036,\n 0.50895311, 0.51265586, 0.51635861, 0.52006139, 0.52376415,\n 0.52746692, 0.53116969, 0.53487245, 0.53857521, 0.54227797,\n 0.54598074, 0.5496835 , 0.55338627, 0.55708902, 0.56079178,\n 0.56449454, 0.5681973 , 0.57190006, 0.57560282, 0.57930558,\n 0.58300835, 0.58671112, 0.59041389, 0.59411664, 0.59781941,\n 0.60152218, 0.60522496, 0.60892772, 0.61263048, 0.61633325,\n 0.62003603, 0.6237388 , 0.62744156, 0.63114433, 0.63484711,\n 0.63854988, 0.64225265, 0.64595543, 0.64965823, 0.653361 ,\n 0.65706377, 0.66076656, 0.66446934, 0.66817212, 0.67187489,\n 0.67557767, 0.67928044, 0.68298322, 0.686686 , 0.69038878,\n 0.69409156, 0.69779433, 0.70149709, 0.70519988, 0.70890264,\n 0.7126054 , 0.71630818, 0.72001095, 0.72371371, 0.72741648,\n 0.73111925, 0.73482204, 0.7385248 , 0.74222757, 0.74593034,\n 0.74963312, 0.75333589, 0.75703868, 0.76074146, 0.76444422,\n 0.76814698, 0.77184976, 0.77555253, 0.77925531, 0.78295807,\n 0.78666085, 0.79036364, 0.79406641, 0.79776918, 0.80147197,\n 0.80517474, 0.80887751, 0.81258028, 0.81628304, 0.81998581,\n 0.82368858, 0.82739136, 0.83109411, 0.83479688, 0.83849965,\n 0.84220242, 0.84590519, 0.84960797, 0.85331075, 0.85701353,\n 0.86071631, 0.86441907, 0.86812186, 0.87182464, 0.87552742,\n 0.87923019, 0.88293296, 0.88663573, 0.89033849, 0.89404126,\n 0.89774404, 0.9014468 , 1. ])],\n array([1.81772748, 1.0828807 , 0.99593794, 0.90023398, 0.79649431,\n 0.73354429, 0.66664314, 0.64137149, 0.59813869, 0.5670836 ,\n 0.54746181, 0.53068399, 0.51304734, 0.49394092, 0.47926274,\n 0.46065259, 0.45992726, 0.43801501, 0.42438665, 0.41150269,\n 0.40033659, 0.38957134, 0.37756538, 0.36292541, 0.34357086,\n 0.3406314 , 0.32299468, 0.31379458, 0.30795386, 0.29207319,\n 0.28697687, 0.27405477, 0.2670497 , 0.25857493, 0.25265783,\n 0.24826777, 0.2414345 , 0.23362778, 0.22956218, 0.22370236,\n 0.22181271, 0.22089651, 0.2194268 , 0.21830064, 0.21845333,\n 0.21753715, 0.21719357, 0.21635373, 0.21667822, 0.21738444,\n 0.21469313, 0.21541846, 0.21465495, 0.2135479 , 0.21392964,\n 0.21074206, 0.20873788, 0.20465319, 0.20205732, 0.19774358,\n 0.19444147, 0.19190285, 0.18850531, 0.18581399, 0.18327537,\n 0.18157659, 0.17814088, 0.17529686, 0.1719375 , 0.16934161,\n 0.16756649, 0.16609676, 0.16414985, 0.16260378, 0.16224113,\n 0.160027 , 0.15827096, 0.1588054 , 0.15552238, 0.15580869,\n 0.15220118, 0.1511132 , 0.14987253, 0.14874637, 0.14678037,\n 0.14620776, 0.14555879, 0.14389819, 0.14359279, 0.14242846,\n 0.14038612, 0.13882096, 0.13954628, 0.13946992, 0.13780934,\n 0.13973714, 0.13698858, 0.13523254, 0.13441178, 0.1352898 ,\n 0.13507985, 0.13647321, 0.13601512, 0.13435452, 0.1334765 ,\n 0.1348317 , 0.13275118, 0.13286571, 0.13263667, 0.13456447,\n 0.13471718, 0.13395369, 0.13448814, 0.1334765 , 0.13298023,\n 0.13259849, 0.13338107, 0.13309476, 0.13275118, 0.13443087,\n 0.13315202, 0.132713 , 0.1330184 , 0.13278936, 0.13225491,\n 0.13317111, 0.13263667, 0.13187316, 0.13265574, 0.13250305,\n 0.13324745, 0.13204496, 0.13242669, 0.13233127, 0.13198769,\n 0.13254122, 0.13145325, 0.13298023, 0.13168229, 0.1313578 ,\n 0.13235036, 0.13120511, 0.13089971, 0.13109058, 0.13082336,\n 0.13011713, 0.129869 , 0.12992626, 0.12942998, 0.12796026,\n 0.12862831, 0.12656689, 0.12734947, 0.12509716, 0.12110791,\n 0.11839751, 0.11244226, 0.11307214, 0.1092165 , 0.10683058,\n 0.10433014, 0.10530359, 0.10056993, 0.09950104, 0.09854668,\n 0.09921473, 0.09541635, 0.09980643, 0.0986612 , 0.09560722,\n 0.09755413, 0.09612258, 0.09430929, 0.09661885, 0.09366032,\n 0.09522548, 0.09535909, 0.09316404, 0.09450016, 0.0930877 ,\n 0.09343126, 0.0932404 , 0.09350762, 0.09339309, 0.09291591,\n 0.09303043, 0.0926296 , 0.0932404 , 0.09261052, 0.09249599,\n 0.09240055, 0.09253416, 0.09209515, 0.09234329, 0.09366032,\n 0.09333583, 0.09322131, 0.09264868, 0.09253416, 0.09243873,\n 0.09230512, 0.09310678, 0.09165615, 0.09159888, 0.09207606,\n 0.09175158, 0.09177067, 0.09236237, 0.09241964, 0.09320222,\n 0.09199972, 0.09167523, 0.09322131, 0.09190428, 0.09167523,\n 0.09285865, 0.09180884, 0.09150345, 0.09186611, 0.0920188 ,\n 0.09320222, 0.09131257, 0.09117896, 0.09133166, 0.09089265,\n 0.09058725, 0.09051091, 0.09033912, 0.09041547, 0.0911217 ,\n 0.0894611 , 0.08999555, 0.08921297, 0.08881213, 0.08797229,\n 0.08709427, 0.08503284, 0.07601531]))),\n 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n 'Negative electrode active material volume fraction': 0.75,\n 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n 'Negative electrode electrons in reaction': 1.0,\n 'Negative electrode exchange-current density [A.m-2]': ,\n 'Negative electrode porosity': 0.25,\n 'Negative electrode thickness [m]': 8.52e-05,\n 'Negative particle radius [m]': 5.86e-06,\n 'Nominal cell capacity [A.h]': 5.0,\n 'Number of cells connected in series to make a battery': 1.0,\n 'Number of electrodes connected in parallel to make a cell': 1.0,\n 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Positive electrode OCP [V]': ('nmc_LGM50_ocp_Chen2020',\n ([array([0.24879728, 0.26614516, 0.26886763, 0.27159011, 0.27431258,\n 0.27703505, 0.27975753, 0.28248 , 0.28520247, 0.28792495,\n 0.29064743, 0.29336992, 0.29609239, 0.29881487, 0.30153735,\n 0.30425983, 0.30698231, 0.30970478, 0.31242725, 0.31514973,\n 0.3178722 , 0.32059466, 0.32331714, 0.32603962, 0.32876209,\n 0.33148456, 0.33420703, 0.3369295 , 0.33965197, 0.34237446,\n 0.34509694, 0.34781941, 0.3505419 , 0.35326438, 0.35598685,\n 0.35870932, 0.3614318 , 0.36415428, 0.36687674, 0.36959921,\n 0.37232169, 0.37504418, 0.37776665, 0.38048913, 0.38321161,\n 0.38593408, 0.38865655, 0.39137903, 0.39410151, 0.39682398,\n 0.39954645, 0.40226892, 0.4049914 , 0.40771387, 0.41043634,\n 0.41315882, 0.41588129, 0.41860377, 0.42132624, 0.42404872,\n 0.4267712 , 0.42949368, 0.43221616, 0.43493864, 0.43766111,\n 0.44038359, 0.44310607, 0.44582856, 0.44855103, 0.45127351,\n 0.453996 , 0.45671848, 0.45944095, 0.46216343, 0.46488592,\n 0.46760838, 0.47033085, 0.47305333, 0.47577581, 0.47849828,\n 0.48122074, 0.48394321, 0.48666569, 0.48938816, 0.49211064,\n 0.4948331 , 0.49755557, 0.50027804, 0.50300052, 0.50572298,\n 0.50844545, 0.51116792, 0.51389038, 0.51661284, 0.51933531,\n 0.52205777, 0.52478024, 0.52750271, 0.53022518, 0.53294765,\n 0.53567012, 0.53839258, 0.54111506, 0.54383753, 0.54656 ,\n 0.54928247, 0.55200494, 0.5547274 , 0.55744986, 0.56017233,\n 0.5628948 , 0.56561729, 0.56833976, 0.57106222, 0.57378469,\n 0.57650716, 0.57922963, 0.5819521 , 0.58467456, 0.58739702,\n 0.59011948, 0.59284194, 0.5955644 , 0.59828687, 0.60100935,\n 0.60373182, 0.60645429, 0.60917677, 0.61189925, 0.61462172,\n 0.61734419, 0.62006666, 0.62278914, 0.62551162, 0.62823408,\n 0.63095656, 0.63367903, 0.6364015 , 0.63912397, 0.64184645,\n 0.64456893, 0.6472914 , 0.65001389, 0.65273637, 0.65545884,\n 0.65818131, 0.66090379, 0.66362625, 0.66634874, 0.66907121,\n 0.67179369, 0.67451616, 0.67723865, 0.67996113, 0.68268361,\n 0.68540608, 0.68812855, 0.69085103, 0.6935735 , 0.69629597,\n 0.69901843, 0.7017409 , 0.70446338, 0.70718585, 0.70990833,\n 0.71263081, 0.71535328, 0.71807574, 0.72079822, 0.72352069,\n 0.72624317, 0.72896564, 0.7316881 , 0.73441057, 0.73713303,\n 0.73985551, 0.74257799, 0.74530047, 0.74802293, 0.7507454 ,\n 0.75346787, 0.75619034, 0.75891281, 0.76163529, 0.76435776,\n 0.76708024, 0.7698027 , 0.77252517, 0.77524765, 0.77797012,\n 0.78069258, 0.78341506, 0.78613753, 0.78885999, 0.79158246,\n 0.79430494, 0.79702741, 0.79974987, 0.80247234, 0.8051948 ,\n 0.80791727, 0.81063974, 0.81336221, 0.81608468, 0.81880714,\n 0.82152961, 0.82425208, 0.82697453, 0.829697 , 0.83241946,\n 0.83514192, 0.83786439, 0.84058684, 0.84330931, 0.84603177,\n 0.84875424, 0.8514767 , 0.85419916, 0.85692162, 0.85964409,\n 0.86236656, 0.86508902, 0.86781149, 0.87053395, 0.87325642,\n 0.87597888, 0.87870135, 0.88142383, 0.8841463 , 0.88686877,\n 0.88959124, 0.89231371, 0.8950362 , 0.89775868, 0.90048116,\n 0.90320364, 0.90592613, 1. ])],\n array([4.4 , 4.2935653 , 4.2768621 , 4.2647018 , 4.2540312 ,\n 4.2449446 , 4.2364879 , 4.2302647 , 4.2225528 , 4.2182574 ,\n 4.213294 , 4.2090373 , 4.2051239 , 4.2012677 , 4.1981564 ,\n 4.1955218 , 4.1931167 , 4.1889744 , 4.1881533 , 4.1865883 ,\n 4.1850228 , 4.1832285 , 4.1808805 , 4.1805749 , 4.1789522 ,\n 4.1768146 , 4.1768146 , 4.1752872 , 4.173111 , 4.1726718 ,\n 4.1710877 , 4.1702285 , 4.168797 , 4.1669831 , 4.1655135 ,\n 4.1634517 , 4.1598248 , 4.1571712 , 4.154079 , 4.1504135 ,\n 4.1466532 , 4.1423388 , 4.1382346 , 4.1338248 , 4.1305799 ,\n 4.1272392 , 4.1228104 , 4.1186109 , 4.114182 , 4.1096005 ,\n 4.1046948 , 4.1004758 , 4.0956464 , 4.0909696 , 4.0864644 ,\n 4.0818448 , 4.077683 , 4.0733309 , 4.0690737 , 4.0647216 ,\n 4.0608654 , 4.0564747 , 4.0527525 , 4.0492401 , 4.0450211 ,\n 4.041986 , 4.0384736 , 4.035171 , 4.0320406 , 4.0289288 ,\n 4.02597 , 4.0227437 , 4.0199757 , 4.0175133 , 4.0149746 ,\n 4.0122066 , 4.009954 , 4.0075679 , 4.0050669 , 4.0023184 ,\n 3.9995501 , 3.9969349 , 3.9926589 , 3.9889555 , 3.9834003 ,\n 3.9783037 , 3.9755929 , 3.9707632 , 3.9681098 , 3.9635665 ,\n 3.9594433 , 3.9556634 , 3.9521511 , 3.9479132 , 3.9438281 ,\n 3.9400866 , 3.9362304 , 3.9314201 , 3.9283848 , 3.9242232 ,\n 3.9192028 , 3.9166257 , 3.9117961 , 3.90815 , 3.9038739 ,\n 3.8995597 , 3.8959136 , 3.8909314 , 3.8872662 , 3.8831048 ,\n 3.8793442 , 3.8747628 , 3.8702576 , 3.8666878 , 3.8623927 ,\n 3.8581741 , 3.854146 , 3.8499846 , 3.8450022 , 3.8422534 ,\n 3.8380919 , 3.8341596 , 3.8309333 , 3.8272109 , 3.823164 ,\n 3.8192315 , 3.8159864 , 3.8123021 , 3.8090379 , 3.8071671 ,\n 3.8040555 , 3.8013639 , 3.7970879 , 3.7953317 , 3.7920673 ,\n 3.788383 , 3.7855389 , 3.7838206 , 3.78111 , 3.7794874 ,\n 3.7769294 , 3.773608 , 3.7695992 , 3.7690265 , 3.7662776 ,\n 3.7642922 , 3.7626889 , 3.7603791 , 3.7575538 , 3.7552056 ,\n 3.7533159 , 3.7507198 , 3.7487535 , 3.7471499 , 3.7442865 ,\n 3.7423012 , 3.7400677 , 3.7385788 , 3.7345319 , 3.7339211 ,\n 3.7301605 , 3.7301033 , 3.7278316 , 3.7251589 , 3.723861 ,\n 3.7215703 , 3.7191267 , 3.7172751 , 3.7157097 , 3.7130945 ,\n 3.7099447 , 3.7071004 , 3.7045615 , 3.703588 , 3.70208 ,\n 3.7002664 , 3.6972122 , 3.6952841 , 3.6929362 , 3.6898055 ,\n 3.6890991 , 3.686522 , 3.6849759 , 3.6821697 , 3.6808143 ,\n 3.6786573 , 3.6761947 , 3.674763 , 3.6712887 , 3.6697233 ,\n 3.6678908 , 3.6652565 , 3.6630611 , 3.660274 , 3.6583652 ,\n 3.6554828 , 3.6522949 , 3.6499848 , 3.6470451 , 3.6405547 ,\n 3.6383405 , 3.635076 , 3.633549 , 3.6322317 , 3.6306856 ,\n 3.6283948 , 3.6268487 , 3.6243098 , 3.6223626 , 3.6193655 ,\n 3.6177621 , 3.6158531 , 3.6128371 , 3.6118062 , 3.6094582 ,\n 3.6072438 , 3.6049912 , 3.6030822 , 3.6012688 , 3.5995889 ,\n 3.5976417 , 3.5951984 , 3.593843 , 3.5916286 , 3.5894907 ,\n 3.587429 , 3.5852909 , 3.5834775 , 3.5817785 , 3.5801177 ,\n 3.5778842 , 3.5763381 , 3.5737801 , 3.5721002 , 3.5702102 ,\n 3.5684922 , 3.5672133 , 3.52302167]))),\n 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n 'Positive electrode active material volume fraction': 0.665,\n 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n 'Positive electrode electrons in reaction': 1.0,\n 'Positive electrode exchange-current density [A.m-2]': ,\n 'Positive electrode porosity': 0.335,\n 'Positive electrode thickness [m]': 7.56e-05,\n 'Positive particle radius [m]': 5.22e-06,\n 'Reference temperature [K]': 298.15,\n 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n 'Separator porosity': 0.47,\n 'Separator thickness [m]': 1.2e-05,\n 'Typical current [A]': 5.0,\n 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n 'Upper voltage cut-off [V]': 4.4}" + "text/plain": "{'Ambient temperature [K]': 298.15,\n 'Boltzmann constant [J.K-1]': 1.380649e-23,\n 'Current function [A]': 5.0,\n 'Electrode height [m]': 0.065,\n 'Electrode width [m]': 1.58,\n 'Electron charge [C]': 1.602176634e-19,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Initial concentration in electrolyte [mol.m-3]': 1000,\n 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n 'Initial temperature [K]': 298.15,\n 'Lower voltage cut-off [V]': 2.5,\n 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Negative electrode OCP [V]': ('graphite_LGM50_ocp_Chen2020',\n ([array([0. , 0.03129623, 0.03499902, 0.0387018 , 0.04240458,\n 0.04610736, 0.04981015, 0.05351292, 0.05721568, 0.06091845,\n 0.06462122, 0.06832399, 0.07202675, 0.07572951, 0.07943227,\n 0.08313503, 0.08683779, 0.09054054, 0.09424331, 0.09794607,\n 0.10164883, 0.10535158, 0.10905434, 0.1127571 , 0.11645985,\n 0.12016261, 0.12386536, 0.12756811, 0.13127086, 0.13497362,\n 0.13867638, 0.14237913, 0.14608189, 0.14978465, 0.15348741,\n 0.15719018, 0.16089294, 0.1645957 , 0.16829847, 0.17200122,\n 0.17570399, 0.17940674, 0.1831095 , 0.18681229, 0.19051504,\n 0.1942178 , 0.19792056, 0.20162334, 0.2053261 , 0.20902886,\n 0.21273164, 0.2164344 , 0.22013716, 0.22383993, 0.2275427 ,\n 0.23124547, 0.23494825, 0.23865101, 0.24235377, 0.24605653,\n 0.2497593 , 0.25346208, 0.25716486, 0.26086762, 0.26457039,\n 0.26827314, 0.2719759 , 0.27567867, 0.27938144, 0.28308421,\n 0.28678698, 0.29048974, 0.29419251, 0.29789529, 0.30159806,\n 0.30530083, 0.30900361, 0.31270637, 0.31640913, 0.32011189,\n 0.32381466, 0.32751744, 0.33122021, 0.33492297, 0.33862575,\n 0.34232853, 0.34603131, 0.34973408, 0.35343685, 0.35713963,\n 0.36084241, 0.36454517, 0.36824795, 0.37195071, 0.37565348,\n 0.37935626, 0.38305904, 0.38676182, 0.3904646 , 0.39416737,\n 0.39787015, 0.40157291, 0.40527567, 0.40897844, 0.41268121,\n 0.41638398, 0.42008676, 0.42378953, 0.4274923 , 0.43119506,\n 0.43489784, 0.43860061, 0.44230338, 0.44600615, 0.44970893,\n 0.45341168, 0.45711444, 0.46081719, 0.46451994, 0.46822269,\n 0.47192545, 0.47562821, 0.47933098, 0.48303375, 0.48673651,\n 0.49043926, 0.49414203, 0.49784482, 0.50154759, 0.50525036,\n 0.50895311, 0.51265586, 0.51635861, 0.52006139, 0.52376415,\n 0.52746692, 0.53116969, 0.53487245, 0.53857521, 0.54227797,\n 0.54598074, 0.5496835 , 0.55338627, 0.55708902, 0.56079178,\n 0.56449454, 0.5681973 , 0.57190006, 0.57560282, 0.57930558,\n 0.58300835, 0.58671112, 0.59041389, 0.59411664, 0.59781941,\n 0.60152218, 0.60522496, 0.60892772, 0.61263048, 0.61633325,\n 0.62003603, 0.6237388 , 0.62744156, 0.63114433, 0.63484711,\n 0.63854988, 0.64225265, 0.64595543, 0.64965823, 0.653361 ,\n 0.65706377, 0.66076656, 0.66446934, 0.66817212, 0.67187489,\n 0.67557767, 0.67928044, 0.68298322, 0.686686 , 0.69038878,\n 0.69409156, 0.69779433, 0.70149709, 0.70519988, 0.70890264,\n 0.7126054 , 0.71630818, 0.72001095, 0.72371371, 0.72741648,\n 0.73111925, 0.73482204, 0.7385248 , 0.74222757, 0.74593034,\n 0.74963312, 0.75333589, 0.75703868, 0.76074146, 0.76444422,\n 0.76814698, 0.77184976, 0.77555253, 0.77925531, 0.78295807,\n 0.78666085, 0.79036364, 0.79406641, 0.79776918, 0.80147197,\n 0.80517474, 0.80887751, 0.81258028, 0.81628304, 0.81998581,\n 0.82368858, 0.82739136, 0.83109411, 0.83479688, 0.83849965,\n 0.84220242, 0.84590519, 0.84960797, 0.85331075, 0.85701353,\n 0.86071631, 0.86441907, 0.86812186, 0.87182464, 0.87552742,\n 0.87923019, 0.88293296, 0.88663573, 0.89033849, 0.89404126,\n 0.89774404, 0.9014468 , 1. ])],\n array([1.81772748, 1.0828807 , 0.99593794, 0.90023398, 0.79649431,\n 0.73354429, 0.66664314, 0.64137149, 0.59813869, 0.5670836 ,\n 0.54746181, 0.53068399, 0.51304734, 0.49394092, 0.47926274,\n 0.46065259, 0.45992726, 0.43801501, 0.42438665, 0.41150269,\n 0.40033659, 0.38957134, 0.37756538, 0.36292541, 0.34357086,\n 0.3406314 , 0.32299468, 0.31379458, 0.30795386, 0.29207319,\n 0.28697687, 0.27405477, 0.2670497 , 0.25857493, 0.25265783,\n 0.24826777, 0.2414345 , 0.23362778, 0.22956218, 0.22370236,\n 0.22181271, 0.22089651, 0.2194268 , 0.21830064, 0.21845333,\n 0.21753715, 0.21719357, 0.21635373, 0.21667822, 0.21738444,\n 0.21469313, 0.21541846, 0.21465495, 0.2135479 , 0.21392964,\n 0.21074206, 0.20873788, 0.20465319, 0.20205732, 0.19774358,\n 0.19444147, 0.19190285, 0.18850531, 0.18581399, 0.18327537,\n 0.18157659, 0.17814088, 0.17529686, 0.1719375 , 0.16934161,\n 0.16756649, 0.16609676, 0.16414985, 0.16260378, 0.16224113,\n 0.160027 , 0.15827096, 0.1588054 , 0.15552238, 0.15580869,\n 0.15220118, 0.1511132 , 0.14987253, 0.14874637, 0.14678037,\n 0.14620776, 0.14555879, 0.14389819, 0.14359279, 0.14242846,\n 0.14038612, 0.13882096, 0.13954628, 0.13946992, 0.13780934,\n 0.13973714, 0.13698858, 0.13523254, 0.13441178, 0.1352898 ,\n 0.13507985, 0.13647321, 0.13601512, 0.13435452, 0.1334765 ,\n 0.1348317 , 0.13275118, 0.13286571, 0.13263667, 0.13456447,\n 0.13471718, 0.13395369, 0.13448814, 0.1334765 , 0.13298023,\n 0.13259849, 0.13338107, 0.13309476, 0.13275118, 0.13443087,\n 0.13315202, 0.132713 , 0.1330184 , 0.13278936, 0.13225491,\n 0.13317111, 0.13263667, 0.13187316, 0.13265574, 0.13250305,\n 0.13324745, 0.13204496, 0.13242669, 0.13233127, 0.13198769,\n 0.13254122, 0.13145325, 0.13298023, 0.13168229, 0.1313578 ,\n 0.13235036, 0.13120511, 0.13089971, 0.13109058, 0.13082336,\n 0.13011713, 0.129869 , 0.12992626, 0.12942998, 0.12796026,\n 0.12862831, 0.12656689, 0.12734947, 0.12509716, 0.12110791,\n 0.11839751, 0.11244226, 0.11307214, 0.1092165 , 0.10683058,\n 0.10433014, 0.10530359, 0.10056993, 0.09950104, 0.09854668,\n 0.09921473, 0.09541635, 0.09980643, 0.0986612 , 0.09560722,\n 0.09755413, 0.09612258, 0.09430929, 0.09661885, 0.09366032,\n 0.09522548, 0.09535909, 0.09316404, 0.09450016, 0.0930877 ,\n 0.09343126, 0.0932404 , 0.09350762, 0.09339309, 0.09291591,\n 0.09303043, 0.0926296 , 0.0932404 , 0.09261052, 0.09249599,\n 0.09240055, 0.09253416, 0.09209515, 0.09234329, 0.09366032,\n 0.09333583, 0.09322131, 0.09264868, 0.09253416, 0.09243873,\n 0.09230512, 0.09310678, 0.09165615, 0.09159888, 0.09207606,\n 0.09175158, 0.09177067, 0.09236237, 0.09241964, 0.09320222,\n 0.09199972, 0.09167523, 0.09322131, 0.09190428, 0.09167523,\n 0.09285865, 0.09180884, 0.09150345, 0.09186611, 0.0920188 ,\n 0.09320222, 0.09131257, 0.09117896, 0.09133166, 0.09089265,\n 0.09058725, 0.09051091, 0.09033912, 0.09041547, 0.0911217 ,\n 0.0894611 , 0.08999555, 0.08921297, 0.08881213, 0.08797229,\n 0.08709427, 0.08503284, 0.07601531]))),\n 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n 'Negative electrode active material volume fraction': 0.75,\n 'Negative electrode electrons in reaction': 1.0,\n 'Negative electrode exchange-current density [A.m-2]': ,\n 'Negative electrode porosity': 0.25,\n 'Negative electrode thickness [m]': 8.52e-05,\n 'Negative particle diffusivity [m2.s-1]': 3.3e-14,\n 'Negative particle radius [m]': 5.86e-06,\n 'Nominal cell capacity [A.h]': 5.0,\n 'Number of cells connected in series to make a battery': 1.0,\n 'Number of electrodes connected in parallel to make a cell': 1.0,\n 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Positive electrode OCP [V]': ('nmc_LGM50_ocp_Chen2020',\n ([array([0.24879728, 0.26614516, 0.26886763, 0.27159011, 0.27431258,\n 0.27703505, 0.27975753, 0.28248 , 0.28520247, 0.28792495,\n 0.29064743, 0.29336992, 0.29609239, 0.29881487, 0.30153735,\n 0.30425983, 0.30698231, 0.30970478, 0.31242725, 0.31514973,\n 0.3178722 , 0.32059466, 0.32331714, 0.32603962, 0.32876209,\n 0.33148456, 0.33420703, 0.3369295 , 0.33965197, 0.34237446,\n 0.34509694, 0.34781941, 0.3505419 , 0.35326438, 0.35598685,\n 0.35870932, 0.3614318 , 0.36415428, 0.36687674, 0.36959921,\n 0.37232169, 0.37504418, 0.37776665, 0.38048913, 0.38321161,\n 0.38593408, 0.38865655, 0.39137903, 0.39410151, 0.39682398,\n 0.39954645, 0.40226892, 0.4049914 , 0.40771387, 0.41043634,\n 0.41315882, 0.41588129, 0.41860377, 0.42132624, 0.42404872,\n 0.4267712 , 0.42949368, 0.43221616, 0.43493864, 0.43766111,\n 0.44038359, 0.44310607, 0.44582856, 0.44855103, 0.45127351,\n 0.453996 , 0.45671848, 0.45944095, 0.46216343, 0.46488592,\n 0.46760838, 0.47033085, 0.47305333, 0.47577581, 0.47849828,\n 0.48122074, 0.48394321, 0.48666569, 0.48938816, 0.49211064,\n 0.4948331 , 0.49755557, 0.50027804, 0.50300052, 0.50572298,\n 0.50844545, 0.51116792, 0.51389038, 0.51661284, 0.51933531,\n 0.52205777, 0.52478024, 0.52750271, 0.53022518, 0.53294765,\n 0.53567012, 0.53839258, 0.54111506, 0.54383753, 0.54656 ,\n 0.54928247, 0.55200494, 0.5547274 , 0.55744986, 0.56017233,\n 0.5628948 , 0.56561729, 0.56833976, 0.57106222, 0.57378469,\n 0.57650716, 0.57922963, 0.5819521 , 0.58467456, 0.58739702,\n 0.59011948, 0.59284194, 0.5955644 , 0.59828687, 0.60100935,\n 0.60373182, 0.60645429, 0.60917677, 0.61189925, 0.61462172,\n 0.61734419, 0.62006666, 0.62278914, 0.62551162, 0.62823408,\n 0.63095656, 0.63367903, 0.6364015 , 0.63912397, 0.64184645,\n 0.64456893, 0.6472914 , 0.65001389, 0.65273637, 0.65545884,\n 0.65818131, 0.66090379, 0.66362625, 0.66634874, 0.66907121,\n 0.67179369, 0.67451616, 0.67723865, 0.67996113, 0.68268361,\n 0.68540608, 0.68812855, 0.69085103, 0.6935735 , 0.69629597,\n 0.69901843, 0.7017409 , 0.70446338, 0.70718585, 0.70990833,\n 0.71263081, 0.71535328, 0.71807574, 0.72079822, 0.72352069,\n 0.72624317, 0.72896564, 0.7316881 , 0.73441057, 0.73713303,\n 0.73985551, 0.74257799, 0.74530047, 0.74802293, 0.7507454 ,\n 0.75346787, 0.75619034, 0.75891281, 0.76163529, 0.76435776,\n 0.76708024, 0.7698027 , 0.77252517, 0.77524765, 0.77797012,\n 0.78069258, 0.78341506, 0.78613753, 0.78885999, 0.79158246,\n 0.79430494, 0.79702741, 0.79974987, 0.80247234, 0.8051948 ,\n 0.80791727, 0.81063974, 0.81336221, 0.81608468, 0.81880714,\n 0.82152961, 0.82425208, 0.82697453, 0.829697 , 0.83241946,\n 0.83514192, 0.83786439, 0.84058684, 0.84330931, 0.84603177,\n 0.84875424, 0.8514767 , 0.85419916, 0.85692162, 0.85964409,\n 0.86236656, 0.86508902, 0.86781149, 0.87053395, 0.87325642,\n 0.87597888, 0.87870135, 0.88142383, 0.8841463 , 0.88686877,\n 0.88959124, 0.89231371, 0.8950362 , 0.89775868, 0.90048116,\n 0.90320364, 0.90592613, 1. ])],\n array([4.4 , 4.2935653 , 4.2768621 , 4.2647018 , 4.2540312 ,\n 4.2449446 , 4.2364879 , 4.2302647 , 4.2225528 , 4.2182574 ,\n 4.213294 , 4.2090373 , 4.2051239 , 4.2012677 , 4.1981564 ,\n 4.1955218 , 4.1931167 , 4.1889744 , 4.1881533 , 4.1865883 ,\n 4.1850228 , 4.1832285 , 4.1808805 , 4.1805749 , 4.1789522 ,\n 4.1768146 , 4.1768146 , 4.1752872 , 4.173111 , 4.1726718 ,\n 4.1710877 , 4.1702285 , 4.168797 , 4.1669831 , 4.1655135 ,\n 4.1634517 , 4.1598248 , 4.1571712 , 4.154079 , 4.1504135 ,\n 4.1466532 , 4.1423388 , 4.1382346 , 4.1338248 , 4.1305799 ,\n 4.1272392 , 4.1228104 , 4.1186109 , 4.114182 , 4.1096005 ,\n 4.1046948 , 4.1004758 , 4.0956464 , 4.0909696 , 4.0864644 ,\n 4.0818448 , 4.077683 , 4.0733309 , 4.0690737 , 4.0647216 ,\n 4.0608654 , 4.0564747 , 4.0527525 , 4.0492401 , 4.0450211 ,\n 4.041986 , 4.0384736 , 4.035171 , 4.0320406 , 4.0289288 ,\n 4.02597 , 4.0227437 , 4.0199757 , 4.0175133 , 4.0149746 ,\n 4.0122066 , 4.009954 , 4.0075679 , 4.0050669 , 4.0023184 ,\n 3.9995501 , 3.9969349 , 3.9926589 , 3.9889555 , 3.9834003 ,\n 3.9783037 , 3.9755929 , 3.9707632 , 3.9681098 , 3.9635665 ,\n 3.9594433 , 3.9556634 , 3.9521511 , 3.9479132 , 3.9438281 ,\n 3.9400866 , 3.9362304 , 3.9314201 , 3.9283848 , 3.9242232 ,\n 3.9192028 , 3.9166257 , 3.9117961 , 3.90815 , 3.9038739 ,\n 3.8995597 , 3.8959136 , 3.8909314 , 3.8872662 , 3.8831048 ,\n 3.8793442 , 3.8747628 , 3.8702576 , 3.8666878 , 3.8623927 ,\n 3.8581741 , 3.854146 , 3.8499846 , 3.8450022 , 3.8422534 ,\n 3.8380919 , 3.8341596 , 3.8309333 , 3.8272109 , 3.823164 ,\n 3.8192315 , 3.8159864 , 3.8123021 , 3.8090379 , 3.8071671 ,\n 3.8040555 , 3.8013639 , 3.7970879 , 3.7953317 , 3.7920673 ,\n 3.788383 , 3.7855389 , 3.7838206 , 3.78111 , 3.7794874 ,\n 3.7769294 , 3.773608 , 3.7695992 , 3.7690265 , 3.7662776 ,\n 3.7642922 , 3.7626889 , 3.7603791 , 3.7575538 , 3.7552056 ,\n 3.7533159 , 3.7507198 , 3.7487535 , 3.7471499 , 3.7442865 ,\n 3.7423012 , 3.7400677 , 3.7385788 , 3.7345319 , 3.7339211 ,\n 3.7301605 , 3.7301033 , 3.7278316 , 3.7251589 , 3.723861 ,\n 3.7215703 , 3.7191267 , 3.7172751 , 3.7157097 , 3.7130945 ,\n 3.7099447 , 3.7071004 , 3.7045615 , 3.703588 , 3.70208 ,\n 3.7002664 , 3.6972122 , 3.6952841 , 3.6929362 , 3.6898055 ,\n 3.6890991 , 3.686522 , 3.6849759 , 3.6821697 , 3.6808143 ,\n 3.6786573 , 3.6761947 , 3.674763 , 3.6712887 , 3.6697233 ,\n 3.6678908 , 3.6652565 , 3.6630611 , 3.660274 , 3.6583652 ,\n 3.6554828 , 3.6522949 , 3.6499848 , 3.6470451 , 3.6405547 ,\n 3.6383405 , 3.635076 , 3.633549 , 3.6322317 , 3.6306856 ,\n 3.6283948 , 3.6268487 , 3.6243098 , 3.6223626 , 3.6193655 ,\n 3.6177621 , 3.6158531 , 3.6128371 , 3.6118062 , 3.6094582 ,\n 3.6072438 , 3.6049912 , 3.6030822 , 3.6012688 , 3.5995889 ,\n 3.5976417 , 3.5951984 , 3.593843 , 3.5916286 , 3.5894907 ,\n 3.587429 , 3.5852909 , 3.5834775 , 3.5817785 , 3.5801177 ,\n 3.5778842 , 3.5763381 , 3.5737801 , 3.5721002 , 3.5702102 ,\n 3.5684922 , 3.5672133 , 3.52302167]))),\n 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n 'Positive electrode active material volume fraction': 0.665,\n 'Positive electrode electrons in reaction': 1.0,\n 'Positive electrode exchange-current density [A.m-2]': ,\n 'Positive electrode porosity': 0.335,\n 'Positive electrode thickness [m]': 7.56e-05,\n 'Positive particle diffusivity [m2.s-1]': 4e-15,\n 'Positive particle radius [m]': 5.22e-06,\n 'Reference temperature [K]': 298.15,\n 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n 'Separator porosity': 0.47,\n 'Separator thickness [m]': 1.2e-05,\n 'Typical current [A]': 5.0,\n 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n 'Upper voltage cut-off [V]': 4.4}" }, - "execution_count": 16, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -1144,7 +1149,7 @@ " \"Typical current [A]\": 5.0,\n", " \"Current function [A]\": 5.0,\n", " \"Maximum concentration in negative electrode [mol.m-3]\": 33133.0,\n", - " \"Negative electrode diffusivity [m2.s-1]\": 3.3e-14,\n", + " \"Negative particle diffusivity [m2.s-1]\": 3.3e-14,\n", " \"Negative electrode OCP [V]\": (\"graphite_LGM50_ocp_Chen2020\", neg_ocp),\n", " \"Negative electrode porosity\": 0.25,\n", " \"Negative electrode active material volume fraction\": 0.75,\n", @@ -1155,7 +1160,7 @@ " \"Negative electrode exchange-current density [A.m-2]\": graphite_LGM50_electrolyte_exchange_current_density_Chen2020,\n", " \"Negative electrode OCP entropic change [V.K-1]\": 0.0,\n", " \"Maximum concentration in positive electrode [mol.m-3]\": 63104.0,\n", - " \"Positive electrode diffusivity [m2.s-1]\": 4e-15,\n", + " \"Positive particle diffusivity [m2.s-1]\": 4e-15,\n", " \"Positive electrode OCP [V]\": (\"nmc_LGM50_ocp_Chen2020\", pos_ocp),\n", " \"Positive electrode porosity\": 0.335,\n", " \"Positive electrode active material volume fraction\": 0.665,\n", @@ -1193,19 +1198,19 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 63, "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T12:14:19.608102800Z", - "start_time": "2023-12-10T12:14:19.450757200Z" + "end_time": "2024-03-05T17:18:14.841689Z", + "start_time": "2024-03-05T17:18:14.682137Z" } }, "outputs": [ { "data": { - "text/plain": "{'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Negative electrode thickness [m]': 8.52e-05,\n 'Separator thickness [m]': 1.2e-05,\n 'Positive electrode thickness [m]': 7.56e-05,\n 'Electrode height [m]': 0.065,\n 'Electrode width [m]': 1.58,\n 'Nominal cell capacity [A.h]': 5.0,\n 'Current function [A]': 5.0,\n 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n 'Negative electrode OCP [V]': ,\n 'Negative electrode porosity': 0.25,\n 'Negative electrode active material volume fraction': 0.75,\n 'Negative particle radius [m]': 5.86e-06,\n 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Negative electrode Bruggeman coefficient (electrode)': 0,\n 'Negative electrode exchange-current density [A.m-2]': ,\n 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n 'Positive electrode OCP [V]': ,\n 'Positive electrode porosity': 0.335,\n 'Positive electrode active material volume fraction': 0.665,\n 'Positive particle radius [m]': 5.22e-06,\n 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Positive electrode Bruggeman coefficient (electrode)': 0,\n 'Positive electrode exchange-current density [A.m-2]': ,\n 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n 'Separator porosity': 0.47,\n 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n 'Reference temperature [K]': 298.15,\n 'Ambient temperature [K]': 298.15,\n 'Number of electrodes connected in parallel to make a cell': 1.0,\n 'Number of cells connected in series to make a battery': 1.0,\n 'Lower voltage cut-off [V]': 2.5,\n 'Upper voltage cut-off [V]': 4.2,\n 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n 'Initial concentration in positive electrode [mol.m-3]': 17038.0}" + "text/plain": "{'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Negative electrode thickness [m]': 8.52e-05,\n 'Separator thickness [m]': 1.2e-05,\n 'Positive electrode thickness [m]': 7.56e-05,\n 'Electrode height [m]': 0.065,\n 'Electrode width [m]': 1.58,\n 'Nominal cell capacity [A.h]': 5.0,\n 'Current function [A]': 5.0,\n 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n 'Negative particle diffusivity [m2.s-1]': 3.3e-14,\n 'Negative electrode OCP [V]': ,\n 'Negative electrode porosity': 0.25,\n 'Negative electrode active material volume fraction': 0.75,\n 'Negative particle radius [m]': 5.86e-06,\n 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Negative electrode Bruggeman coefficient (electrode)': 0,\n 'Negative electrode exchange-current density [A.m-2]': ,\n 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n 'Positive particle diffusivity [m2.s-1]': 4e-15,\n 'Positive electrode OCP [V]': ,\n 'Positive electrode porosity': 0.335,\n 'Positive electrode active material volume fraction': 0.665,\n 'Positive particle radius [m]': 5.22e-06,\n 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Positive electrode Bruggeman coefficient (electrode)': 0,\n 'Positive electrode exchange-current density [A.m-2]': ,\n 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n 'Separator porosity': 0.47,\n 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n 'Reference temperature [K]': 298.15,\n 'Ambient temperature [K]': 298.15,\n 'Number of electrodes connected in parallel to make a cell': 1.0,\n 'Number of cells connected in series to make a battery': 1.0,\n 'Lower voltage cut-off [V]': 2.5,\n 'Upper voltage cut-off [V]': 4.2,\n 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n 'Initial concentration in positive electrode [mol.m-3]': 17038.0}" }, - "execution_count": 17, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -1241,11 +1246,11 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 64, "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T12:14:19.611194400Z", - "start_time": "2023-12-10T12:14:19.609138100Z" + "end_time": "2024-03-05T17:18:14.847825Z", + "start_time": "2024-03-05T17:18:14.842707Z" } }, "outputs": [ @@ -1260,7 +1265,7 @@ "data": { "text/plain": "4.0" }, - "execution_count": 18, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -1283,11 +1288,11 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 65, "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T12:14:19.641429500Z", - "start_time": "2023-12-10T12:14:19.616345800Z" + "end_time": "2024-03-05T17:18:14.856575Z", + "start_time": "2024-03-05T17:18:14.849104Z" } }, "outputs": [ @@ -1295,7 +1300,7 @@ "data": { "text/plain": "" }, - "execution_count": 19, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -1330,18 +1335,18 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 66, "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T12:14:19.700673700Z", - "start_time": "2023-12-10T12:14:19.627406900Z" + "end_time": "2024-03-05T17:18:14.931974Z", + "start_time": "2024-03-05T17:18:14.857726Z" } }, "outputs": [ { "data": { - "image/png": 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", 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" }, "metadata": {}, "output_type": "display_data" @@ -1350,7 +1355,7 @@ "data": { "text/plain": "" }, - "execution_count": 20, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -1375,18 +1380,18 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 67, "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T12:14:19.785875100Z", - "start_time": "2023-12-10T12:14:19.699175500Z" + "end_time": "2024-03-05T17:18:15.004549Z", + "start_time": "2024-03-05T17:18:14.933554Z" } }, "outputs": [ { "data": { - "image/png": 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", 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" }, "metadata": {}, "output_type": "display_data" @@ -1395,7 +1400,7 @@ "data": { "text/plain": "" }, - "execution_count": 21, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } @@ -1425,31 +1430,31 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 68, "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T12:14:21.137222900Z", - "start_time": "2023-12-10T12:14:19.775429Z" + "end_time": "2024-03-05T17:18:16.333348Z", + "start_time": "2024-03-05T17:18:15.006083Z" } }, "outputs": [ { "data": { + "text/plain": "interactive(children=(FloatSlider(value=0.0, description='t', max=3599.0, step=35.99), Output()), _dom_classes…", "application/vnd.jupyter.widget-view+json": { - "model_id": "e3e2a10c3de140de8cc785ae5421b534", "version_major": 2, - "version_minor": 0 - }, - "text/plain": "interactive(children=(FloatSlider(value=0.0, description='t', max=3599.0, step=35.99), Output()), _dom_classes…" + "version_minor": 0, + "model_id": "986fc61ae451470d9def1d54fd2288dd" + } }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "" + "text/plain": "" }, - "execution_count": 22, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -1472,11 +1477,11 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 69, "metadata": { "ExecuteTime": { - "end_time": "2023-12-10T12:14:21.184199300Z", - "start_time": "2023-12-10T12:14:21.136110400Z" + "end_time": "2024-03-05T17:18:16.354429Z", + "start_time": "2024-03-05T17:18:16.334463Z" } }, "outputs": [ diff --git a/docs/source/examples/notebooks/plotting/plot-voltage-components.ipynb b/docs/source/examples/notebooks/plotting/plot-voltage-components.ipynb index bab1b8093e..be0706bbfe 100644 --- a/docs/source/examples/notebooks/plotting/plot-voltage-components.ipynb +++ b/docs/source/examples/notebooks/plotting/plot-voltage-components.ipynb @@ -179,7 +179,7 @@ } ], "source": [ - "pybamm.plot_voltage_components(sol, split_by_electrode=True)" + "sol.plot_voltage_components(split_by_electrode=True)" ] }, { @@ -217,7 +217,7 @@ } ], "source": [ - "pybamm.plot_voltage_components(sol)" + "sol.plot_voltage_components()" ] }, { @@ -254,8 +254,7 @@ "[6] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", "[7] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n", "[8] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", - "[9] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", - "\n" + "[9] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n" ] } ], diff --git a/docs/source/examples/notebooks/pybamm_data.ipynb b/docs/source/examples/notebooks/pybamm_data.ipynb new file mode 100644 index 0000000000..b7111ed996 --- /dev/null +++ b/docs/source/examples/notebooks/pybamm_data.ipynb @@ -0,0 +1,438 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8bcfdcc0-07bc-4a0b-90dc-84444e5f65d5", + "metadata": {}, + "source": [ + "# PyBaMM DataLoader\n", + "\n", + "This notebook is a reference for using pybamm.DataLoader module for using and fetching data files from the pybamm-data registry.\n", + "Checkout the [documentation](../../api/pybamm_data.rst) for further implementation details on this module.\n", + "\n", + "The following steps provide an example for using pybamm.DataLoader to download data files from PyBaMM data registry upstream at [pybamm-data](https://github.com/pybamm-team/pybamm-data/releases/tag/v1.0.0).\n" + ] + }, + { + "cell_type": "markdown", + "id": "5050f302-5246-43ca-a3cd-a50570ad2983", + "metadata": {}, + "source": [ + "### 1. Creating DataLoader instance and listing data files present in the registry." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c641a158-388a-4ebb-8be5-7f736c3be159", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" + ] + }, + { + "data": { + "text/plain": [ + "['comsol_01C.json',\n", + " 'comsol_05C.json',\n", + " 'comsol_1C.json',\n", + " 'comsol_1plus1D_3C.json',\n", + " 'comsol_2C.json',\n", + " 'comsol_3C.json',\n", + " 'Ecker_1C.csv',\n", + " 'Ecker_5C.csv',\n", + " '0.1C_discharge_U.txt',\n", + " '0.1C_discharge_displacement.txt',\n", + " '0.5C_discharge_T.txt',\n", + " '0.5C_discharge_U.txt',\n", + " '0.5C_discharge_displacement.txt',\n", + " '1C_discharge_T.txt',\n", + " '1C_discharge_U.txt',\n", + " '1C_discharge_displacement.txt',\n", + " '2C_discharge_T.txt',\n", + " '2C_discharge_U.txt',\n", + " '2C_discharge_displacement.txt',\n", + " 'stn_2C.txt',\n", + " 'stp_2C.txt',\n", + " 'UDDS.csv',\n", + " 'US06.csv',\n", + " 'WLTC.csv',\n", + " 'car_current.csv']" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%pip install \"pybamm[plot,cite]\" -q # install PyBaMM if it is not installed\n", + "import pybamm\n", + "\n", + "data_loader = pybamm.DataLoader()\n", + "data_loader.show_registry()" + ] + }, + { + "cell_type": "markdown", + "id": "2bf77f88-3ab0-475a-84e0-cd0b8d3028a4", + "metadata": {}, + "source": [ + "### 2. Listing data files along with their checksums" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "50addccb-a1d0-48e0-9510-6697702f7eaa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'comsol_01C.json': 'sha256:bc5136fe961e269453bdc31fcaa97376d6f8c347d570fd30ce4b7660c68ae22c',\n", + " 'comsol_05C.json': 'sha256:3b044135ad88bdb88959304a33fe42b654d5ef7ef79d1271dd909cec55b257fb',\n", + " 'comsol_1C.json': 'sha256:d45e3ab482c497c37ebbc68898da22bab0b0263992d8f2302502028bfd5ba0e9',\n", + " 'comsol_1plus1D_3C.json': 'sha256:cdd5759202f9c7887d2ea6032f82212f2ca89297191fe5282b8812e1a09b1e1f',\n", + " 'comsol_2C.json': 'sha256:15c2637f54bf1639621c58795db859cb08611c8182b7b20ade10e4c3e2839a5b',\n", + " 'comsol_3C.json': 'sha256:11d5afccb70be85d4ac7e61d413c6e0f5e318e1635b1347c9a3c6784119711e6',\n", + " 'Ecker_1C.csv': 'sha256:428dc5113a6430492f430fb9e895f67d3e20f5643dc49a1cc0a922b92a5a8e01',\n", + " 'Ecker_5C.csv': 'sha256:a89f8bf6e305b2a4195e1fae5e803277a40ed7557d263ef726f621803dcbb495',\n", + " '0.1C_discharge_U.txt': 'sha256:7b9fcd137441eea4ab686faee8d57fe242c5544400939ef358ccd99c63c9579d',\n", + " '0.1C_discharge_displacement.txt': 'sha256:f1329731ead5a82a2be9851cf80e4c6d68dd0774e07aee5361e2af3ab420d7be',\n", + " '0.5C_discharge_T.txt': 'sha256:2140b2f6bd698135d09a25b1f04c271d35a3a02999ace118b10389e01defa2ae',\n", + " '0.5C_discharge_U.txt': 'sha256:9ed8368b2c6149d2a69218e7df6aaade2511c9f7f6fc7932cda153d9a3a10f39',\n", + " '0.5C_discharge_displacement.txt': 'sha256:8098565ff99bc938864797b402f483c1c64a583d6db85d086f39ab0e7b638dd1',\n", + " '1C_discharge_T.txt': 'sha256:97308dfd7f7dd6c434e30f6c00fb6707c43c963855bb0800e0336809d5cc3756',\n", + " '1C_discharge_U.txt': 'sha256:8fc19de45172215d65c56522c224e6fc700ee443db236b814238a829b7a14c3a',\n", + " '1C_discharge_displacement.txt': 'sha256:c2e8617ac48a20921da1b40bbebac479a0a143edf16b12b2e1ff9aaaf1a32ff4',\n", + " '2C_discharge_T.txt': 'sha256:4bd688fb7653539701fe3df61857474b4d54e8b142c84fdc4c8b92b9573fa5d0',\n", + " '2C_discharge_U.txt': 'sha256:7b3c24b5e6df377075002abc2f62bab7c88b27d826812ba5a4c8385a1a12e723',\n", + " '2C_discharge_displacement.txt': 'sha256:2b11513d80827c762325c819a084b87b3a239af7d112f234c9871481760a0013',\n", + " 'stn_2C.txt': 'sha256:bb2f90ccfd2cd86ad589287caae13470e554df2f4f47f0f583a5a7e3e6bd9d4c',\n", + " 'stp_2C.txt': 'sha256:6fe73b3a18e5fcfb95151dfd7d34c3cbe929792631447ed3ec88c047c9778223',\n", + " 'UDDS.csv': 'sha256:9fe6558c17aad3cc08109186923aeb7459cd3097a381c44e854bf22dd12a5a2a',\n", + " 'US06.csv': 'sha256:5909eb2ec7983fae86a050ff3b35a2041d0ab698710a6b0f95d5816e348077ba',\n", + " 'WLTC.csv': 'sha256:bb2f95018a44ac1425cb9c787c34721192af502c7385f1358f28e4f75df11fd8',\n", + " 'car_current.csv': 'sha256:4305b91b9df073cb048c25dd3fae725e06a94fe200e322e5c08db290d6799e36'}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_loader.files" + ] + }, + { + "cell_type": "markdown", + "id": "47c826d1-37ed-4d9f-95f1-c919902f04d6", + "metadata": {}, + "source": [ + "### 3. Fetching a file from upstream and storing it in local cache folder" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "757aa66c-b8f3-4aa9-86af-8c9355bb7be1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PosixPath('/home/santa/.cache/pybamm/v1.0.0/Ecker_1C.csv')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_loader.get_data(\"Ecker_1C.csv\")" + ] + }, + { + "cell_type": "markdown", + "id": "8d667cce-43ab-4079-ac16-2b8969294690", + "metadata": {}, + "source": [ + "### 4. Loading a file from cache into python code" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6661ce52-1fbe-4546-a00a-8f96c3dca95c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "pd.read_csv(data_loader.get_data(\"Ecker_1C.csv\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6737e5a9-714a-48eb-a51a-cc41aaa9c231", + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/examples/notebooks/simulations_and_experiments/custom-experiments.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/custom-experiments.ipynb index 4dfa8c8c72..b77ceec3ce 100644 --- a/docs/source/examples/notebooks/simulations_and_experiments/custom-experiments.ipynb +++ b/docs/source/examples/notebooks/simulations_and_experiments/custom-experiments.ipynb @@ -25,15 +25,6 @@ "import pybamm" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Custom steps\n", - "\n", - "This feature is in development" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -52,7 +43,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -115,7 +106,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -124,7 +115,7 @@ }, { "data": { - "image/png": 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" ] @@ -136,12 +127,7 @@ "source": [ "# Plot\n", "plot = pybamm.QuickPlot(\n", - " sim.solution,\n", - " [\n", - " \"Current [A]\",\n", - " \"Voltage [V]\",\n", - " \"Anode potential [V]\",\n", - " ],\n", + " sim.solution, [\"Current [A]\", \"Voltage [V]\", \"Anode potential [V]\"]\n", ")\n", "plot.plot(0)\n", "\n", @@ -178,10 +164,268 @@ " print(f\"Step {i}: {step.termination}\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Custom steps\n", + "\n", + "Custom steps can be defined using either explicit or implicit control. In explicit control, the user specifies the current explicitly as a function of other variables in the model. " + ] + }, { "cell_type": "code", "execution_count": 5, "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def custom_step_power(variables):\n", + " target_power = 4\n", + " voltage = variables[\"Voltage [V]\"]\n", + " return target_power / voltage\n", + "\n", + "\n", + "# Run for 10 minutes and plot\n", + "step = pybamm.step.CustomStepExplicit(custom_step_power, duration=600)\n", + "sol = pybamm.Simulation(\n", + " model, experiment=step, parameter_values=parameter_values\n", + ").solve()\n", + "pybamm.QuickPlot(sol, [\"Current [A]\", \"Voltage [V]\", \"Power [W]\"]).plot(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For custom steps to work with voltage-based termination, we have to specify whether the step is a charge or discharge step. This is done by setting the `direction` attribute of the step to either \"charge\" or \"discharge\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "step = pybamm.step.CustomStepExplicit(\n", + " custom_step_power, termination=\"2.5V\", direction=\"discharge\"\n", + ")\n", + "sol = pybamm.Simulation(\n", + " model, experiment=step, parameter_values=parameter_values\n", + ").solve()\n", + "pybamm.QuickPlot(sol, [\"Current [A]\", \"Voltage [V]\", \"Power [W]\"]).plot(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Be careful not to create an expression that depends on the current itself, as this will lead to a circular dependency. For example, in some models, the voltage is an explicit function of the current, so the user should not create a step that depends on the voltage. An expression that works for one model may not work for another." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also specify a custom step using implicit control. This comes with \"algebraic\" or \"differential\" control. In algebraic control (the default), the user specifies the equation that must be satisfied at all times, and the model adjusts the current to satisfy this equation. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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UKCgoyGlfcHCw1q1b57QvKytL7dq1U9euXXXXXXcpOzvbY/0GALifZUNFSJs2UkyM+YCrFQBgGdu2bVNoaKjsdrumTJmiZcuWqXfv3tW2HTlypF5++WVlZWWpoqJCq1at0tKlS5WTk+NoM2TIEC1cuFArV65Uamqq9u/fr6uvvlqnTp2qsQ8lJSUqKChw2iQpJCTEvW8WAHBRLBsqJEmVA/UYVwEAlhEXF6fMzExlZGRo6tSpmjBhgnbs2FFt27lz56p79+7q2bOnAgMD9cADD2jSpEny8ztXfkaNGqXbbrtN8fHxGjlypJYvX66TJ0/qH//4R419SElJUXh4uGOLqfwlFADAK3wjVHClAgAsIzAwULGxsRo0aJBSUlLUv39/zZ07t9q2bdq00UcffaSioiJ9//33+u677xQaGqqulbe4ViMiIkI9evTQnj17amyTnJys/Px8x3bw4MF6vy8AQN1ZNlQUFxdzpQIAfEBFRYVKSkpqbRMUFKT27durrKxMH374oUaPHl1j28LCQu3du1fR0dE1trHb7Y5pbSs36b+1AwDQ4Cw7+1N5ebkUF2c+IFQAgCUkJydr1KhR6tixo06dOqXFixcrPT1daWlpkqTx48erffv2SklJkSRlZGTo8OHDuvTSS3X48GE9/fTTqqio0KOPPuo45yOPPKKkpCR16tRJR44c0VNPPSV/f3+NHTvW5f6Vl5e7540CAFxi2VAh6dyVir17pdJSKTDQu/0BgCYuLy9P48ePV05OjsLDwxUfH6+0tDQNHz5ckpSdne00XqK4uFizZs3Svn37FBoaqptuukl/+9vfFBER4Whz6NAhjR07VsePH1ebNm101VVXacOGDWrTpk1Dvz0AQB3ZDMMwvN2J8xUUFCg8PFxHjhxRdFSUFBYmFRZKO3ZIvXp5u3sAYAmV35X5+fmOW3+aMqfaUcttUwDQlHmydlh2TIUkyWZjsDYAAABgcdYOFRKDtQEAAACLs36oYLA2AAAAYGkuhYqUlBQNHjxYLVq0UNu2bTVmzBjt+sltScOGDZPNZnPapkyZUvcecqUCAAAAsDSXQsXatWs1bdo0bdiwQatWrdLZs2c1YsQIFRUVObX79a9/rZycHMf24osvutyxkJAQ84fzQ4W1xpQDACzGUTsAAA3KpSllV65c6fR44cKFatu2rbZs2aJrrrnGsb958+aKiopyTw9jYyU/Pyk/X8rLkyIj3XNeAAAAAG5RrzEV+fn5kqSWLVs67X/33XfVunVr9e3bV8nJyTp9+nTdXyQoSOrSxfyZW6AAAAAAy6nz4ncVFRWaMWOGhg4dqr59+zr233nnnerUqZPatWunb775Ro899ph27dqlpUuXVnuekpISlZSUOB4XFBRIMhdMcsyfGxdnLoD33XfStdfWtcsAgEbOqXYAABpMnUPFtGnTtH37dq1bt85p/3333ef4uV+/foqOjtYNN9ygvXv3qlu3blXOk5KSomeeeabK/vLy8nMPevaUli/nSgUAoFZOtQMA0GDqdPvTAw88oH//+99as2aNOnToUGvbIUOGSJL27NlT7fHk5GTl5+c7toMHD1ZtxAxQAAAAgGW5dKXCMAxNnz5dy5YtU3p6urpUjnWoRWZmpiQpOjq62uN2u112u732k7CqNgAAAGBZLoWKadOmafHixfrnP/+pFi1aKDc3V5IUHh6u4OBg7d27V4sXL9ZNN92kVq1a6ZtvvtFDDz2ka665RvHx8XXvZWWoOHBAOnNGCg6u+7kAAAAAuJVLtz+lpqYqPz9fw4YNU3R0tGN77733JEmBgYH65JNPNGLECPXs2VMPP/ywbr31Vv3rX/+qXy9bt5YuucRcpyIrq37nAgAAAOBWLt/+VJuYmBitXbu2Xh2qls1mXq1Yv94cV1Gfqx4AAAAA3Kpe61Q0KAZrAwAAAJZk2VDRvHlz5x0M1gYAXECV2gEAaBCWDRU2m815R1yc+SdXKgAANahSOwAADcKyoaKK829/qqjwbl8AAAAAOFg2VJSUlDjv6NpVCgiQTp+WDh/2TqcAAJZWpXYAABqEZUNFWVmZ845mzaTYWPNnboECAFSjSu0AADQIy4aKajFYGwAAALAc3woVDNYGAAAALMe3QgVrVQAAAACWQ6gAAAAAUC++FSoqb386fFg6dcq7fQEAAAAgyddCxSWXSJGR5s+7d3u3LwAAAAAkWThUNG/evPoDDNYGANSgxtoBAPAoy4YKm81W/QHGVQAAalBj7QAAeJRlQ0WNCBUAAACApVg2VJSUlFR/gAXwAAA1qLF2AAA8yrKhoqysrPoDlaFi926pvLzhOgQAUGpqquLj4xUWFqawsDAlJCRoxYoVNbY/e/asnn32WXXr1k1BQUHq37+/Vq5cWaXdvHnz1LlzZwUFBWnIkCHauHFjnfpXY+0AAHiUZUNFjTp2lOx2qaRE+v57b/cGAJqUDh06aM6cOdqyZYs2b96s66+/XqNHj9a3335bbftZs2bpzTff1J///Gft2LFDU6ZM0S233KKtW7c62rz33nuaOXOmnnrqKX311Vfq37+/Ro4cqby8vIZ6WwCAerIZhmF4uxPnKygoUHh4uI4cOaLo6OjqG8XHS9u2Sf/3f9JNNzVsBwHAAiq/K/Pz8xUWFubVvrRs2VIvvfSSJk+eXOVYu3bt9MQTT2jatGmOfbfeequCg4P1zjvvSJKGDBmiwYMH67XXXpMkVVRUKCYmRtOnT9fjjz9+UX24qNoBAE2cJ2uH712pkBisDQAWUF5eriVLlqioqEgJCQnVtikpKVFQUJDTvuDgYK1bt06SVFpaqi1btigxMdFx3M/PT4mJiVq/fn2Nr11SUqKCggKnDQDgPb4dKhisDQANbtu2bQoNDZXdbteUKVO0bNky9e7du9q2I0eO1Msvv6ysrCxVVFRo1apVWrp0qXJyciRJx44dU3l5uSIrFzb9r8jISOXm5tbYh5SUFIWHhzu2mJgY971BAIDLfDtUcKUCABpcXFycMjMzlZGRoalTp2rChAnasWNHtW3nzp2r7t27q2fPngoMDNQDDzygSZMmyc+vfuUnOTlZ+fn5ju3gwYP1Oh8AoH58M1SwqjYAeE1gYKBiY2M1aNAgpaSkqH///po7d261bdu0aaOPPvpIRUVF+v777/Xdd98pNDRUXbt2lSS1bt1a/v7+Onr0qNPzjh49qqioqBr7YLfbHTNQVW4AAO+xbKgIDg6u+WBlqMjLk06caJgOAQCqVVFRccH1IYKCgtS+fXuVlZXpww8/1OjRoyWZAWXQoEFavXq10/lWr15d4ziN2tRaOwAAHhPg7Q7UpNZL46GhUocO0qFD5riKOhQeAIDrkpOTNWrUKHXs2FGnTp3S4sWLlZ6errS0NEnS+PHj1b59e6WkpEiSMjIydPjwYV166aU6fPiwnn76aVVUVOjRRx91nHPmzJmaMGGCLrvsMl1++eV65ZVXVFRUpEmTJrncv/reVgUAqBvLhooL6tmTUAEADSwvL0/jx49XTk6OwsPDFR8fr7S0NA0fPlySlJ2d7fQP++LiYs2aNUv79u1TaGiobrrpJv3tb39TRESEo82vfvUr/fDDD5o9e7Zyc3N16aWXauXKlVUGbwMArMuy61T88MMPat26dc0NH3hAmjdPeuwxac6chusgAFiAldapsIKLrh0A0IQ1yXUqzp49W3sDZoACAPzEBWsHAMAjLBsqLohQAQAAAFiC74eKvXslfjMFAAAAeI3vhor27aWQEKmsTNq3z9u9AQAAAJos3w0VNhuL4AEAAAAW4LuhQmJcBQAAAGABhAoAAAAA9WLZUBEcHHzhRoQKAMB5Lqp2AADczrKh4vwVWWtUGSp27ZKstYYfAMALLqp2AADczre/fWNjzQHbP/4o/fCDt3sDAAAANEmWDRWlpaUXbhQcLHXubP7MLVAA0ORdVO0AALidZUPF2Ytd0I5xFQCA/7ro2gEAcCvLhoqLdv64CgAAAAANrvGECq5UAAAAAF7h+6GCVbUBAAAAr/L9UFF5pWL/fqm42Lt9AQAAAJog3w8VbdtKERHmOhVZWd7uDQAAANDk+H6osNkYrA0AAAB4kWVDRVBQ0MU3ZlwFAEAu1g4AgNtYNlT4+/tffGNmgAIAyMXaAQBwG8uGCpcQKgAAAACvcSlUpKSkaPDgwWrRooXatm2rMWPGaNdPxjEUFxdr2rRpatWqlUJDQ3Xrrbfq6NGjLnestLT04hufP6bCMFx+LQBA4+BS7QAAuI1LoWLt2rWaNm2aNmzYoFWrVuns2bMaMWKEioqKHG0eeugh/etf/9L777+vtWvX6siRI/rFL37hcsfOnj178Y27dZMCAqTCQunIEZdfCwDQOLhUOwAAbhPgSuOVK1c6PV64cKHatm2rLVu26JprrlF+fr7mz5+vxYsX6/rrr5ckLViwQL169dKGDRt0xRVXuK/n52vWTOraVdq927wFqn17z7wOAAAAgCrqNaYiPz9fktSyZUtJ0pYtW3T27FklJiY62vTs2VMdO3bU+vXrqz1HSUmJCgoKnLY6YVwFAAAA4BV1DhUVFRWaMWOGhg4dqr59+0qScnNzFRgYqIiICKe2kZGRys3NrfY8KSkpCg8Pd2wxMTF16xChAgAAAPCKOoeKadOmafv27VqyZEm9OpCcnKz8/HzHdvDgwbqdiFABAAAAeIVLYyoqPfDAA/r3v/+tzz77TB06dHDsj4qKUmlpqU6ePOl0teLo0aOKioqq9lx2u112u70u3XDGqtoAAACAV7h0pcIwDD3wwANatmyZPv30U3Xp0sXp+KBBg9SsWTOtXr3asW/Xrl3Kzs5WQkKCe3pck8pVtQ8eNGeBAgAAANAgXAoV06ZN0zvvvKPFixerRYsWys3NVW5urs6cOSNJCg8P1+TJkzVz5kytWbNGW7Zs0aRJk5SQkODyzE9BQUEutVfLllKbNubPu3e79lwAwEVJTU1VfHy8wsLCFBYWpoSEBK1YsaLW57zyyiuKi4tTcHCwYmJi9NBDD6m4uNhx/Omnn5bNZnPaelZefXaRy7UDAOAWLt3+lJqaKkkaNmyY0/4FCxZo4sSJkqQ//elP8vPz06233qqSkhKNHDlSr7/+ussd8/f3d/k56tlT+uEHc1zFwIGuPx8AUKsOHTpozpw56t69uwzD0KJFizR69Ght3bpVffr0qdJ+8eLFevzxx/XWW2/pyiuv1O7duzVx4kTZbDa9/PLLjnZ9+vTRJ5984ngcEFCnu3PrVjsAAPXm0re2cRGrVQcFBWnevHmaN29enTtVZz17Sp9/zmBtAPCQpKQkp8fPP/+8UlNTtWHDhmpDxZdffqmhQ4fqzjvvlCR17txZY8eOVUZGhlO7gICAGsfeAQCsr17rVHhSnVZFZbA2ADSY8vJyLVmyREVFRTWOm7vyyiu1ZcsWbdy4UZK0b98+LV++XDfddJNTu6ysLLVr105du3bVXXfdpezs7Dr1iRW1AcA76nZ9uQGUlpa6/qTKwdpcqQAAj9m2bZsSEhJUXFys0NBQLVu2TL1796627Z133qljx47pqquukmEYKisr05QpU/S73/3O0WbIkCFauHCh4uLilJOTo2eeeUZXX321tm/frhYtWlR73pKSEpWUlDgeVy6cWqfaAQCoN8teqaiTyisVu3dL5eXe7QsANFJxcXHKzMxURkaGpk6dqgkTJmjHjh3Vtk1PT9cLL7yg119/XV999ZWWLl2q//u//9Nzzz3naDNq1Cjddtttio+P18iRI7V8+XKdPHlS//jHP2rsg9sWTgUAuIXNuJiBEg2ooKBA4eHhOnLkiKKjo117cnm51Ly5VFoq7dsn/WTKWwBoLCq/K/Pz8xUWFubVviQmJqpbt2568803qxy7+uqrdcUVV+ill15y7HvnnXd03333qbCwUH5+1f9ua/DgwUpMTFRKSkq1x6u7UhETE1O32gEATYQna0fjulLh7y/16GH+zLgKAGgQFRUVTv/AP9/p06erBIfKGZpq+p1WYWGh9u7dW2s4sNvtjmltKzcAgPc0rlAhMa4CADwoOTlZn332mQ4cOKBt27YpOTlZ6enpuuuuuyRJ48ePV3JysqN9UlKSUlNTtWTJEu3fv1+rVq3Sk08+qaSkJEe4eOSRR7R27VodOHBAX375pW655Rb5+/tr7NixXnmPAADXWXagdp1VjqsgVACA2+Xl5Wn8+PHKyclReHi44uPjlZaWpuHDh0uSsrOzna5MzJo1SzabTbNmzdLhw4fVpk0bJSUl6fnnn3e0OXTokMaOHavjx4+rTZs2uuqqq7Rhwwa1qVzQFABgeY1rTIUkvfOONG6cdO21Unq62/sHAFZgpTEVVlDv2gEATUCTHFNht9vr9kSuVABAk1Xn2gEAqBfLhoqAgDremVU5puLoUenkSbf1BwBgfXWuHQCAerFsqKizFi2kdu3Mn5kBCgAAAPA4y4aKs2fP1v3J3AIFAE1SvWoHAKDOLBsqSktL6/5kQgUANEn1qh0AgDqzbKioF0IFAAAA0GAad6hgTAUAAADgcY0zVFTOALVnj8T9tQAAAIBHNc5Q0aGD1Ly5GSj27/d2bwAAAIBGrXGGCj+/c1crGFcBAAAAeFTjDBUSg7UBAACABmLZUGG32+t3AgZrA0CTU+/aAQCoE8uGioCAgPqdgNufAKDJqXftAADUiWVDRb1VXqnYuVMyDO/2BQAAAGjELBsqysrK6neC7t0lm0368Ufp2DH3dAoAYGn1rh0AgDqxbKgoKSmp3wmaN5c6dTJ/5hYoAGgS6l07AAB1YtlQ4RYM1gYAAAA8rnGHCgZrAwAAAB7XuEMFa1UAAAAAHkeoAAAAAFAvTSNU7N8vMXgPAAAA8IjGHSoiI6WwMKmiQtqzx9u9AQAAABoly4aKwMDA+p/EZuMWKABoQtxSOwAALrNsqGjWrJl7TkSoAIAmw221AwDgEsuGCrchVAAAAAAeZdlQUVZW5p4TsQAeADQZbqsdAACXWDZUlLhrtqbzF8AzDPecEwBgSW6rHQAAl1g2VLhNt26Sv7906pSUk+Pt3gAAAACNTuMPFXa71LWr+TPjKgAAAAC3a/yhQmKwNgAAAOBBTStUMFgbAOolNTVV8fHxCgsLU1hYmBISErRixYpan/PKK68oLi5OwcHBiomJ0UMPPaTi4mKnNvPmzVPnzp0VFBSkIUOGaOPGjZ58GwAAN2saoeL8wdoAgDrr0KGD5syZoy1btmjz5s26/vrrNXr0aH377bfVtl+8eLEef/xxPfXUU9q5c6fmz5+v9957T7/73e8cbd577z3NnDlTTz31lL766iv1799fI0eOVF5eXkO9LQBAPdkMw1pTIhUUFCg8PFxHjhxRdHS0e076xRfSVVdJHTtK33/vnnMCgBdVflfm5+crLCzMq31p2bKlXnrpJU2ePLnKsQceeEA7d+7U6tWrHfsefvhhZWRkaN26dZKkIUOGaPDgwXrttdckSRUVFYqJidH06dP1+OOPX1QfHLVjzx5FR0W54V0BQONTUFCg8HbtPFI7Atx6NjcKDAx038kqb3/KzpaKiqSQEPedGwCaqPLycr3//vsqKipSQkJCtW2uvPJKvfPOO9q4caMuv/xy7du3T8uXL9e4ceMkSaWlpdqyZYuSk5Mdz/Hz81NiYqLWr19f42uXlJQ4TR9bUFAgSQqMjXXHWwMAuMiyoaJZs2buO1mrVlLr1tKxY9Lu3dKAAe47NwA0Mdu2bVNCQoKKi4sVGhqqZcuWqXfv3tW2vfPOO3Xs2DFdddVVMgxDZWVlmjJliuP2p2PHjqm8vFyRkZFOz4uMjNR3tdyympKSomeeeabKfjdWDgCACywbKtyuZ09p3TpzsDahAgDqLC4uTpmZmcrPz9cHH3ygCRMmaO3atdUGi/T0dL3wwgt6/fXXNWTIEO3Zs0cPPvignnvuOT355JN17kNycrJmzpzpeFxQUKCYmBjpyBHJy7eDAYBlFRRI7dp55NSWDRXl5eXuPWFcnBkqGKwNAPUSGBio2P/eZjRo0CBt2rRJc+fO1Ztvvlml7ZNPPqlx48bp3nvvlST169dPRUVFuu+++/TEE0+odevW8vf319GjR52ed/ToUUXVMjbCbrfLbrdX2V8eFMQtrgBQE3f/+/o8lp396afTDdYba1UAgEdUVFQ4jW843+nTp+Xn51xq/P39JUmGYSgwMFCDBg1yGshdUVGh1atX1zhOozZurx0AgIti2SsVbkeoAIB6S05O1qhRo9SxY0edOnVKixcvVnp6utLS0iRJ48ePV/v27ZWSkiJJSkpK0ssvv6wBAwY4bn968sknlZSU5AgXM2fO1IQJE3TZZZfp8ssv1yuvvKKioiJNmjTJa+8TAOCaphcqdu+WKiokP8tepAEAy8rLy9P48eOVk5Oj8PBwxcfHKy0tTcOHD5ckZWdnO12ZmDVrlmw2m2bNmqXDhw+rTZs2SkpK0vPPP+9o86tf/Uo//PCDZs+erdzcXF166aVauXJllcHbAADrcnmdis8++0wvvfSStmzZopycHC1btkxjxoxxHJ84caIWLVrk9JyRI0dq5cqVF3V+j6xTIUllZVLz5tLZs9KBA1KnTu47NwA0MCutU2EFHqsdANCIeLJ2uPzr+qKiIvXv31/z5s2rsc2NN96onJwcx/b3v/+9Xp10i4AAqXt382dugQIAAADcxuXbn0aNGqVRo0bV2sZut9c6a4fX9Owp7dhhhoqRI73dGwAAAKBR8MjAgvT0dLVt21ZxcXGaOnWqjh8/XmPbkpISFRQUOG0ew2BtAAAAwO3cHipuvPFGvf3221q9erX+8Ic/aO3atRo1alSN606kpKQoPDzcscXExEhy84ralQgVANCoeaR2AAAuyO2zP91xxx2On/v166f4+Hh169ZN6enpuuGGG6q0r2lV1MDAQHd3zVwATzJX1QYANDoeqR0AgAvy+LyqXbt2VevWrbVnz55qj9vtdoWFhTltHlMZKnJypPx8z70OAAAA0IR4PFQcOnRIx48fd3mKv5pul6qX8HCpsh9crQCARscjtQMAcEEuh4rCwkJlZmYqMzNTkrR//35lZmYqOztbhYWF+u1vf6sNGzbowIEDWr16tUaPHq3Y2FiNdHG2peLiYle7dnEYVwEAjZbHagcAoFYuh4rNmzdrwIABGjBggCRp5syZGjBggGbPni1/f3998803+vnPf64ePXpo8uTJGjRokD7//HPZ7Xa3d75OCBUAAACAW7k8UHvYsGGqbRHutLS0enXI4xisDQAAALiVx8dUWA5XKgAAAAC3arqhIitLKivzbl8AAACARqDphYqYGCk4WDp7Vtq/39u9AQAAAHxe0wsVfn6MqwAAAADcyLKholmzZp47eWWoYFwFADQqHq0dAIAaWTZUBAYGeu7kDNYGgEbJo7UDAFAjy4YKjyJUAAAAAG5j2VBRUVHhuZMTKgCgUfJo7QAA1MiyoeLMmTOeO3n37uafx49Lx4557nUAAA3Ko7UDAFAjy4YKjwoJkTp2NH9mBigAAACgXppmqJC4BQoAAABwE0IFoQIAAACoF0IFoQIAAACol6YbKlhVGwAAAHCLphsqKq9U7NsnlZR4ty8AAACAD7NsqGjWrJlnXyA6WmrRQiovl/bu9exrAQAahMdrBwCgWpYNFYGBgZ59AZuNcRUA0Mh4vHYAAKpl2VDRIAgVAAAAQL1ZNlRUVFR4/kUYrA0AjUqD1A4AQBWWDRVnzpzx/ItwpQIAXJKamqr4+HiFhYUpLCxMCQkJWrFiRY3thw0bJpvNVmW7+eabHW0mTpxY5fiNN95Yp/41SO0AAFQR4O0OeNX5ocIwzHEWAIAadejQQXPmzFH37t1lGIYWLVqk0aNHa+vWrerTp0+V9kuXLlVpaanj8fHjx9W/f3/ddtttTu1uvPFGLViwwPHYbrd77k0AANyuaYeK2FjJz08qKJByc80ZoQAANUpKSnJ6/Pzzzys1NVUbNmyoNlS0bNnS6fGSJUvUvHnzKqHCbrcrKirK/R0GADQIy97+1CDsdqlrV/NnboECAJeUl5dryZIlKioqUkJCwkU9Z/78+brjjjsUEhLitD89PV1t27ZVXFycpk6dquPHj9d6npKSEhUUFDhtAADvadqhQmKwNgC4aNu2bQoNDZXdbteUKVO0bNky9e7d+4LP27hxo7Zv3657773Xaf+NN96ot99+W6tXr9Yf/vAHrV27VqNGjVJ5eXmN50pJSVF4eLhji4mJqff7AgDUXdO+/Ukyx1X83/9xpQIALlJcXJwyMzOVn5+vDz74QBMmTNDatWsvGCzmz5+vfv366fLLL3faf8cddzh+7tevn+Lj49WtWzelp6frhhtuqPZcycnJmjlzpuNxQUEBwQIAvIgrFcwABQAuCQwMVGxsrAYNGqSUlBT1799fc+fOrfU5RUVFWrJkiSZPnnzB83ft2lWtW7fWnj17amxjt9sdM1BVbgAA77FsqAgIaKCLKIQKAKiXiooKlZSU1Nrm/fffV0lJie6+++4Lnu/QoUM6fvy4ousweUaD1Q4AgBPLfvs22HSClaHi+++l06el5s0b5nUBwAclJydr1KhR6tixo06dOqXFixcrPT1daWlpkqTx48erffv2SklJcXre/PnzNWbMGLVq1cppf2FhoZ555hndeuutioqK0t69e/Xoo48qNjZWI0eOdLl/TEULAN5h2VDRYFq3llq2lE6ckLKypP79vd0jALCsvLw8jR8/Xjk5OQoPD1d8fLzS0tI0fPhwSVJ2drb8/Jwvgu/atUvr1q3Tf/7znyrn8/f31zfffKNFixbp5MmTateunUaMGKHnnnuOgAAAPsSyocIwjIZ7sZ49pS+/NG+BIlQAQI3mz59f6/H09PQq++Li4mr8Tg8ODnZc5XCHBq0dAAAHy46pOH36dMO9GOMqAKBRaNDaAQBwsGyoaFCECgAAAKDOCBUSC+ABAAAA9UCokM5dqdi1S6qo8G5fAAAAAB9DqJCkLl2kZs3MKWUPHfJ2bwAAAACfQqiQzEARG2v+zLgKAAAAwCWEikoM1gYAAADqxLKhIiCggZfQYLA2APi8Bq8dAABJFg4VDb6SKlcqAMDnsQo3AHiHZUNFgyNUAAAAAHVi2VBhGEbDvmDl7U9HjkgFBQ372gAAt2jw2gEAkGThUHH69OmGfcGICCkqyvyZcRUA4JMavHYAACRZOFR4BYO1AQAAAJcRKs7HuAoAAADAZYSK8xEqAAAAAJcRKs5HqAAAAABcRqg4X2WoyMqSysu92xcAAADAR7gcKj777DMlJSWpXbt2stls+uijj5yOG4ah2bNnKzo6WsHBwUpMTFRWVpa7+utZHTtKQUFSaal04IC3ewMAAAD4BJdDRVFRkfr376958+ZVe/zFF1/Uq6++qjfeeEMZGRkKCQnRyJEjVVxc7NLr+Pv7u9q1+vPzk3r0MH/mFigA8DleqR0AAAW4+oRRo0Zp1KhR1R4zDEOvvPKKZs2apdGjR0uS3n77bUVGRuqjjz7SHXfccdGvExQU5GrX3KNnT+mbb8xQcfPN3ukDAKBOvFY7AKCJc+uYiv379ys3N1eJiYmOfeHh4RoyZIjWr1/vzpfyHAZrAwAAAC5x+UpFbXJzcyVJkZGRTvsjIyMdx36qpKREJSUljscFBQXu7JLrKhfAI1QAAAAAF8Xrsz+lpKQoPDzcscXExEgyx254ReWVClbVBgCf47XaAQBNnFtDRVRUlCTp6NGjTvuPHj3qOPZTycnJys/Pd2wHDx50Z5dcVzlQ+4cfpOPHvdsXAAAAwAe4NVR06dJFUVFRWr16tWNfQUGBMjIylJCQUO1z7Ha7wsLCnDavCg2V/nu1hKsVAAAAwIW5HCoKCwuVmZmpzMxMSebg7MzMTGVnZ8tms2nGjBn6/e9/r48//ljbtm3T+PHj1a5dO40ZM8bNXfegylugnn9eOnzYu30BAAAALM7lULF582YNGDBAAwYMkCTNnDlTAwYM0OzZsyVJjz76qKZPn6777rtPgwcPVmFhoVauXOlb0/zdd5+5ZsXy5WbA+OMfpbNnvd0rAAAAwJJshmEY3u7E+QoKChQeHq4jR44oOjraex3JzJTuv1+qnAq3Tx9p3jzp2mu91ycA+K/K78r8/Hzv3zZqAZapHQBgYZ6sHV6f/cmyLr1UWrdOmj9fat1a+vZbadgwadw4qYbpcQEAAICmyLKhwt/f39tdMG+Buucec8D2//t/ks0mvfOOuZbFn/8slZV5u4cA0KBSU1MVHx/vmFgjISFBK1asqLH9sGHDZLPZqmw333yzo41hGJo9e7aio6MVHBysxMREZWVl1al/lqgdANAEWTZUWGoMRsuW0htvSBkZ0qBBUkGB9JvfSIMHn7s9CgCagA4dOmjOnDnasmWLNm/erOuvv16jR4/Wt99+W237pUuXKicnx7Ft375d/v7+uu222xxtXnzxRb366qt64403lJGRoZCQEI0cOVLFxcUu989StQMAmhDLhgpLGjzYDBavvy5FRJjjLq68Upo8WTp2zNu9AwCPS0pK0k033aTu3burR48eev755xUaGqoNGzZU275ly5aKiopybKtWrVLz5s0docIwDL3yyiuaNWuWRo8erfj4eL399ts6cuSIPvroowZ8ZwCA+iBUuMrfX5o6Vdq9W5o0ydz31lvmonlvvilVVHi3fwDQQMrLy7VkyRIVFRXVuBbRT82fP1933HGHQkJCJJnTkufm5ioxMdHRJjw8XEOGDNF6rgQDgM+wbKgoKirydhdq16aNGSbWrZPi46Uff5SmTJGuuELavNnbvQMAj9m2bZtCQ0Nlt9s1ZcoULVu2TL17977g8zZu3Kjt27fr3nvvdezL/e/EF5GRkU5tIyMjHceqU1JSooKCAqdN8oHaAQCNlGVDhc8YOlTaskV65RWpRQtp0ybp8svN6Wh//NHbvQMAt4uLi1NmZqYyMjI0depUTZgwQTt27Ljg8+bPn69+/frp8ssvr3cfUlJSFB4e7thiYmLqfU4AQN0RKtwhIEB68EFzlqg775QMQ0pNNW+JWrCAW6IANCqBgYGKjY3VoEGDlJKSov79+2vu3Lm1PqeoqEhLlizR5MmTnfZHRUVJko4ePeq0/+jRo45j1UlOTlZ+fr5jO3jwYB3fDQDAHQgV7hQdLb37rvTpp1KvXubg7Xvuka65Rvr6a2/3DgA8oqKiQiUlJbW2ef/991VSUqK7777baX+XLl0UFRWl1atXO/YVFBQoIyOj1nEadrvdMa1t5QYA8B5ChSdcd505M9SLL0ohIdIXX5hT0c6YYU5HCwA+Kjk5WZ999pkOHDigbdu2KTk5Wenp6brrrrskSePHj1dycnKV582fP19jxoxRq1atnPbbbDbNmDFDv//97/Xxxx9r27ZtGj9+vNq1a6cxY8Y0xFsCALgBocJTAgOl3/5W2rlT+uUvpfJyae5cc+G8xYvNW6QAwMfk5eVp/PjxiouL0w033KBNmzYpLS1Nw4cPlyRlZ2crJyfH6Tm7du3SunXrqtz6VOnRRx/V9OnTdd9992nw4MEqLCzUypUrWXMCAHyIzTCs9a/bgoIChYeH68iRI4qOjvZ2d9wnLU2aPl2qXCX2uuuk116TLmLGFAD4qcrvyvz8fG79USOuHQDgRp6sHZa9UuHnZ9mu1c3IkdK2bdLvfy8FBUlr1kj9+0uPPSYVFnq7dwDQKDS62gEAPsKy377BwcHe7oL72e3SE0+Yt0T9/OdSWZk57qJXL+nDD7klCgDqqVHWDgDwAZYNFY1a587SP/8p/etf5s+HDpnjLm688dztUQAAAICPIFR4089+Ju3YIT35pDmw+z//kfr2NR+fPu3t3gEAAAAXxbKhoqioyNtdaBjBwdKzz0rbt5vjLkpLzXEXffpIH3/MLVEA4IImUzsAwGIsGyqanO7dpRUrpA8+kDp0kA4ckEaPNvc/8YQ5yJuAAQAAAAsiVFiJzSbdeqv03XfmrFDNm0t790ovvCDFx5u3Rj37rLR7t7d7CgAAADgQKqwoJESaM0fKy5OWLJHGjDHHXOzYIT31lLmA3sCB0h/+YF7RAAAAALyIUGFlISHSr34lLVtmBoxFi6RRo6SAAGnrVunxx6UuXaQrrpBeeUU6fNjbPQYAAEATRKjwFeHh0vjx0vLlUm6u9Je/SNdfb94ylZEhPfSQFBMjXXutlJoq/fCDt3sMAACAJoJQ4YtatZJ+/Wtp9Wrz6sSrr0pDh5oDuT/7TLr/fik6WhoxQnrrLenHH73dYwAAADRilg0Vfn6W7Zq1REdL06dL69ZJ338v/c//SJddJpWXS6tWSZMnS5GRUlKS9O670qlT3u4xAHgMtQMAvMOy377BwcHe7oLv6dhRevhhadMmc2Xu55+X+vWTzp6V/v1v6e67pbZtzdW7P/hAOnPG2z0GALeidgCAd1g2VKCeYmOl3/1O+uYb6dtvpdmzpR49pOJi6cMPpdtuMwPGXXdJ//qXVFLi7R4DAADARxEqmoLevaVnnjHXv/jqK3MNjE6dpMJCafFi6ec/l6KipHvukf7zH6mszNs9BgAAgA+xbKg4ffq0t7vQ+Nhs0oAB5hoY+/dL69dLM2aY4zJOnpQWLJBGjjQfT50qrV1rjs0AAB9B7QAA77BsqDAMw9tdaNxsNnN9iz/9STp4UEpPN4NE69bSsWPSG29Iw4aZ09SOH2/OIrV3rznDFABYFLUDALzDsqECDcjf31zf4vXXpZwcKS3NvBUqIsJ8/Le/mbNIxcaag8Hvvlv63/+V9uwhZAAAAEA2w2K/1ikoKFB4eLiOHDmi6Ohob3enaSspMde9WLvWvJKxcaM5k9T52rUzr2hce635Z/fu5lUQAB5V+V2Zn5+vsLAwb3fH66gdAHBhnqwdAW49GxoXu10aPtzcJOn0aXMcRmXIyMiQjhwxB3svXmy2iYpyDhlxcYQMAACARo5QgYvXvLl0ww3mJpnrXGzYcC5kbNgg5eZKS5aYm2QuvHfttedCRq9ehAwAAIBGhlCBugsOlq67ztwkcw2MjAwzYKxda17VOHpU+sc/zE2S2rQ5FzCuvdac7pYVcAEAAHyaZUOFjd9m+56goHNXJSRzTMbGjedCxpdfSj/8YK7m/cEHZpvWraVrrjkXMvr2JWQAqDNqBwB4h2UHajP4sBEqLZU2bToXMr74whyncb6WLZ1DRnw8IQOoBt+Vzvj7AIAL8+R3JaEC3lNaKm3Zci5krFsnFRU5t7nkEunqq6XLL5cGDjS3yEivdBewEr4rnfH3AQAXRqhA03D2rPTVV+dCxuefS4WFVdu1a3cuYAwYYP4ZE8MAcDQpfFc64+8DAC6sSYaKnJwcRUVFebs78KayMmnrVjNcbN1qXtX47rvqF9xr1epc0Kjcunbl1ik0Wvwj2hm1AwAurEmuU2GxrANvCAiQBg82t0pFRdI335hXNCq37dul48elVavMrVJY2LkrGZVbXJy5gjiARonaAQDeYdlQAVQrJERKSDC3SiUlZrA4P2h8/bVUUGDeRrV27bm2zZtL/fs7B43evaXAwIZ/LwAAAI0EoQK+z26XBg0yt0pnz5q3Sp0fNLZuNa90rF9vbpUCA6V+/ZzHaMTHm+twAAAA4IK44RyNU7NmZlCYMEGaO9ccl5GfbwaNxYulRx6Rrr9eiog4NwvVX/8q3X+/dMUVUosWzs//7DPpxAlvvyvA61JTUxUfH6+wsDCFhYUpISFBK1asqPU5J0+e1LRp0xQdHS273a4ePXpo+fLljuNPP/20bDab09azZ09PvxUAgBtxpQJNh7+/OaYiLk4aO9bcZxjSgQPOVzS2bDEX6du+3dzefvvcOSIjzduleveWevU693Pbtsw+hSahQ4cOmjNnjrp37y7DMLRo0SKNHj1aW7duVZ8+faq0Ly0t1fDhw9W2bVt98MEHat++vb7//ntFREQ4tevTp48++eQTx+OAAMoTAPgSvrXRtNlsUpcu5nbrreY+w5COHHG+bWrrVik7Wzp61NzWrHE+zyWXVB82OnQgbKBRSUpKcnr8/PPPKzU1VRs2bKg2VLz11ls6ceKEvvzySzVr1kyS1Llz5yrtAgICmLUJAHyYZUOFjX+IwVtsNql9e3M7/x9Qp06Zt0/t3Cnt2HFu27dP+vFHc4XwL75wPldo6LmQcX7Y6NyZWajg88rLy/X++++rqKhICedPnnCejz/+WAkJCZo2bZr++c9/qk2bNrrzzjv12GOPyf+8/weysrLUrl07BQUFKSEhQSkpKerYsWONr11SUqKSkhLH44KCAknUDgDwFsuGiubNm3u7C4CzFi2qTnErSWfOSLt3mwHj/MCRlWUu3rdpk7mdLyjIvA3rp2EjNtYcDwJY2LZt25SQkKDi4mKFhoZq2bJl6t27d7Vt9+3bp08//VR33XWXli9frj179uj+++/X2bNn9dRTT0mShgwZooULFyouLk45OTl65plndPXVV2v79u1q0aJFtedNSUnRM888U2U/tQMAvMOyi9+xoBN83tmz0p49zmFj507zakdxcfXPCQiQunevGjZ69GA2Kjjx5ndlaWmpsrOzlZ+frw8++ED/+7//q7Vr11YbLHr06KHi4mLt37/fcWXi5Zdf1ksvvaScnJxqz3/y5El16tRJL7/8siZPnlxtm+quVMTExFA7AKAWPrX43dNPP13lt0dxcXH67rvv3P1SgLU1a2YGg169nPeXl5uDw8+/qlH5c2Gh+fPOnc7PsdnMFcJ79DCvZsTGSt26mX926cI6G2hQgYGBio2NlSQNGjRImzZt0ty5c/Xmm29WaRsdHa1mzZo53erUq1cv5ebmqrS0VIHV/LcbERGhHj16aM+ePTX2wW63y263u+HdAADcwSO3P7ljFo8zZ87w2yY0Tv7+ZiDo1k362c/O7TcM6dChqmHj22/NMRt795rbT/n5SR07ngsb529du3KFAx5XUVHhdNXgfEOHDtXixYtVUVEhPz9zFvPdu3crOjq62kAhSYWFhdq7d6/GjRvncl+oHQDgHR4JFe6YxaOiosJNvQF8hM0mxcSY24gR5/YbhpSXZwaMPXuqbkVF5pWPAwek88K8Q4cO1QeObt3MgeSAC5KTkzVq1Ch17NhRp06d0uLFi5Wenq60tDRJ0vjx49W+fXulpKRIkqZOnarXXntNDz74oKZPn66srCy98MIL+s1vfuM45yOPPKKkpCR16tRJR44c0VNPPSV/f3+NrZz62QXUDgDwDo+ECldn8QBQC5vNXB8jMlIaNsz5mGGYU9xWFzb27DEX/Dt0yNzS06ueOyqq6u1UldtP1hEAJCkvL0/jx49XTk6OwsPDFR8fr7S0NA0fPlySlJ2d7bgiIUkxMTFKS0vTQw89pPj4eLVv314PPvigHnvsMUebQ4cOaezYsTp+/LjatGmjq666Shs2bFCbNm0a/P0BAOrG7QO1V6xYocLCQqdZPA4fPlzjLB41DbY7cuSIoqOj3dk1oGkxDOn48XMBY+9e58Bx7Fjtz2/VqvorHJ07mwGHqTu9ikktnFX+fVA7AKBmnqwdHp/96UKzeFQ3sFsShQHwtJMnqwaNyi03t/bnBgVJnTqZW+fOVf+MjjbHesBjCBXOCBUAcGE+NfvTT11oFo/k5GTNnDnT8bjySgUAD4uIkAYNMrefKiysOXAcPmxOibtrl7lVp1kzc/B4dYGjUydzYcE6TOAAAACsyeNV/UKzeDAtIGBBoaFS//7m9lOlpeYYjQMHpO+/r/rnwYPmGh01zVYlmTNgdehghozqgkeHDkyTCwCAD3F7qHDnLB4ALCgw0JyqtmvX6o+XlZlXM6oLHAcOSNnZZuj4/ntzW7u26jlsNvNqRmXI+Gnw6NBBYuVkAAAsw+2hwl2zeISEhLi7awAaQkDAufEW11xT9XhFhZSTU/OVju+/N2+vqpy16osvqn+dli3NcFHbVs3kEGjcqB0A4B1uDxVLlixx9ykBNCZ+fuZViPbtpaFDqx6vXJfj/LDx0+BRVCSdOGFu33xT82uFhZmvU1vwuOQSZrICAKCeGCkJwFrOX5djyJCqxw3Def2Nmrb8fKmgwNx27qz59YKDnUNGdSGkTRtmswIAoBaWDRVnzpxhmkQAVdls5sxVERFS3741tyssNMd21BY8jh2TzpyRsrLMrSbNmjmHjfbtpXbtzKlzz99atOCqh5dROwDAOywbKioqKrzdBQC+LDRUioszt5oUFzsHj+pCSG6uObC88jas2jRvXjVonL9FRZl/tmrFlQ8PoXYAgHdYNlQAgMcFBUnduplbTc6eNQeW/zRs5OQ4b6dOSadP1z6VbqVmzczbu2oLINHRZhvW8wAA+ACqFQDUpnIhv44da29XVFQ1aFS3HT9uBpXKcFIbm80cz1Fd4AgPd997BACgnggVAOAOISFSbKy51aa01LylKifn3J/VbUePSuXl5kxYeXnS1183zPsAAKAOCBUA0JACAy/uykd5uTmQvKbQcfCgtGlTw/QZAIALIFQAgBX5+5+bWvfSS6seLyjgFigAgGUw/QgAAACAerFsqAgJCfF2FwAAPobaAQDeYdlQAQAAAMA3ECoAAAAA1ItlQ0VxcbG3uwAA8DHUDgDwDsuGivLycm93AQDgY6gdAOAdlg0VAAAAAHwDoQIAAABAvRAqAAAAANQLoQIAAABAvRAqAAAAANRLgLc78FOGYUiSTp06xcqoAFCDgoICSee+M5s6agcAXJgna4flQsXx48clSXFxcV7uCQBY3/HjxxUeHu7tbngdtQMALp4naoflQkXLli0lSdnZ2RRKH1JQUKCYmBgdPHhQYWFh3u4OXMBn55vy8/PVsWNHx3dmU0ft8F18B/kmPjff5MnaYblQ4ednDvMIDw/nP1IfFBYWxufmo/jsfFPld2ZTR+3wfXwH+SY+N9/kidpBNQIAAABQL4QKAAAAAPViuVBht9v11FNPyW63e7srcAGfm+/is/NNfG7O+PvwXXx2vonPzTd58nOzGcxHCAAAAKAeLHelAgAAAIBvIVQAAAAAqBdCBQAAAIB6sVyomDdvnjp37qygoCANGTJEGzdu9HaXmqyUlBQNHjxYLVq0UNu2bTVmzBjt2rXLqU1xcbGmTZumVq1aKTQ0VLfeequOHj3q1CY7O1s333yzmjdvrrZt2+q3v/2tysrKGvKtNGlz5syRzWbTjBkzHPv43Kzr8OHDuvvuu9WqVSsFBwerX79+2rx5s+O4YRiaPXu2oqOjFRwcrMTERGVlZTmd48SJE7rrrrsUFhamiIgITZ48WYWFhQ39VhoUtcM6qB2NA7XDt1iidhgWsmTJEiMwMNB46623jG+//db49a9/bURERBhHjx71dteapJEjRxoLFiwwtm/fbmRmZho33XST0bFjR6OwsNDRZsqUKUZMTIyxevVqY/PmzcYVV1xhXHnllY7jZWVlRt++fY3ExERj69atxvLly43WrVsbycnJ3nhLTc7GjRuNzp07G/Hx8caDDz7o2M/nZk0nTpwwOnXqZEycONHIyMgw9u3bZ6SlpRl79uxxtJkzZ44RHh5ufPTRR8bXX39t/PznPze6dOlinDlzxtHmxhtvNPr3729s2LDB+Pzzz43Y2Fhj7Nix3nhLDYLaYS3UDt9H7fAtVqkdlgoVl19+uTFt2jTH4/LycqNdu3ZGSkqKF3uFSnl5eYYkY+3atYZhGMbJkyeNZs2aGe+//76jzc6dOw1Jxvr16w3DMIzly5cbfn5+Rm5urqNNamqqERYWZpSUlDTsG2hiTp06ZXTv3t1YtWqVce211zoKA5+bdT322GPGVVddVePxiooKIyoqynjppZcc+06ePGnY7Xbj73//u2EYhrFjxw5DkrFp0yZHmxUrVhg2m804fPiw5zrvRdQOa6N2+BZqh++xSu2wzO1PpaWl2rJlixITEx37/Pz8lJiYqPXr13uxZ6iUn58vSWrZsqUkacuWLTp79qzTZ9azZ0917NjR8ZmtX79e/fr1U2RkpKPNyJEjVVBQoG+//bYBe9/0TJs2TTfffLPT5yPxuVnZxx9/rMsuu0y33Xab2rZtqwEDBuivf/2r4/j+/fuVm5vr9NmFh4dryJAhTp9dRESELrvsMkebxMRE+fn5KSMjo+HeTAOhdlgftcO3UDt8j1Vqh2VCxbFjx1ReXu70H6IkRUZGKjc310u9QqWKigrNmDFDQ4cOVd++fSVJubm5CgwMVEREhFPb8z+z3Nzcaj/TymPwjCVLluirr75SSkpKlWN8bta1b98+paamqnv37kpLS9PUqVP1m9/8RosWLZJ07u++tu/J3NxctW3b1ul4QECAWrZs2Sg/O2qHtVE7fAu1wzdZpXYE1PeNoGmYNm2atm/frnXr1nm7K7iAgwcP6sEHH9SqVasUFBTk7e7ABRUVFbrsssv0wgsvSJIGDBig7du364033tCECRO83DvAddQO30Ht8F1WqR2WuVLRunVr+fv7V5lF4OjRo4qKivJSryBJDzzwgP79739rzZo16tChg2N/VFSUSktLdfLkSaf2539mUVFR1X6mlcfgflu2bFFeXp4GDhyogIAABQQEaO3atXr11VcVEBCgyMhIPjeLio6OVu/evZ329erVS9nZ2ZLO/d3X9j0ZFRWlvLw8p+NlZWU6ceJEo/zsqB3WRe3wLdQO32WV2mGZUBEYGKhBgwZp9erVjn0VFRVavXq1EhISvNizpsswDD3wwANatmyZPv30U3Xp0sXp+KBBg9SsWTOnz2zXrl3Kzs52fGYJCQnatm2b03+oq1atUlhYWJX/AeAeN9xwg7Zt26bMzEzHdtlll+muu+5y/MznZk1Dhw6tMvXm7t271alTJ0lSly5dFBUV5fTZFRQUKCMjw+mzO3nypLZs2eJo8+mnn6qiokJDhgxpgHfRsKgd1kPt8E3UDt9lmdpRp2HmHrJkyRLDbrcbCxcuNHbs2GHcd999RkREhNMsAmg4U6dONcLDw4309HQjJyfHsZ0+fdrRZsqUKUbHjh2NTz/91Ni8ebORkJBgJCQkOI5XTi83YsQIIzMz01i5cqXRpk0bppdrYOfP4GEYfG5WtXHjRiMgIMB4/vnnjaysLOPdd981mjdvbrzzzjuONnPmzDEiIiKMf/7zn8Y333xjjB49utppAQcMGGBkZGQY69atM7p3797op5SldlgHtaPxoHb4BqvUDkuFCsMwjD//+c9Gx44djcDAQOPyyy83NmzY4O0uNVmSqt0WLFjgaHPmzBnj/vvvNy655BKjefPmxi233GLk5OQ4nefAgQPGqFGjjODgYKN169bGww8/bJw9e7aB303T9tPCwOdmXf/617+Mvn37Gna73ejZs6fxl7/8xel4RUWF8eSTTxqRkZGG3W43brjhBmPXrl1ObY4fP26MHTvWCA0NNcLCwoxJkyYZp06dasi30eCoHdZB7Wg8qB2+wwq1w2YYhlGHKy0AAAAAIMlCYyoAAAAA+CZCBQAAAIB6IVQAAAAAqBdCBQAAAIB6IVQAAAAAqBdCBQAAAIB6IVQAAAAAqBdCBQAAAIB6IVSg0Zo4caLGjBnT4K+7cOFC2Ww22Ww2zZgx46KeM3HiRMdzPvroI4/2DwBQM2oHUDcB3u4AUBc2m63W40899ZTmzp0rby0YHxYWpl27dikkJOSi2s+dO1dz5sxRdHS0h3sGAE0XtQPwHEIFfFJOTo7j5/fee0+zZ8/Wrl27HPtCQ0MVGhrqja5JMgtXVFTURbcPDw9XeHi4B3sEAKB2AJ7D7U/wSVFRUY4tPDzc8UVcuYWGhla5hD1s2DBNnz5dM2bM0CWXXKLIyEj99a9/VVFRkSZNmqQWLVooNjZWK1ascHqt7du3a9SoUQoNDVVkZKTGjRunY8eOudzn119/Xd27d1dQUJAiIyP1y1/+sr5/DQAAF1A7AM8hVKBJWbRokVq3bq2NGzdq+vTpmjp1qm677TZdeeWV+uqrrzRixAiNGzdOp0+fliSdPHlS119/vQYMGKDNmzdr5cqVOnr0qG6//XaXXnfz5s36zW9+o2effVa7du3SypUrdc0113jiLQIA3IzaAVwYtz+hSenfv79mzZolSUpOTtacOXPUunVr/frXv5YkzZ49W6mpqfrmm290xRVX6LXXXtOAAQP0wgsvOM7x1ltvKSYmRrt371aPHj0u6nWzs7MVEhKin/3sZ2rRooU6deqkAQMGuP8NAgDcjtoBXBhXKtCkxMfHO3729/dXq1at1K9fP8e+yMhISVJeXp4k6euvv9aaNWsc99mGhoaqZ8+ekqS9e/de9OsOHz5cnTp1UteuXTVu3Di9++67jt9oAQCsjdoBXBihAk1Ks2bNnB7bbDanfZUzg1RUVEiSCgsLlZSUpMzMTKctKyvLpUvQLVq00FdffaW///3vio6O1uzZs9W/f3+dPHmy/m8KAOBR1A7gwrj9CajFwIED9eGHH6pz584KCKjf/y4BAQFKTExUYmKinnrqKUVEROjTTz/VL37xCzf1FgBgBdQONEVcqQBqMW3aNJ04cUJjx47Vpk2btHfvXqWlpWnSpEkqLy+/6PP8+9//1quvvqrMzEx9//33evvtt1VRUaG4uDgP9h4A4A3UDjRFhAqgFu3atdMXX3yh8vJyjRgxQv369dOMGTMUEREhP7+L/98nIiJCS5cu1fXXX69evXrpjTfe0N///nf16dPHg70HAHgDtQNNkc3w1rKRQCO1cOFCzZgxo073vNpsNi1btsxpjnQAQONH7YCv40oF4AH5+fkKDQ3VY489dlHtp0yZ4tVVXAEA3kftgC/jSgXgZqdOndLRo0clmZeuW7dufcHn5OXlqaCgQJIUHR2tkJAQj/YRAGAt1A74OkIFAAAAgHrh9icAAAAA9UKoAAAAAFAvhAoAAAAA9UKoAAAAAFAvhAoAAAAA9UKoAAAAAFAvhAoAAAAA9UKoAAAAAFAvhAoAAAAA9fL/AdIwyVl7oQYdAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def constant_voltage(variables):\n", + " return variables[\"Voltage [V]\"] - 3.8\n", + "\n", + "\n", + "step = pybamm.step.CustomStepImplicit(constant_voltage, duration=600)\n", + "sol = pybamm.Simulation(\n", + " model, experiment=step, parameter_values=parameter_values\n", + ").solve()\n", + "pybamm.QuickPlot(sol, [\"Current [A]\", \"Voltage [V]\"]).plot(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In differential control, the user specifies the derivative of the current." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def custom_voltage(variables):\n", + " return 100 * (variables[\"Voltage [V]\"] - 3.8)\n", + "\n", + "\n", + "step = pybamm.step.CustomStepImplicit(\n", + " custom_voltage, duration=600, period=10, control=\"differential\"\n", + ")\n", + "sol = pybamm.Simulation(model, experiment=step).solve()\n", + "pybamm.QuickPlot(sol, [\"Current [A]\", \"Voltage [V]\"]).plot(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Combining custom steps and termination\n", + "\n", + "We now show a full example combining custom steps and termination to charge a battery until the anode potential reaches 0.02V, then ride the 0.02V plateau until the voltage reaches the cut-off voltage, then finish charging with constant voltage" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a function for the anode potential cut-off at 0.02V\n", + "def anode_potential_cutoff(variables):\n", + " return variables[\"Anode potential [V]\"] - 0.02\n", + "\n", + "\n", + "# We can reuse the same function to create both the termination event and the step\n", + "anode_potential_step = pybamm.step.CustomStepImplicit(\n", + " anode_potential_cutoff, direction=\"charge\", termination=\"4.2V\"\n", + ")\n", + "anode_potential_termination = pybamm.step.CustomTermination(\n", + " name=\"Anode potential cut-off [V]\", event_function=anode_potential_cutoff\n", + ")\n", + "\n", + "\n", + "# Charge with constant current, then constant anode potential, then constant voltage\n", + "def run_experiment(c_rate):\n", + " # Create the experiment\n", + " experiment = pybamm.Experiment(\n", + " [\n", + " (\n", + " # include the 4.2V termination event in case the anode potential cut-off is not reached\n", + " pybamm.step.c_rate(\n", + " -c_rate, termination=[anode_potential_termination, \"4.2V\"]\n", + " ),\n", + " anode_potential_step,\n", + " \"Hold at 4.2V until C/50\",\n", + " )\n", + " ]\n", + " )\n", + "\n", + " sim = pybamm.Simulation(\n", + " model, parameter_values=parameter_values, experiment=experiment\n", + " )\n", + "\n", + " # for a charge we start as SOC 0\n", + " sim.solve(initial_soc=0)\n", + "\n", + " # Plot\n", + " pybamm.QuickPlot(\n", + " sim.solution, [\"Current [A]\", \"Voltage [V]\", \"Anode potential [V]\"]\n", + " ).plot(0)\n", + "\n", + "\n", + "run_experiment(c_rate=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we never hit the anode potential cut-off, that step is skipped and we go straight to constant voltage" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "run_experiment(c_rate=0.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "The relevant papers for this notebook are:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -191,10 +435,11 @@ "[2] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", "[3] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", "[4] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", - "[5] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n", - "[6] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", - "[7] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", - "[8] Andrew Weng, Jason B Siegel, and Anna Stefanopoulou. Differential voltage analysis for battery manufacturing process control. arXiv preprint arXiv:2303.07088, 2023.\n", + "[5] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", + "[6] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n", + "[7] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", + "[8] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", + "[9] Andrew Weng, Jason B Siegel, and Anna Stefanopoulou. Differential voltage analysis for battery manufacturing process control. arXiv preprint arXiv:2303.07088, 2023.\n", "\n" ] } @@ -227,7 +472,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.3" + "version": "3.11.8" } }, "nbformat": 4, diff --git a/docs/source/examples/notebooks/solvers/dae-solver.ipynb b/docs/source/examples/notebooks/solvers/dae-solver.ipynb index cb8293c676..4149efab9d 100644 --- a/docs/source/examples/notebooks/solvers/dae-solver.ipynb +++ b/docs/source/examples/notebooks/solvers/dae-solver.ipynb @@ -18,9 +18,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.3.1 is available.\n", - "You should consider upgrading via the '/home/mrobins/git/PyBaMM/env/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n", - "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n" + "\u001B[33mWARNING: You are using pip version 22.0.4; however, version 22.3.1 is available.\n", + "You should consider upgrading via the '/home/mrobins/git/PyBaMM/env/bin/python -m pip install --upgrade pip' command.\u001B[0m\u001B[33m\n", + "\u001B[0mNote: you may need to restart the kernel to use updated packages.\n" ] } ], diff --git a/docs/source/examples/notebooks/solvers/idaklu-jax-interface.ipynb b/docs/source/examples/notebooks/solvers/idaklu-jax-interface.ipynb new file mode 100644 index 0000000000..a1204b6589 --- /dev/null +++ b/docs/source/examples/notebooks/solvers/idaklu-jax-interface.ipynb @@ -0,0 +1,478 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "GgclUjr3sT_E" + }, + "source": [ + "# IDAKLU-JAX interface\n", + "\n", + "The IDAKLU-JAX interface requires that PyBaMM is installed with the [optional JAX solver enabled](https://docs.pybamm.org/en/stable/source/user_guide/installation/gnu-linux-mac.html#optional-jaxsolver) (`pip install pybamm[jax]`) and requires at least Python 3.9.\n", + "\n", + "PyBaMM provides two mechanisms to interface battery models with JAX. The first (JaxSolver) implements PyBaMM models directly in native JAX, and as such provides the greatest flexibility. However, these models can be very slow to compile, especially during their initial run, and can require large amounts of memory.\n", + "\n", + "The second (the IDAKLU-Jax interface) instead provides a JAX-compliant interface to the IDAKLU solver. IDAKLU is a fast (compiled) solver based on SUNDIALS. By exposing the IDAKLU solver to JAX, we provide a fast solver capable of interfacing with third-party JAX-compatible software libraries, such as numpyro.\n", + "\n", + "Despite the apparent advantages, there are some limitations to this approach. The most notable is that model derivatives are limited to first-order (i.e. sensitivities), since the IDAKLU solver is not capable of auto-differentiation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zsJLlejtzjcC" + }, + "source": [ + "## Setup a basic DFN model\n", + "\n", + "To demonstrate use of the IDAKLU-Jax interface, we first set-up a basic model, choosing the DFN model in this case. We will provide two `inputs` to the model and will specify a list of variables of interest (`output_variables`). Specifying `output_variables` is strongly recommended to reduce computational load, while `inputs` are only required when derivatives are to be considered." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install \"pybamm[jax]\" -q # install PyBaMM with JAX support if it is not installed\n", + "import pybamm\n", + "import time\n", + "import numpy as np\n", + "import jax\n", + "import jax.numpy as jnp" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "99tT6F73sRyc" + }, + "outputs": [], + "source": [ + "# We will want to differentiate our model, so let's define two input parameters\n", + "inputs = {\n", + " \"Current function [A]\": 0.222,\n", + " \"Separator porosity\": 0.3,\n", + "}\n", + "\n", + "# Set-up the model\n", + "model = pybamm.lithium_ion.DFN()\n", + "geometry = model.default_geometry\n", + "param = model.default_parameter_values\n", + "param.update({key: \"[input]\" for key in inputs.keys()})\n", + "param.process_geometry(geometry)\n", + "param.process_model(model)\n", + "var = pybamm.standard_spatial_vars\n", + "var_pts = {var.x_n: 20, var.x_s: 20, var.x_p: 20, var.r_n: 10, var.r_p: 10}\n", + "mesh = pybamm.Mesh(geometry, model.default_submesh_types, var_pts)\n", + "disc = pybamm.Discretisation(mesh, model.default_spatial_methods)\n", + "disc.process_model(model)\n", + "\n", + "# Use a short time-vector for this example, and declare which variables to track\n", + "t_eval = np.linspace(0, 360, 10)\n", + "output_variables = [\n", + " \"Voltage [V]\",\n", + " \"Current [A]\",\n", + " \"Time [min]\",\n", + "]\n", + "\n", + "# Create the IDAKLU Solver object\n", + "idaklu_solver = pybamm.IDAKLUSolver(\n", + " rtol=1e-6,\n", + " atol=1e-6,\n", + " output_variables=output_variables,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QhMDaAjt0DR9" + }, + "source": [ + "Next, we jaxify the IDAKLU solver in the same way that we would run the IDAKLU solve. The only difference is that the `jaxify()` function returns an `IDAKLUJax` object, instead of a `Solution` object. We will keep track of this object, and can request a JAX-expression from it using the `get_jaxpr()` method, as below." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "rk4RYT2-z6BD" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX expression: .f at 0x132d50f40>\n" + ] + } + ], + "source": [ + "# This is how we would normally perform a solve using IDAKLU\n", + "sim = idaklu_solver.solve(\n", + " model,\n", + " t_eval,\n", + " inputs=inputs,\n", + " calculate_sensitivities=True,\n", + ")\n", + "\n", + "# Instead, we Jaxify the IDAKLU solver using similar arguments...\n", + "jax_solver = idaklu_solver.jaxify(\n", + " model,\n", + " t_eval,\n", + ")\n", + "\n", + "# ... and then obtain a JAX expression for the solve\n", + "f = jax_solver.get_jaxpr()\n", + "print(f\"JAX expression: {f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wjp4Fpj402Ah" + }, + "source": [ + "The JAX expression (that we named `f` in our example), is a function that can be used and evaluated like any other native JAX expression. This means that it can be included in broader JAX expressions, and can even be JIT compiled. The only limitations are that:\n", + "1) derivatives cannot be taken beyond first-order, which is the limit of our IDAKLU solver implementation, and\n", + "2) you are required to specify `output_variables` either at the `IDAKLUSolver` stage, or at the `jaxify` stage (you can create many jaxified expressions from a single solver object).\n", + "\n", + "Here is the most basic usage example:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "VCKYxXMD0xTX" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[3.81930814e+000 2.22000000e-001 4.95024341e-316]\n", + " [3.81346107e+000 2.22000000e-001 6.66666667e-001]\n", + " [3.81080090e+000 2.22000000e-001 1.33333333e+000]\n", + " [3.80885531e+000 2.22000000e-001 2.00000000e+000]\n", + " [3.80714541e+000 2.22000000e-001 2.66666667e+000]\n", + " [3.80552362e+000 2.22000000e-001 3.33333333e+000]\n", + " [3.80393909e+000 2.22000000e-001 4.00000000e+000]\n", + " [3.80237338e+000 2.22000000e-001 4.66666667e+000]\n", + " [3.80081962e+000 2.22000000e-001 5.33333333e+000]\n", + " [3.79927489e+000 2.22000000e-001 6.00000000e+000]]\n" + ] + } + ], + "source": [ + "# Print all output variables, evaluated over a given time vector\n", + "data = f(t_eval, inputs)\n", + "print(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tUZurVD26t4Z" + }, + "source": [ + "Here we see a matrix of (Nx3), where N is the number of time-samples in `t_eval`, and the three column-vectors correspond to our three `output_variables`. We can evaluate the expression at any point within our time-span (e.g. `f(0.0, inputs)`), or at multiple points (such as the full range of `t_eval`, as in our example). To help isolate output variables, the IDAKLU-Jax interface provides several helper functions. Below we demonstrate isolating a single variable using the `get_var` helper. You can also isolate multiple variables, provided as a list, by using the `get_vars` helper function." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "7UnY6goK633s" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Isolating a single variable returns an array of shape (10,)\n", + "[3.81930814 3.81346107 3.8108009 3.80885531 3.80714541 3.80552362\n", + " 3.80393909 3.80237338 3.80081962 3.79927489]\n", + "\n", + "Isolating two variables returns an array of shape (10, 2)\n", + "[[3.81930814 0.222 ]\n", + " [3.81346107 0.222 ]\n", + " [3.8108009 0.222 ]\n", + " [3.80885531 0.222 ]\n", + " [3.80714541 0.222 ]\n", + " [3.80552362 0.222 ]\n", + " [3.80393909 0.222 ]\n", + " [3.80237338 0.222 ]\n", + " [3.80081962 0.222 ]\n", + " [3.79927489 0.222 ]]\n" + ] + } + ], + "source": [ + "# Isolate a single variables\n", + "data = jax_solver.get_var(\"Voltage [V]\")(t_eval, inputs)\n", + "print(f\"Isolating a single variable returns an array of shape {data.shape}\")\n", + "print(data)\n", + "\n", + "# Isolate two variables from the solver\n", + "data = jax_solver.get_vars(\n", + " [\n", + " \"Voltage [V]\",\n", + " \"Current [A]\",\n", + " ],\n", + ")(t_eval, inputs)\n", + "print(f\"\\nIsolating two variables returns an array of shape {data.shape}\")\n", + "print(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IK32lBj9_rcW" + }, + "source": [ + "As with any JAX expression, we can create new expressions by encapsulating them in outer functions (as further demonstrated below). The method `jax_solver.get_var()` does this for you by encapsulating `f` with a function that isolates a given variable of interest. We then evaluate that new expression by passing our usual arguments `(t_eval, inputs)`.\n", + "\n", + "To compute the Jacobian matrix (the matrix of derivates of output variables with respect to each input parameter), make use of the Jacobian forward derivation `jax.jacfwd` and Jacobian reverse derivation `jax.jacrev` functions.\n", + "\n", + "When calling these functions we note that `argnums=1` signifies that we are taking the Jacobian with respect to the second argument (indexing from 0: `inputs`). Since `inputs` is a dictionary of input parameters, the result will also be a dictionary of derivatives with respect to each dictionary key / input parameter. These two methods (`jacfwd` and `jacrev`) will produce the same output, it is simply their derivation that differs. In general, the forward method tends to be slightly faster to run than the reverse method for our IDAKLU implementation." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "PmPfHSRu8N-_" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Jacobian forward method ran in 0.125 secs\n", + "{'Current function [A]': Array([[-0.13643792, 1. , 0. ],\n", + " [-0.16400861, 1. , 0. ],\n", + " [-0.17630142, 1. , 0. ],\n", + " [-0.18509421, 1. , 0. ],\n", + " [-0.19273301, 1. , 0. ],\n", + " [-0.19993145, 1. , 0. ],\n", + " [-0.20692727, 1. , 0. ],\n", + " [-0.21380043, 1. , 0. ],\n", + " [-0.22057579, 1. , 0. ],\n", + " [-0.2272616 , 1. , 0. ]], dtype=float64), 'Separator porosity': Array([[0.00579553, 0. , 0. ],\n", + " [0.00797 , 0. , 0. ],\n", + " [0.0095281 , 0. , 0. ],\n", + " [0.01024868, 0. , 0. ],\n", + " [0.01053737, 0. , 0. ],\n", + " [0.0106461 , 0. , 0. ],\n", + " [0.01068649, 0. , 0. ],\n", + " [0.01070164, 0. , 0. ],\n", + " [0.01070816, 0. , 0. ],\n", + " [0.01071172, 0. , 0. ]], dtype=float64)}\n", + "\n", + "Jacobian reverse method ran in 0.196 secs\n", + "{'Current function [A]': Array([[-0.13643792, 1. , 0. ],\n", + " [-0.16400861, 1. , 0. ],\n", + " [-0.17630142, 1. , 0. ],\n", + " [-0.18509421, 1. , 0. ],\n", + " [-0.19273301, 1. , 0. ],\n", + " [-0.19993145, 1. , 0. ],\n", + " [-0.20692727, 1. , 0. ],\n", + " [-0.21380043, 1. , 0. ],\n", + " [-0.22057579, 1. , 0. ],\n", + " [-0.2272616 , 1. , 0. ]], dtype=float64, weak_type=True), 'Separator porosity': Array([[0.00579553, 0. , 0. ],\n", + " [0.00797 , 0. , 0. ],\n", + " [0.0095281 , 0. , 0. ],\n", + " [0.01024868, 0. , 0. ],\n", + " [0.01053737, 0. , 0. ],\n", + " [0.0106461 , 0. , 0. ],\n", + " [0.01068649, 0. , 0. ],\n", + " [0.01070164, 0. , 0. ],\n", + " [0.01070816, 0. , 0. ],\n", + " [0.01071172, 0. , 0. ]], dtype=float64, weak_type=True)}\n" + ] + } + ], + "source": [ + "# Calculate the Jacobian matrix (via forward autodiff)\n", + "t_start = time.time()\n", + "out = jax.jacfwd(f, argnums=1)(t_eval, inputs)\n", + "print(f\"Jacobian forward method ran in {time.time()-t_start:0.3} secs\")\n", + "print(out)\n", + "\n", + "# Calculate Jacobian matrix (via backward autodiff)\n", + "t_start = time.time()\n", + "out = jax.jacrev(f, argnums=1)(t_eval, inputs)\n", + "print(f\"\\nJacobian reverse method ran in {time.time()-t_start:0.3} secs\")\n", + "print(out)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To extract the relevant data vector from the above expression, we can again make use of the `get_var()` helper function, which can also take numpy arrays as input, for example:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.13643792 -0.16400861 -0.17630142 -0.18509421 -0.19273301 -0.19993145\n", + " -0.20692727 -0.21380043 -0.22057579 -0.2272616 ]\n" + ] + } + ], + "source": [ + "# Isolate the derivate of Voltage with respect to the Current function:\n", + "out = jax.jacfwd(f, argnums=1)(t_eval, inputs)\n", + "data = jax_solver.get_var(out[\"Current function [A]\"], \"Voltage [V]\")\n", + "print(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2tC9Bp_g9mOp" + }, + "source": [ + "The gradient (`grad`) function on the other hand requires the underlying function to return a scalar value. The function must therefore be called separately for each time sample, and can only be evaluted for one output variable at a time. We can obey these restrictions with our JAX expression `f` through use of the `get_var` and `vmap` functions (the latter of which provides vector-mapping over time)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "sJjWUIcG9lWa" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gradient method ran in 0.105 secs\n", + "{'Current function [A]': Array([-0.13643792, -0.16400861, -0.17630142, -0.18509421, -0.19273301,\n", + " -0.19993145, -0.20692727, -0.21380043, -0.22057579, -0.2272616 ], dtype=float64), 'Separator porosity': Array([0.00579553, 0.00797 , 0.0095281 , 0.01024868, 0.01053737,\n", + " 0.0106461 , 0.01068649, 0.01070164, 0.01070816, 0.01071172], dtype=float64)}\n" + ] + } + ], + "source": [ + "# Example evaluation using the `grad` function\n", + "t_start = time.time()\n", + "data = jax.vmap(\n", + " jax.grad(\n", + " jax_solver.get_var(\"Voltage [V]\"),\n", + " argnums=1, # take derivative with respect to `inputs`\n", + " ),\n", + " in_axes=(0, None), # map time over the 0th dimension and do not map inputs\n", + ")(t_eval, inputs)\n", + "print(f\"Gradient method ran in {time.time()-t_start:0.3} secs\")\n", + "print(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G0bo1TPL-ZAM" + }, + "source": [ + "## A use-case example\n", + "\n", + "As a use-case example, consider a fitting procedure where we want to compare simulation data against some experimental data. We achieve this by computing the sum-of-squared error (SEE) between the two. Many fitting procedures will converge more quickly (with fewer iterations) if both the value *and gradient* of the SSE function are provided. By making use of JAX-expressions we can derive these effortlessly.\n", + "\n", + "*Note*: We do not need to map over time when calling `value_and_grad` in this example as the `sse` function returns a scalar (despite taking vector inputs)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "56NPH9sZ-ZFq" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Value and gradient computed in 0.095 secs\n", + "SSE value: 0.0020846163677995366\n", + "SSE gradient (wrt each input): {'Current function [A]': array(-0.05775429), 'Separator porosity': array(0.00146983)}\n" + ] + } + ], + "source": [ + "# Simulate some experimental data using our original parameter settings\n", + "data = sim[\"Voltage [V]\"](t_eval)\n", + "\n", + "\n", + "# Sum-of-squared errors\n", + "def sse(t, inputs):\n", + " modelled = jax_solver.get_var(\"Voltage [V]\")(t_eval, inputs)\n", + " return jnp.sum((modelled - data) ** 2)\n", + "\n", + "\n", + "# Provide some predicted model inputs (these could come from a fitting procedure)\n", + "inputs_pred = {\n", + " \"Current function [A]\": 0.150,\n", + " \"Separator porosity\": 0.333,\n", + "}\n", + "\n", + "# Get the value and gradient of the SSE function\n", + "t_start = time.time()\n", + "value, gradient = jax.value_and_grad(sse, argnums=1)(t_eval, inputs_pred)\n", + "print(f\"Value and gradient computed in {time.time()-t_start:0.3} secs\")\n", + "print(\"SSE value: \", value)\n", + "print(\"SSE gradient (wrt each input): \", gradient)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Nj0ylzso8Yu_" + }, + "source": [ + "All of the above expressions can be JIT compiled (onto CPU) by using the `jax.jit` directive. Practically, this provides a wrap-around back to the Python interface of the IDAKLU Solver, so is only provided to afford maximum downstream compatibility (where JIT may be called outside of the user's control)." + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "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": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb b/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb index 849f1bdf47..78e1a161b4 100644 --- a/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb +++ b/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb @@ -920,9 +920,7 @@ "int_v_over_r2 = pybamm.Integral(v / r_var**2, r_var)\n", "int_v_over_r2_disc = disc.process_symbol(int_v_over_r2)\n", "print(\n", - " \"int(v/r^2) = {} is approximately equal to 4 * pi * sin(1), {}\".format(\n", - " int_v_over_r2_disc.evaluate(y=y), 4 * np.pi * np.sin(1)\n", - " )\n", + " f\"int(v/r^2) = {int_v_over_r2_disc.evaluate(y=y)} is approximately equal to 4 * pi * sin(1), {4 * np.pi * np.sin(1)}\"\n", ")" ] }, diff --git a/docs/source/user_guide/index.md b/docs/source/user_guide/index.md index 8b28fc6636..58ce04101a 100644 --- a/docs/source/user_guide/index.md +++ b/docs/source/user_guide/index.md @@ -57,8 +57,6 @@ glob: ../examples/notebooks/getting_started/tutorial-7-model-options.ipynb ../examples/notebooks/getting_started/tutorial-8-solver-options.ipynb ../examples/notebooks/getting_started/tutorial-9-changing-the-mesh.ipynb -../examples/notebooks/getting_started/tutorial-10-creating-a-model.ipynb -../examples/notebooks/getting_started/tutorial-11-creating-a-submodel.ipynb ``` ```{nbgallery} diff --git a/docs/source/user_guide/installation/gnu-linux-mac.rst b/docs/source/user_guide/installation/gnu-linux-mac.rst index d774285556..0be4b98e4c 100644 --- a/docs/source/user_guide/installation/gnu-linux-mac.rst +++ b/docs/source/user_guide/installation/gnu-linux-mac.rst @@ -8,22 +8,14 @@ Prerequisites To use PyBaMM, you must have Python 3.8, 3.9, 3.10, 3.11, or 3.12 installed. -.. tab:: Debian-based distributions (Debian, Ubuntu, Linux Mint) +.. tab:: Debian-based distributions (Debian, Ubuntu) - To install Python 3 on Debian-based distributions (Debian, Ubuntu, Linux Mint), open a terminal and run + To install Python 3 on Debian-based distributions (Debian, Ubuntu), open a terminal and run .. code:: bash - sudo apt update - sudo apt install python3 - -.. tab:: Fedora/CentOS - - On Fedora or CentOS, you can use DNF or Yum. For example - - .. code:: bash - - sudo dnf install python3 + sudo apt-get update + sudo apt-get install python3 .. tab:: macOS @@ -38,7 +30,8 @@ To use PyBaMM, you must have Python 3.8, 3.9, 3.10, 3.11, or 3.12 installed. .. code:: bash - brew install python3 + brew install python + Install PyBaMM -------------- @@ -84,11 +77,10 @@ library beforehand. .. tab:: macOS - In a terminal, run the following commands: + In a terminal, run the following command: .. code:: bash - brew install sundials pip install pybamm PyBaMM’s required dependencies (such as ``numpy``, ``casadi``, etc) will be @@ -97,74 +89,12 @@ installed automatically when you install PyBaMM using ``pip``. For an introduction to virtual environments, see (https://realpython.com/python-virtual-environments-a-primer/). -.. _scikits.odes-label: - -Optional - scikits.odes solver -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Users can install `scikits.odes `__ to utilize its interfaced SUNDIALS ODE and DAE `solvers `__ wrapped in PyBaMM. - -.. note:: - - Currently, only GNU/Linux and macOS are supported. - -.. note:: - - The ``scikits.odes`` solver is not supported on Python 3.12 yet. Please refer to https://github.com/bmcage/odes/issues/162. - There is support for Python 3.8, 3.9, 3.10, and 3.11. - -.. tab:: GNU/Linux - - In a terminal, run the following commands: - - .. code:: bash - - apt-get install libopenblas-dev - pip install wget cmake - pybamm_install_odes - - system (under ``~/.local``), before installing ``scikits.odes``. (Alternatively, one can install SUNDIALS without this script and run ``pip install pybamm[odes]`` to install ``pybamm`` with ``scikits.odes``.) - -.. tab:: macOS - - In a terminal, run the following command: - - .. code:: bash - - brew install openblas gcc gfortran - pip install wget cmake - pybamm_install_odes - -The ``pybamm_install_odes`` command, installed with PyBaMM, automatically downloads and installs the SUNDIALS library on your -system (under ``~/.local``), before installing `scikits.odes `__ . (Alternatively, one can install SUNDIALS without this script and run ``pip install pybamm[odes]`` to install ``pybamm`` with `scikits.odes `__) - -To avoid installation failures when using ``pip install pybamm[odes]``, make sure to set the ``SUNDIALS_INST`` environment variable. If you have installed SUNDIALS using Homebrew, set the variable to the appropriate location. For example: - -.. code:: bash - - export SUNDIALS_INST=$(brew --prefix sundials) - -Ensure that the path matches the installation location on your system. You can verify the installation location by running: - -.. code:: bash - - brew info sundials - -Look for the installation path, and use that path to set the ``SUNDIALS_INST`` variable. - -Note: The location where Homebrew installs SUNDIALS might vary based on the system architecture (ARM or Intel). Adjust the path in the ``export SUNDIALS_INST`` command accordingly. - -To avoid manual setup of path the ``pybamm_install_odes`` is recommended for a smoother installation process, as it takes care of automatically downloading and installing the SUNDIALS library on your system. Optional - JaxSolver ~~~~~~~~~~~~~~~~~~~~ Users can install ``jax`` and ``jaxlib`` to use the Jax solver. -.. note:: - - The Jax solver is only supported for Python versions 3.9 through 3.12. - .. code:: bash pip install "pybamm[jax]" diff --git a/docs/source/user_guide/installation/index.rst b/docs/source/user_guide/installation/index.rst index f0c12d46fc..778f64c3f9 100644 --- a/docs/source/user_guide/installation/index.rst +++ b/docs/source/user_guide/installation/index.rst @@ -30,7 +30,7 @@ It can be installed using ``pip`` or ``conda``, or from source. .. code:: bash - brew install sundials && pip install pybamm + pip install pybamm .. tab:: conda @@ -45,9 +45,8 @@ It can be installed using ``pip`` or ``conda``, or from source. Optional solvers ---------------- -Following GNU/Linux and macOS solvers are optionally available: +The following solvers are optionally available: -* `scikits.odes `_ -based solver, see `Optional - scikits.odes solver `_. * `jax `_ -based solver, see `Optional - JaxSolver `_. Dependencies @@ -59,15 +58,19 @@ Required dependencies PyBaMM requires the following dependencies. -================================================================ ========================== -Package Minimum supported version -================================================================ ========================== -`NumPy `__ 1.23.5 -`SciPy `__ 1.9.3 -`CasADi `__ 3.6.3 -`Xarray `__ 2022.6.0 -`Anytree `__ 2.8.0 -================================================================ ========================== +=================================================================== ========================== +Package Minimum supported version +=================================================================== ========================== +`NumPy `__ 1.23.5 +`SciPy `__ 1.9.3 +`CasADi `__ 3.6.3 +`Xarray `__ 2022.6.0 +`Anytree `__ 2.8.0 +`SymPy `__ 1.9.3 +`typing-extensions `__ 4.10.0 +`pandas `__ 1.5.0 +`pooch `__ 1.8.1 +=================================================================== ========================== .. _install.optional_dependencies: @@ -95,19 +98,6 @@ Dependency Minimum Version p `matplotlib `__ 3.6.0 plot To plot various battery models, and analyzing battery performance. =========================================================== ================== ================== ================================================================== -.. _install.pandas_dependencies: - -Pandas dependencies -^^^^^^^^^^^^^^^^^^^ - -Installable with ``pip install "pybamm[pandas]"`` - -=========================================================== ================== ================== ================================================================== -Dependency Minimum Version pip extra Notes -=========================================================== ================== ================== ================================================================== -`pandas `__ 1.5.0 pandas For data manipulation and analysis. -=========================================================== ================== ================== ================================================================== - .. _install.docs_dependencies: Docs dependencies @@ -154,8 +144,10 @@ Dependency `pre-commit `__ \- dev For managing and maintaining multi-language pre-commit hooks. `ruff `__ \- dev For code formatting. `nox `__ \- dev For running testing sessions in multiple environments. -`coverage `__ \- dev For calculating coverage of tests. -`pytest `__ 6.0.0 dev For running Jupyter notebooks tests. +`pytest-cov `__ \- dev For calculating test coverage. +`parameterized `__ \- dev For test parameterization. +`pytest `__ 6.0.0 dev For running the test suites. +`pytest-doctestplus `__ \- dev For running doctests. `pytest-xdist `__ \- dev For running tests in parallel across distributed workers. `nbmake `__ \- dev A ``pytest`` plugin for executing Jupyter notebooks. ================================================================================ ================== ================== ============================================================= @@ -173,19 +165,6 @@ Dependency Minimum Version p `pybtex `__ 0.24.0 cite BibTeX-compatible bibliography processor. =========================================================== ================== ================== ========================================= -.. _install.latexify_dependencies: - -Latexify dependencies -^^^^^^^^^^^^^^^^^^^^^ - -Installable with ``pip install "pybamm[latexify]"`` - -=========================================================== ================== ================== ========================= -Dependency Minimum Version pip extra Notes -=========================================================== ================== ================== ========================= -`sympy `__ 1.9.3 latexify For symbolic mathematics. -=========================================================== ================== ================== ========================= - .. _install.bpx_dependencies: bpx dependencies @@ -226,23 +205,6 @@ Dependency Minimu `jaxlib `__ 0.4.20 jax Support library for JAX ========================================================================= ================== ================== ======================= -.. _install.odes_dependencies: - -odes dependencies -^^^^^^^^^^^^^^^^^ - -Installable with ``pip install "pybamm[odes]"`` - -======================================================================================================================================= ================== ================== ============================= -Dependency Minimum Version pip extra Notes -======================================================================================================================================= ================== ================== ============================= -`scikits.odes `__ \- odes For scikits ODE & DAE solvers -======================================================================================================================================= ================== ================== ============================= - -.. note:: - - Before running ``pip install "pybamm[odes]"``, make sure to install ``scikits.odes`` build-time requirements as described `here `_ . - Full installation guide ----------------------- diff --git a/docs/source/user_guide/installation/install-from-docker.rst b/docs/source/user_guide/installation/install-from-docker.rst index 61f99817c7..f8fe733098 100644 --- a/docs/source/user_guide/installation/install-from-docker.rst +++ b/docs/source/user_guide/installation/install-from-docker.rst @@ -20,35 +20,11 @@ Pulling the Docker image Use the following command to pull the PyBaMM Docker image from Docker Hub: -.. tab:: No optional solver - .. code:: bash - - docker pull pybamm/pybamm:latest - -.. tab:: Scikits.odes solver - - .. code:: bash - - docker pull pybamm/pybamm:odes - -.. tab:: JAX solver - - .. code:: bash - - docker pull pybamm/pybamm:jax - -.. tab:: IDAKLU solver - - .. code:: bash - - docker pull pybamm/pybamm:idaklu - -.. tab:: All solvers +.. code:: bash - .. code:: bash + docker pull pybamm/pybamm - docker pull pybamm/pybamm:all Running the Docker container ---------------------------- @@ -57,40 +33,23 @@ Once you have pulled the Docker image, you can run a Docker container with the P 1. In your terminal, use the following command to start a Docker container from the pulled image: -.. tab:: Basic - - .. code:: bash - - docker run -it pybamm/pybamm:latest -.. tab:: ODES Solver - - .. code:: bash - - docker run -it pybamm/pybamm:odes - -.. tab:: JAX Solver - - .. code:: bash - - docker run -it pybamm/pybamm:jax - -.. tab:: IDAKLU Solver - - .. code:: bash - - docker run -it pybamm/pybamm:idaklu - -.. tab:: All Solver +.. code:: bash - .. code:: bash + docker run -it pybamm/pybamm - docker run -it pybamm/pybamm:all 2. You will now be inside the Docker container's shell. You can use PyBaMM and its dependencies as if you were in a virtual environment. 3. You can execute PyBaMM-related commands, run tests develop & contribute from the container. +.. note:: + + The default user for the container is ``pybamm`` with ``pybamm`` as password. The user belongs to + ``sudoers`` and ``root`` group, so the sudo command can be issued to install additional packages to + the container. After a clean install, ``sudo apt-get update`` should be executed to update the source + list. Additional packages can be installed using ``sudo apt-get install [package_name]``. + Exiting the Docker container ---------------------------- @@ -137,59 +96,9 @@ If you want to build the PyBaMM Docker image locally from the PyBaMM source code conda activate pybamm -Building Docker images with optional arguments ----------------------------------------------- - -When building the PyBaMM Docker images locally, you have the option to include specific solvers by using optional arguments. These solvers include: - -- ``IDAKLU``: For IDA solver provided by the SUNDIALS plus KLU. -- ``ODES``: For scikits.odes solver for ODE & DAE problems. -- ``JAX``: For Jax solver. -- ``ALL``: For all the above solvers. - -To build the Docker images with optional arguments, you can follow these steps for each solver: - -.. tab:: Scikits.odes solver - - .. code-block:: bash - - docker build -t pybamm:odes -f scripts/Dockerfile --build-arg ODES=true . - -.. tab:: JAX solver - - .. code-block:: bash - - docker build -t pybamm:jax -f scripts/Dockerfile --build-arg JAX=true . - -.. tab:: IDAKLU solver - - .. code-block:: bash - - docker build -t pybamm:idaklu -f scripts/Dockerfile --build-arg IDAKLU=true . - -.. tab:: All solvers - - .. code-block:: bash - - docker build -t pybamm:all -f scripts/Dockerfile --build-arg ALL=true . - -After building the Docker images with the desired solvers, use the ``docker run`` command followed by the desired image name. For example, to run a container from the image built with all optional solvers: - -.. code-block:: bash - - docker run -it pybamm:all - -Activate PyBaMM development environment inside docker container using: - -.. code-block:: bash - - conda activate pybamm - -If you want to exit the Docker container's shell, you can simply type: - -.. code-block:: bash +.. note:: - exit + PyBaMM's Docker image comes with all available solvers by default. These solvers include ``IDAKLU`` IDAS solver provided by the SUNDIALS linked with SuiteSparse's KLU and the ``JAX`` solver. Using Git inside a running Docker container diff --git a/docs/source/user_guide/installation/install-from-source.rst b/docs/source/user_guide/installation/install-from-source.rst index 26b6b5cf20..ea664b4a5b 100644 --- a/docs/source/user_guide/installation/install-from-source.rst +++ b/docs/source/user_guide/installation/install-from-source.rst @@ -25,41 +25,45 @@ or download the source archive on the repository's homepage. To install PyBaMM, you will need: -- Python 3 (PyBaMM supports versions 3.8, 3.9, 3.10, 3.11, and 3.12) +- Python 3 (PyBaMM supports versions 3.9, 3.10, 3.11, and 3.12) - The Python headers file for your current Python version. - A BLAS library (for instance `openblas `_). - A C compiler (ex: ``gcc``). - A Fortran compiler (ex: ``gfortran``). - ``graphviz`` (optional), if you wish to build the documentation locally. +- ``pandoc`` (optional) to convert the example Jupyter notebooks when building the documentation. You can install the above with -.. tab:: Ubuntu +.. tab:: Ubuntu/Debian .. code:: bash - sudo apt install python3.X python3.X-dev libopenblas-dev gcc gfortran graphviz + sudo apt install python3.X python3.X-dev libopenblas-dev gcc gfortran graphviz cmake pandoc Where ``X`` is the version sub-number. - .. note:: - - On Windows, you can install ``graphviz`` using the `Chocolatey `_ package manager, or - follow the instructions on the `graphviz website `_. - .. tab:: MacOS .. code:: bash - brew install python openblas gcc gfortran graphviz libomp + brew install python openblas gcc gfortran graphviz libomp cmake pandoc + +.. note:: + + If you are using some other linux distribution you can install the equivalent packages for ``python3, cmake, gcc, gfortran, openblas, pandoc``. + + On Windows, you can install ``graphviz`` using the `Chocolatey `_ package manager, or follow the instructions on the `graphviz website `_. Finally, we recommend using `Nox `_. -You can install it with +You can install it to your local user account (make sure you are not within a virtual environment) with .. code:: bash python3.X -m pip install --user nox +Note that running ``nox`` will create new virtual environments for you to use, so you do not need to create one yourself. + Depending on your operating system, you may or may not have ``pip`` installed along Python. If ``pip`` is not found, you probably want to install the ``python3-pip`` package. @@ -81,6 +85,24 @@ If you are running windows, you can simply skip this section and jump to :ref:`p This will download, compile and install the SuiteSparse and SUNDIALS libraries. Both libraries are installed in ``~/.local``. +For users requiring more control over the installation process, the ``pybamm-requires`` session supports additional command-line arguments: + +- ``--install-dir``: Specify a custom installation directory for SUNDIALS and SuiteSparse. + + Example: + + .. code:: bash + + nox -s pybamm-requires -- --install-dir [custom_directory_path] + +- ``--force``: Force the installation of SUNDIALS and SuiteSparse, even if they are already found in the specified directory. + + Example: + + .. code:: bash + + nox -s pybamm-requires -- --force + Manual install of build time requirements ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -98,6 +120,25 @@ PyBaMM ships with a Python script that automates points 2. and 3. You can run it python scripts/install_KLU_Sundials.py +This script supports optional arguments for custom installations: + +- ``--install-dir``: Specify a custom installation directory for SUNDIALS and SuiteSparse. + By default, they are installed in ``~/.local``. + + Example: + + .. code:: bash + + python scripts/install_KLU_Sundials.py --install-dir [custom_directory_path] + +- ``--force``: Force the installation of SUNDIALS and SuiteSparse, even if they are already found in the specified directory. + + Example: + + .. code:: bash + + python scripts/install_KLU_Sundials.py --force + .. _pybamm-install: Installing PyBaMM @@ -121,7 +162,7 @@ It comes ready with PyBaMM and some useful development tools like `pre-commit `__. +Follow the `installation instructions for PyBaMM on Linux `__. Using Visual Studio Code with the WSL --------------------------------------- diff --git a/docs/source/user_guide/installation/windows.rst b/docs/source/user_guide/installation/windows.rst index d99d1f2eb2..02d9f8dd29 100644 --- a/docs/source/user_guide/installation/windows.rst +++ b/docs/source/user_guide/installation/windows.rst @@ -6,7 +6,7 @@ Windows Prerequisites ------------- -To use PyBaMM, you must have Python 3.8, 3.9, 3.10, 3.11, or 3.12 installed. +To use PyBaMM, you must have Python 3.9, 3.10, 3.11, or 3.12 installed. To install Python 3 download the installation files from `Python’s website `__. Make sure to @@ -71,10 +71,6 @@ Optional - JaxSolver Users can install ``jax`` and ``jaxlib`` to use the Jax solver. -.. note:: - - The Jax solver is only supported for Python versions 3.9 through 3.12. - .. code:: bash pip install "pybamm[jax]" diff --git a/examples/scripts/SPMe_step.py b/examples/scripts/SPMe_step.py index 90d6f3d017..f277c0e790 100644 --- a/examples/scripts/SPMe_step.py +++ b/examples/scripts/SPMe_step.py @@ -32,12 +32,14 @@ # step model dt = 500 +# t_eval is an array of time in the interval 0 to dt, dt being size of the step. +t_eval = np.array([0, 50, 100, 200, 500]) time = 0 end_time = solution.t[-1] step_solver = pybamm.CasadiSolver() step_solution = None while time < end_time: - step_solution = step_solver.step(step_solution, model, dt=dt, npts=10) + step_solution = step_solver.step(step_solution, model, dt=dt, t_eval=t_eval) time += dt # plot diff --git a/examples/scripts/compare_comsol/compare_comsol_DFN.py b/examples/scripts/compare_comsol/compare_comsol_DFN.py index 45bc4182ef..c2730e71e7 100644 --- a/examples/scripts/compare_comsol/compare_comsol_DFN.py +++ b/examples/scripts/compare_comsol/compare_comsol_DFN.py @@ -1,6 +1,7 @@ import pybamm import os -import pickle +import json +import numpy as np import scipy.interpolate as interp # change working directory to the root of pybamm @@ -16,10 +17,12 @@ C_rate = "1" # choose the key from the above dictionary of available results # load the comsol results +data_loader = pybamm.DataLoader() comsol_results_path = pybamm.get_parameters_filepath( - f"input/comsol_results/comsol_{C_rate}C.pickle" + f"{data_loader.get_data(f'comsol_{C_rate}C.json')}" ) -comsol_variables = pickle.load(open(comsol_results_path, "rb")) + +comsol_variables = json.load(open(comsol_results_path)) "-----------------------------------------------------------------------------" "Create and solve pybamm model" @@ -53,13 +56,13 @@ disc.process_model(pybamm_model) # solve model at comsol times -time = comsol_variables["time"] +time = np.array(comsol_variables["time"]) pybamm_solution = pybamm.CasadiSolver(mode="fast").solve(pybamm_model, time) # Make Comsol 'model' for comparison whole_cell = ["negative electrode", "separator", "positive electrode"] -comsol_t = comsol_variables["time"] +comsol_t = np.array(comsol_variables["time"]) L_x = param.evaluate(pybamm_model.param.L_x) @@ -69,13 +72,13 @@ def get_interp_fun(variable_name, domain): :class:`pybamm.QuickPlot` (interpolate in space to match edges, and then create function to interpolate in time) """ - variable = comsol_variables[variable_name] + variable = np.array(comsol_variables[variable_name]) if domain == ["negative electrode"]: - comsol_x = comsol_variables["x_n"] + comsol_x = np.array(comsol_variables["x_n"]) elif domain == ["positive electrode"]: - comsol_x = comsol_variables["x_p"] + comsol_x = np.array(comsol_variables["x_p"]) elif domain == whole_cell: - comsol_x = comsol_variables["x"] + comsol_x = np.array(comsol_variables["x"]) # Make sure to use dimensional space pybamm_x = mesh[domain].nodes @@ -95,7 +98,9 @@ def get_interp_fun(variable_name, domain): comsol_phi_n = get_interp_fun("phi_n", ["negative electrode"]) comsol_phi_e = get_interp_fun("phi_e", whole_cell) comsol_phi_p = get_interp_fun("phi_p", ["positive electrode"]) -comsol_voltage = pybamm.Interpolant(comsol_t, comsol_variables["voltage"], pybamm.t) +comsol_voltage = pybamm.Interpolant( + comsol_t, np.array(comsol_variables["voltage"]), pybamm.t +) comsol_voltage.mesh = None comsol_voltage.secondary_mesh = None diff --git a/examples/scripts/compare_comsol/discharge_curve.py b/examples/scripts/compare_comsol/discharge_curve.py index 9e437ce301..5e01de8586 100644 --- a/examples/scripts/compare_comsol/discharge_curve.py +++ b/examples/scripts/compare_comsol/discharge_curve.py @@ -1,7 +1,7 @@ import pybamm import numpy as np import os -import pickle +import json import matplotlib.pyplot as plt # change working directory to the root of pybamm @@ -55,15 +55,16 @@ plt.ylabel(r"$\vert V - V_{comsol} \vert$", fontsize=20) colors = iter(plt.cycler(color="bgrcmyk")) +data_loader = pybamm.DataLoader() for key, C_rate in C_rates.items(): current = 24 * C_rate # load the comsol results comsol_results_path = pybamm.get_parameters_filepath( - f"input/comsol_results/comsol_{key}C.pickle" + f"{data_loader.get_data(f'comsol_{key}C.json')}" ) - comsol_variables = pickle.load(open(comsol_results_path, "rb")) - comsol_time = comsol_variables["time"] - comsol_voltage = comsol_variables["voltage"] + comsol_variables = json.load(open(comsol_results_path)) + comsol_time = np.array(comsol_variables["time"]) + comsol_voltage = np.array(comsol_variables["voltage"]) # solve model at comsol times t = comsol_time diff --git a/examples/scripts/compare_dae_solver.py b/examples/scripts/compare_dae_solver.py index c98309a2ed..815b458f1a 100644 --- a/examples/scripts/compare_dae_solver.py +++ b/examples/scripts/compare_dae_solver.py @@ -38,16 +38,6 @@ Please consult installation instructions on GitHub. """ ) -if pybamm.have_scikits_odes(): - scikits_sol = pybamm.ScikitsDaeSolver(atol=1e-8, rtol=1e-8).solve(model, t_eval) - solutions.append(scikits_sol) -else: - pybamm.logger.error( - """ - Cannot solve model with Scikits DAE solver as solver is not installed. - Please consult installation instructions on GitHub. - """ - ) # plot plot = pybamm.QuickPlot(solutions) diff --git a/examples/scripts/compare_lithium_ion_heat_of_mixing.py b/examples/scripts/compare_lithium_ion_heat_of_mixing.py new file mode 100644 index 0000000000..455f5b1e8b --- /dev/null +++ b/examples/scripts/compare_lithium_ion_heat_of_mixing.py @@ -0,0 +1,112 @@ +# +# Compare SPMe model with and without heat of mixing +# +import pybamm +import numpy as np +import matplotlib.pyplot as plt + +pybamm.set_logging_level("INFO") + +# load models +models = [ + pybamm.lithium_ion.SPMe( + {"heat of mixing": "true", "thermal": "lumped"}, name="SPMe with heat of mixing" + ), + pybamm.lithium_ion.SPMe({"thermal": "lumped"}, name="SPMe without heat of mixing"), +] + +# set parametrisation (Ecker et al., 2015) +parameter_values = pybamm.ParameterValues("Ecker2015") + +# set mesh +# (increased number of points in particles to avoid oscillations for high C-rates) +var_pts = {"x_n": 16, "x_s": 8, "x_p": 16, "r_n": 128, "r_p": 128} + +# set the constant current discharge C-rate +C_rate = 5 + +# set the simulation time and increase the number of points +# for better integration in time +t_eval = np.linspace(0, 720, 360) + +# set up an extra plot with the heat of mixing vs time in each electrode and +# the integrated heat of mixing vs time in each electrode to compare with +# Figure 6(a) from Richardson et al. (2021) +fig, axs = plt.subplots(2, len(models), figsize=(12, 7)) + +# extract some of the parameters +L_n = parameter_values["Negative electrode thickness [m]"] +L_p = parameter_values["Positive electrode thickness [m]"] +A = parameter_values["Electrode width [m]"] * parameter_values["Electrode height [m]"] + +# create and run simulations +sims = [] +for m, model in enumerate(models): + sim = pybamm.Simulation( + model, parameter_values=parameter_values, var_pts=var_pts, C_rate=C_rate + ) + sim.solve(t_eval) + sims.append(sim) + + # extract solution for plotting + sol = sim.solution + + # extract variables from the solution + time = sol["Time [h]"].entries + Q_mix = sol["Heat of mixing [W.m-3]"].entries + + # heat of mixing in negative and positive electrodes multiplied by the electrode + # width, represents the integral of heat of mixing term across each of the + # electrodes (W.m-2) + Q_mix_n = Q_mix[0, :] * L_n + Q_mix_p = Q_mix[-1, :] * L_p + + # heat of mixing integrals (J.m-2) + Q_mix_n_int = 0.0 + Q_mix_p_int = 0.0 + + # data for plotting + Q_mix_n_plt = [] + Q_mix_p_plt = [] + + # performs integration in time + for i, t in enumerate(time[1:]): + dt = (t - time[i]) * 3600 # seconds + Q_mix_n_avg = (Q_mix_n[i] + Q_mix_n[i + 1]) * 0.5 + Q_mix_p_avg = (Q_mix_p[i] + Q_mix_p[i + 1]) * 0.5 + # convert J to kJ and divide the integral by the electrode area A to compare + # with Figure 6(a) from Richardson et al. (2021) + Q_mix_n_int += Q_mix_n_avg * dt / 1000 / A + Q_mix_p_int += Q_mix_p_avg * dt / 1000 / A + Q_mix_n_plt.append(Q_mix_n_int) + Q_mix_p_plt.append(Q_mix_p_int) + + # plots heat of mixing in each electrode vs time in minutes + axs[0, m].plot(time * 60, Q_mix_n, ls="-", label="Negative electrode") + axs[0, m].plot(time * 60, Q_mix_p, ls="--", label="Positive electrode") + axs[0, m].set_title(f"{model.name}") + axs[0, m].set_xlabel("Time [min]") + axs[0, m].set_ylabel("Heat of mixing [W.m-2]") + axs[0, m].grid(True) + axs[0, m].legend() + + # plots integrated heat of mixing in each electrode vs time in minutes + axs[1, m].plot(time[1:] * 60, Q_mix_n_plt, ls="-", label="Negative electrode") + axs[1, m].plot(time[1:] * 60, Q_mix_p_plt, ls="--", label="Positive electrode") + axs[1, m].set_xlabel("Time [min]") + axs[1, m].set_ylabel("Integrated heat of mixing [kJ.m-2]") + axs[1, m].grid(True) + axs[1, m].legend() + +# plot +pybamm.dynamic_plot( + sims, + output_variables=[ + "X-averaged cell temperature [K]", + "X-averaged heat of mixing [W.m-3]", + "X-averaged total heating [W.m-3]", + "Heat of mixing [W.m-3]", + "Voltage [V]", + "Current [A]", + ], +) diff --git a/examples/scripts/custom_model.py b/examples/scripts/custom_model.py index 4c6dcf3fad..a0e7ef2b80 100644 --- a/examples/scripts/custom_model.py +++ b/examples/scripts/custom_model.py @@ -17,61 +17,61 @@ model.submodels["current collector"] = pybamm.current_collector.Uniform(model.param) model.submodels["thermal"] = pybamm.thermal.isothermal.Isothermal(model.param) model.submodels["porosity"] = pybamm.porosity.Constant(model.param, model.options) -model.submodels[ - "electrolyte diffusion" -] = pybamm.electrolyte_diffusion.ConstantConcentration(model.param) -model.submodels[ - "electrolyte conductivity" -] = pybamm.electrolyte_conductivity.LeadingOrder(model.param) +model.submodels["electrolyte diffusion"] = ( + pybamm.electrolyte_diffusion.ConstantConcentration(model.param) +) +model.submodels["electrolyte conductivity"] = ( + pybamm.electrolyte_conductivity.LeadingOrder(model.param) +) # Loop over negative and positive electrode domains for some submodels for domain in ["negative", "positive"]: model.submodels[f"{domain} active material"] = pybamm.active_material.Constant( model.param, domain, model.options ) - model.submodels[ - f"{domain} electrode potential" - ] = pybamm.electrode.ohm.LeadingOrder(model.param, domain) + model.submodels[f"{domain} electrode potential"] = ( + pybamm.electrode.ohm.LeadingOrder(model.param, domain) + ) model.submodels[f"{domain} particle"] = pybamm.particle.XAveragedPolynomialProfile( model.param, domain, options={**model.options, "particle": "uniform profile"}, phase="primary", ) - model.submodels[ - f"{domain} total particle concentration" - ] = pybamm.particle.TotalConcentration( - model.param, domain, model.options, phase="primary" + model.submodels[f"{domain} total particle concentration"] = ( + pybamm.particle.TotalConcentration( + model.param, domain, model.options, phase="primary" + ) ) - model.submodels[ - f"{domain} open-circuit potential" - ] = pybamm.open_circuit_potential.SingleOpenCircuitPotential( - model.param, - domain, - "lithium-ion main", - options=model.options, - phase="primary", + model.submodels[f"{domain} open-circuit potential"] = ( + pybamm.open_circuit_potential.SingleOpenCircuitPotential( + model.param, + domain, + "lithium-ion main", + options=model.options, + phase="primary", + ) ) model.submodels[f"{domain} interface"] = pybamm.kinetics.InverseButlerVolmer( model.param, domain, "lithium-ion main", options=model.options ) - model.submodels[ - f"{domain} interface utilisation" - ] = pybamm.interface_utilisation.Full(model.param, domain, model.options) - model.submodels[ - f"{domain} interface current" - ] = pybamm.kinetics.CurrentForInverseButlerVolmer( - model.param, domain, "lithium-ion main" + model.submodels[f"{domain} interface utilisation"] = ( + pybamm.interface_utilisation.Full(model.param, domain, model.options) ) - model.submodels[ - f"{domain} surface potential difference [V]" - ] = pybamm.electrolyte_conductivity.surface_potential_form.Explicit( - model.param, domain, model.options + model.submodels[f"{domain} interface current"] = ( + pybamm.kinetics.CurrentForInverseButlerVolmer( + model.param, domain, "lithium-ion main" + ) + ) + model.submodels[f"{domain} surface potential difference [V]"] = ( + pybamm.electrolyte_conductivity.surface_potential_form.Explicit( + model.param, domain, model.options + ) + ) + model.submodels[f"{domain} particle mechanics"] = ( + pybamm.particle_mechanics.NoMechanics(model.param, domain, model.options) ) - model.submodels[ - f"{domain} particle mechanics" - ] = pybamm.particle_mechanics.NoMechanics(model.param, domain, model.options) model.submodels[f"{domain} sei"] = pybamm.sei.NoSEI( model.param, domain, model.options ) diff --git a/examples/scripts/drive_cycle.py b/examples/scripts/drive_cycle.py index 5fbbce2296..e884aa679c 100644 --- a/examples/scripts/drive_cycle.py +++ b/examples/scripts/drive_cycle.py @@ -15,8 +15,9 @@ # import drive cycle from file +data_loader = pybamm.DataLoader() drive_cycle = pd.read_csv( - "pybamm/input/drive_cycles/US06.csv", comment="#", header=None + data_loader.get_data("US06.csv"), comment="#", header=None ).to_numpy() # create interpolant diff --git a/examples/scripts/emperical_hysteresis.py b/examples/scripts/empirical_hysteresis.py similarity index 95% rename from examples/scripts/emperical_hysteresis.py rename to examples/scripts/empirical_hysteresis.py index 3f3fc7c640..e21d201b4c 100644 --- a/examples/scripts/emperical_hysteresis.py +++ b/examples/scripts/empirical_hysteresis.py @@ -122,9 +122,9 @@ def exchange_current_density_average(sto): "": exchange_current_density_lithiation, "Negative electrode delithiation exchange-current density [A.m-2]" "": exchange_current_density_delithiation, - "Negative electrode diffusivity [m2.s-1]": 3.3e-14, - "Negative electrode lithiation diffusivity [m2.s-1]": 4e-14, - "Negative electrode delithiation diffusivity [m2.s-1]": 2.6e-14, + "Negative particle diffusivity [m2.s-1]": 3.3e-14, + "Negative particle lithiation diffusivity [m2.s-1]": 4e-14, + "Negative particle delithiation diffusivity [m2.s-1]": 2.6e-14, }, check_already_exists=False, ) diff --git a/examples/scripts/experiment_drive_cycle.py b/examples/scripts/experiment_drive_cycle.py index e6793b9b20..9e0a9415a0 100644 --- a/examples/scripts/experiment_drive_cycle.py +++ b/examples/scripts/experiment_drive_cycle.py @@ -10,8 +10,9 @@ pybamm.set_logging_level("INFO") # import drive cycle from file +data_loader = pybamm.DataLoader() drive_cycle_current = pd.read_csv( - "pybamm/input/drive_cycles/US06.csv", comment="#", header=None + f"{data_loader.get_data('US06.csv')}", comment="#", header=None ).to_numpy() diff --git a/examples/scripts/minimal_example_of_lookup_tables.py b/examples/scripts/minimal_example_of_lookup_tables.py index 1c93e311c0..24aba26a44 100644 --- a/examples/scripts/minimal_example_of_lookup_tables.py +++ b/examples/scripts/minimal_example_of_lookup_tables.py @@ -22,7 +22,7 @@ def process_2D(name, data): parameter_values = pybamm.ParameterValues(pybamm.parameter_sets.Chen2020) # overwrite the diffusion coefficient with a 2D lookup table -D_s_n = parameter_values["Negative electrode diffusivity [m2.s-1]"] +D_s_n = parameter_values["Negative particle diffusivity [m2.s-1]"] df = pd.DataFrame( { "T": [0, 0, 25, 25, 45, 45], @@ -31,7 +31,7 @@ def process_2D(name, data): } ) df["T"] = df["T"] + 273.15 -D_s_n_data = process_2D("Negative electrode diffusivity [m2.s-1]", df) +D_s_n_data = process_2D("Negative particle diffusivity [m2.s-1]", df) def D_s_n(sto, T): @@ -39,7 +39,7 @@ def D_s_n(sto, T): return pybamm.Interpolant(x, y, [T, sto], name) -parameter_values["Negative electrode diffusivity [m2.s-1]"] = D_s_n +parameter_values["Negative particle diffusivity [m2.s-1]"] = D_s_n k_n = parameter_values["Negative electrode exchange-current density [A.m-2]"] diff --git a/examples/scripts/multiprocess_inputs.py b/examples/scripts/multiprocess_inputs.py new file mode 100644 index 0000000000..16da6b1453 --- /dev/null +++ b/examples/scripts/multiprocess_inputs.py @@ -0,0 +1,18 @@ +import pybamm +import numpy as np + +# create the model +model = pybamm.lithium_ion.DFN() + +# set the default model parameters +param = model.default_parameter_values + +# change the current function to be an input parameter +param["Current function [A]"] = "[input]" + +simulation = pybamm.Simulation(model, parameter_values=param) + +# solve the model at the given time points, passing multiple current values as inputs +t_eval = np.linspace(0, 600, 300) +inputs = [{"Current function [A]": x} for x in range(1, 3)] +sol = simulation.solve(t_eval, inputs=inputs) diff --git a/noxfile.py b/noxfile.py index a670b48e17..7237786ef6 100644 --- a/noxfile.py +++ b/noxfile.py @@ -5,6 +5,7 @@ # Options to modify nox behaviour +nox.options.default_venv_backend = "virtualenv" nox.options.reuse_existing_virtualenvs = True if sys.platform != "win32": nox.options.sessions = ["pre-commit", "pybamm-requires", "unit"] @@ -16,6 +17,7 @@ PYBAMM_ENV = { "SUNDIALS_INST": f"{homedir}/.local", "LD_LIBRARY_PATH": f"{homedir}/.local/lib", + "PYTHONIOENCODING": "utf-8", } VENV_DIR = Path("./venv").resolve() @@ -38,17 +40,23 @@ def set_environment_variables(env_dict, session): @nox.session(name="pybamm-requires") def run_pybamm_requires(session): - """Download, compile, and install the build-time requirements for Linux and macOS: the SuiteSparse and SUNDIALS libraries.""" + """Download, compile, and install the build-time requirements for Linux and macOS. Supports --install-dir for custom installation paths and --force to force installation.""" set_environment_variables(PYBAMM_ENV, session=session) if sys.platform != "win32": - session.install("wget", "cmake", silent=False) - session.run("python", "scripts/install_KLU_Sundials.py") + session.install("cmake", silent=False) + session.run("python", "scripts/install_KLU_Sundials.py", *session.posargs) if not os.path.exists("./pybind11"): session.run( "git", "clone", + "--depth", + "1", + "--branch", + "v2.12.0", "https://github.com/pybind/pybind11.git", "pybind11/", + "-c", + "advice.detachedHead=false", external=True, ) else: @@ -59,55 +67,28 @@ def run_pybamm_requires(session): def run_coverage(session): """Run the coverage tests and generate an XML report.""" set_environment_variables(PYBAMM_ENV, session=session) - session.install("coverage", silent=False) - # Temporary fix for Python 3.12 CI. TODO: remove after - # https://bitbucket.org/pybtex-devs/pybtex/issues/169/replace-pkg_resources-with - # is fixed session.install("setuptools", silent=False) - if sys.platform != "win32": - if sys.version_info > (3, 12): - session.install("-e", ".[all,jax]", silent=False) - else: - session.install("-e", ".[all,jax,odes]", silent=False) - else: - if sys.version_info < (3, 9): - session.install("-e", ".[all]", silent=False) - else: - session.install("-e", ".[all,jax]", silent=False) - session.run("coverage", "run", "run-tests.py", "--nosub") - session.run("coverage", "combine") - session.run("coverage", "xml") + session.install("coverage", silent=False) + session.install("-e", ".[all,dev,jax]", silent=False) + session.run("pytest", "--cov=pybamm", "--cov-report=xml", "tests/unit") @nox.session(name="integration") def run_integration(session): """Run the integration tests.""" set_environment_variables(PYBAMM_ENV, session=session) - # Temporary fix for Python 3.12 CI. TODO: remove after - # https://bitbucket.org/pybtex-devs/pybtex/issues/169/replace-pkg_resources-with - # is fixed session.install("setuptools", silent=False) - if sys.platform != "win32": - if sys.version_info > (3, 12): - session.install("-e", ".[all,jax]", silent=False) - else: - session.install("-e", ".[all,jax,odes]", silent=False) - else: - if sys.version_info < (3, 9): - session.install("-e", ".[all]", silent=False) - else: - session.install("-e", ".[all,jax]", silent=False) + session.install("-e", ".[all,dev,jax]", silent=False) session.run("python", "run-tests.py", "--integration") @nox.session(name="doctests") def run_doctests(session): """Run the doctests and generate the output(s) in the docs/build/ directory.""" - # Temporary fix for Python 3.12 CI. TODO: remove after - # https://bitbucket.org/pybtex-devs/pybtex/issues/169/replace-pkg_resources-with - # is fixed + # TODO: Temporary fix for Python 3.12 CI. + # See: https://bitbucket.org/pybtex-devs/pybtex/issues/169/ session.install("setuptools", silent=False) - session.install("-e", ".[all,docs]", silent=False) + session.install("-e", ".[all,dev,docs]", silent=False) session.run("python", "run-tests.py", "--doctest") @@ -115,20 +96,8 @@ def run_doctests(session): def run_unit(session): """Run the unit tests.""" set_environment_variables(PYBAMM_ENV, session=session) - # Temporary fix for Python 3.12 CI. TODO: remove after - # https://bitbucket.org/pybtex-devs/pybtex/issues/169/replace-pkg_resources-with - # is fixed session.install("setuptools", silent=False) - if sys.platform != "win32": - if sys.version_info > (3, 12): - session.install("-e", ".[all,jax]", silent=False) - else: - session.install("-e", ".[all,jax,odes]", silent=False) - else: - if sys.version_info < (3, 9): - session.install("-e", ".[all]", silent=False) - else: - session.install("-e", ".[all,jax]", silent=False) + session.install("-e", ".[all,dev,jax]", silent=False) session.run("python", "run-tests.py", "--unit") @@ -136,9 +105,6 @@ def run_unit(session): def run_examples(session): """Run the examples tests for Jupyter notebooks.""" set_environment_variables(PYBAMM_ENV, session=session) - # Temporary fix for Python 3.12 CI. TODO: remove after - # https://bitbucket.org/pybtex-devs/pybtex/issues/169/replace-pkg_resources-with - # is fixed session.install("setuptools", silent=False) session.install("-e", ".[all,dev]", silent=False) notebooks_to_test = session.posargs if session.posargs else [] @@ -153,7 +119,7 @@ def run_scripts(session): # https://bitbucket.org/pybtex-devs/pybtex/issues/169/replace-pkg_resources-with # is fixed session.install("setuptools", silent=False) - session.install("-e", ".[all]", silent=False) + session.install("-e", ".[all,dev]", silent=False) session.run("python", "run-tests.py", "--scripts") @@ -168,68 +134,23 @@ def set_dev(session): # https://bitbucket.org/pybtex-devs/pybtex/issues/169/replace-pkg_resources-with # is fixed session.run(python, "-m", "pip", "install", "setuptools", external=True) - if sys.platform == "linux": - if sys.version_info > (3, 12): - session.run( - python, - "-m", - "pip", - "install", - "-e", - ".[all,dev,jax]", - external=True, - ) - else: - session.run( - python, - "-m", - "pip", - "install", - "-e", - ".[all,dev,jax,odes]", - external=True, - ) - else: - if sys.version_info < (3, 9): - session.run( - python, - "-m", - "pip", - "install", - "-e", - ".[all,dev]", - external=True, - ) - else: - session.run( - python, - "-m", - "pip", - "install", - "-e", - ".[all,dev,jax]", - external=True, - ) + session.run( + python, + "-m", + "pip", + "install", + "-e", + ".[all,dev,jax]", + external=True, + ) @nox.session(name="tests") def run_tests(session): """Run the unit tests and integration tests sequentially.""" set_environment_variables(PYBAMM_ENV, session=session) - # Temporary fix for Python 3.12 CI. TODO: remove after - # https://bitbucket.org/pybtex-devs/pybtex/issues/169/replace-pkg_resources-with - # is fixed session.install("setuptools", silent=False) - if sys.platform != "win32": - if sys.version_info > (3, 12): - session.install("-e", ".[all,jax]", silent=False) - else: - session.install("-e", ".[all,jax,odes]", silent=False) - else: - if sys.version_info < (3, 9): - session.install("-e", ".[all]", silent=False) - else: - session.install("-e", ".[all,jax]", silent=False) + session.install("-e", ".[all,dev,jax]", silent=False) session.run("python", "run-tests.py", "--all") @@ -237,11 +158,10 @@ def run_tests(session): def build_docs(session): """Build the documentation and load it in a browser tab, rebuilding on changes.""" envbindir = session.bin - session.install("-e", ".[all,docs]", silent=False) - # Temporary fix for Python 3.12 CI. TODO: remove after - # https://bitbucket.org/pybtex-devs/pybtex/issues/169/replace-pkg_resources-with - # is fixed + # TODO: Temporary fix for Python 3.12 CI. + # See: https://bitbucket.org/pybtex-devs/pybtex/issues/169/ session.install("setuptools", silent=False) + session.install("-e", ".[all,docs]", silent=False) session.chdir("docs") # Local development if session.interactive: @@ -255,11 +175,11 @@ def build_docs(session): f"{envbindir}/../tmp/html", ) # Runs in CI only, treating warnings as errors + # Run in single-threaded mode, see + # https://github.com/pydata/pydata-sphinx-theme/issues/1643 else: session.run( "sphinx-build", - "-j", - "auto", "-b", "html", "-W", diff --git a/pybamm/CITATIONS.bib b/pybamm/CITATIONS.bib index 21740584b5..33ac6055d3 100644 --- a/pybamm/CITATIONS.bib +++ b/pybamm/CITATIONS.bib @@ -252,19 +252,6 @@ @article{Lain2019 doi = {10.3390/batteries5040064}, } -@article{Malengier2018, - year = {2018}, - month = {feb}, - publisher = {The Open Journal}, - volume = {3}, - number = {22}, - pages = {165}, - author = {Malengier, Benny and Ki{\v{s}}on, Pavol and Tocknell, James and Abert, Claas and Bruckner, Florian and Bisotti, Marc-Antonio}, - title = {{ODES: a high level interface to ODE and DAE solvers}}, - journal = {The Journal of Open Source Software}, - doi = {10.21105/joss.00165}, -} - @article{Marquis2019, title = {{An asymptotic derivation of a single particle model with electrolyte}}, author = {Marquis, Scott G. and Sulzer, Valentin and Timms, Robert and Please, Colin P. and Chapman, S. Jon}, @@ -431,6 +418,17 @@ @article{Richardson2020 doi = {10.1016/j.electacta.2020.135862}, } +@article{Richardson2021, + title = {Heat Generation and a Conservation Law for Chemical Energy in Li-ion Batteries}, + author = {Richardson, Giles and Korotkin, Ivan}, + journal = {Electrochimica Acta}, + volume = {392}, + pages = {138909}, + year = {2021}, + publisher = {Elsevier}, + doi = {10.1016/j.electacta.2021.138909}, +} + @article{Sripad2020, title={Kinetics of lithium electrodeposition and stripping}, author={Sripad, Shashank and Korff, Daniel and DeCaluwe, Steven C and Viswanathan, Venkatasubramanian}, @@ -704,3 +702,95 @@ @article{landesfeind2019temperature year={2019}, publisher={The Electrochemical Society} } +@article{akanni1987effective, + title={Effective transport coefficients in heterogeneous media}, + author={Akanni, KA and Evans, JW and Abramson, IS}, + journal={Chemical Engineering Science}, + volume={42}, + number={8}, + pages={1945--1954}, + year={1987}, + publisher={Elsevier} +} +@article{petersen1958diffusion, + title={Diffusion in a pore of varying cross section}, + author={Petersen, EE}, + journal={AIChE Journal}, + volume={4}, + number={3}, + pages={343--345}, + year={1958}, + publisher={Wiley Online Library} +} +@article{bruggeman1935berechnung, + title={Berechnung verschiedener physikalischer Konstanten von heterogenen Substanzen. I. Dielektrizit{\"a}tskonstanten und Leitf{\"a}higkeiten der Mischk{\"o}rper aus isotropen Substanzen}, + author={Bruggeman, Von DAG}, + journal={Annalen der physik}, + volume={416}, + number={7}, + pages={636--664}, + year={1935}, + publisher={Wiley Online Library} +} +@article{weissberg1963effective, + title={Effective diffusion coefficient in porous media}, + author={Weissberg, Harold L}, + journal={Journal of Applied Physics}, + volume={34}, + number={9}, + pages={2636--2639}, + year={1963}, + publisher={American Institute of Physics} +} +@article{tomadakis1993transport, + title={Transport properties of random arrays of freely overlapping cylinders with various orientation distributions}, + author={Tomadakis, Manolis M and Sotirchos, Stratis V}, + journal={The Journal of chemical physics}, + volume={98}, + number={1}, + pages={616--626}, + year={1993}, + publisher={American Institute of Physics} +} +@article{beeckman1990mathematical, + title={Mathematical description of heterogeneous materials}, + author={Beeckman, JW}, + journal={Chemical engineering science}, + volume={45}, + number={8}, + pages={2603--2610}, + year={1990}, + publisher={Elsevier} +} +@article{mackie1955diffusion, + title={The diffusion of electrolytes in a cation-exchange resin membrane I. Theoretical}, + author={Mackie, JS and Meares, P}, + journal={Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences}, + volume={232}, + number={1191}, + pages={498--509}, + year={1955}, + publisher={The Royal Society London} +} +@article{shen2007critical, + title={Critical review of the impact of tortuosity on diffusion}, + author={Shen, Lihua and Chen, Zhangxin}, + journal={Chemical Engineering Science}, + volume={62}, + number={14}, + pages={3748--3755}, + year={2007}, + publisher={Elsevier} +} +@article{Wycisk2022, + title = {Modified Plett-model for modeling voltage hysteresis in lithium-ion cells}, + journal = {Journal of Energy Storage}, + volume = {52}, + pages = {105016}, + year = {2022}, + issn = {2352-152X}, + doi = {https://doi.org/10.1016/j.est.2022.105016}, + url = {https://www.sciencedirect.com/science/article/pii/S2352152X22010192}, + author = {Dominik Wycisk and Marc Oldenburger and Marc Gerry Stoye and Toni Mrkonjic and Arnulf Latz}, + keywords = {Lithium-ion battery, Voltage hysteresis, Plett-model, Silicon–graphite anode}, +} diff --git a/pybamm/__init__.py b/pybamm/__init__.py index f2da97b5fa..b3b7fafd3f 100644 --- a/pybamm/__init__.py +++ b/pybamm/__init__.py @@ -1,45 +1,12 @@ -# -# Root of the pybamm module. -# Provides access to all shared functionality (models, solvers, etc.). -# -# The code in this file is adapted from Pints -# (see https://github.com/pints-team/pints) -# import sys -import os - -# -# Version info -# from pybamm.version import __version__ -# -# Constants -# -# Float format: a float can be converted to a 17 digit decimal and back without -# loss of information -FLOAT_FORMAT = "{: .17e}" -# Absolute path to the PyBaMM repo -script_path = os.path.abspath(__file__) - -from .util import root_dir - -ABSOLUTE_PATH = root_dir() -PARAMETER_PATH = [ - root_dir(), - os.getcwd(), - os.path.join(root_dir(), "pybamm", "input", "parameters"), -] - - -# # Utility classes and methods -# +from .util import root_dir from .util import Timer, TimerTime, FuzzyDict from .util import ( root_dir, - rmse, load, is_constant_and_can_evaluate, ) @@ -47,16 +14,15 @@ get_parameters_filepath, have_jax, install_jax, - have_optional_dependency, + import_optional_dependency, is_jax_compatible, get_git_commit_info, ) from .logger import logger, set_logging_level, get_new_logger from .settings import settings from .citations import Citations, citations, print_citations -# + # Classes for the Expression Tree -# from .expression_tree.symbol import * from .expression_tree.binary_operators import * from .expression_tree.concatenations import * @@ -94,9 +60,7 @@ from .expression_tree.operations.convert_to_casadi import CasadiConverter from .expression_tree.operations.unpack_symbols import SymbolUnpacker -# # Model classes -# from .models.base_model import BaseModel from .models.event import Event from .models.event import EventType @@ -110,9 +74,7 @@ from .models.full_battery_models import lithium_ion from .models.full_battery_models import equivalent_circuit -# # Submodel classes -# from .models.submodels.base_submodel import BaseSubModel from .models.submodels import ( @@ -138,18 +100,14 @@ from .models.submodels.interface import interface_utilisation from .models.submodels.interface import open_circuit_potential -# # Geometry -# from .geometry.geometry import Geometry from .geometry.battery_geometry import battery_geometry from .expression_tree.independent_variable import KNOWN_COORD_SYS from .geometry import standard_spatial_vars -# # Parameter classes and methods -# from .parameters.parameter_values import ParameterValues from .parameters import constants from .parameters.geometric_parameters import geometric_parameters, GeometricParameters @@ -163,11 +121,8 @@ from .parameters.ecm_parameters import EcmParameters from .parameters.size_distribution_parameters import * from .parameters.parameter_sets import parameter_sets -from .parameters_cli import add_parameter, remove_parameter, edit_parameter -# # Mesh and Discretisation classes -# from .discretisations.discretisation import Discretisation from .discretisations.discretisation import has_bc_of_form from .meshes.meshes import Mesh, SubMesh, MeshGenerator @@ -188,23 +143,17 @@ UserSupplied2DSubMesh, ) -# # Serialisation -# from .models.base_model import load_model -# # Spatial Methods -# from .spatial_methods.spatial_method import SpatialMethod from .spatial_methods.zero_dimensional_method import ZeroDimensionalSpatialMethod from .spatial_methods.finite_volume import FiniteVolume from .spatial_methods.spectral_volume import SpectralVolume from .spatial_methods.scikit_finite_element import ScikitFiniteElement -# # Solver classes -# from .solvers.solution import Solution, EmptySolution, make_cycle_solution from .solvers.processed_variable import ProcessedVariable from .solvers.processed_variable_computed import ProcessedVariableComputed @@ -213,49 +162,64 @@ from .solvers.algebraic_solver import AlgebraicSolver from .solvers.casadi_solver import CasadiSolver from .solvers.casadi_algebraic_solver import CasadiAlgebraicSolver -from .solvers.scikits_dae_solver import ScikitsDaeSolver -from .solvers.scikits_ode_solver import ScikitsOdeSolver, have_scikits_odes from .solvers.scipy_solver import ScipySolver from .solvers.jax_solver import JaxSolver from .solvers.jax_bdf_solver import jax_bdf_integrate +from .solvers.idaklu_jax import IDAKLUJax from .solvers.idaklu_solver import IDAKLUSolver, have_idaklu -# # Experiments -# from .experiment.experiment import Experiment from . import experiment from .experiment import step - -# # Plotting -# from .plotting.quick_plot import QuickPlot, close_plots, QuickPlotAxes from .plotting.plot import plot from .plotting.plot2D import plot2D from .plotting.plot_voltage_components import plot_voltage_components +from .plotting.plot_thermal_components import plot_thermal_components from .plotting.plot_summary_variables import plot_summary_variables from .plotting.dynamic_plot import dynamic_plot -# # Simulation -# from .simulation import Simulation, load_sim, is_notebook -# # Batch Study -# from .batch_study import BatchStudy -# # Callbacks -# from . import callbacks -# +# Pybamm Data manager using pooch +from .pybamm_data import DataLoader + # Remove any imported modules, so we don't expose them as part of pybamm -# del sys + +__all__ = [ + "batch_study", + "callbacks", + "citations", + "discretisations", + "doc_utils", + "experiment", + "expression_tree", + "geometry", + "input", + "logger", + "meshes", + "models", + "parameters", + "plotting", + "settings", + "simulation", + "solvers", + "spatial_methods", + "type_definitions", + "util", + "version", + "pybamm_data", +] diff --git a/pybamm/batch_study.py b/pybamm/batch_study.py index 4f1c37e71c..e854c94e00 100644 --- a/pybamm/batch_study.py +++ b/pybamm/batch_study.py @@ -102,7 +102,6 @@ def solve( self, t_eval=None, solver=None, - check_model=True, save_at_cycles=None, calc_esoh=True, starting_solution=None, @@ -158,7 +157,6 @@ def solve( sol = sim.solve( t_eval, solver, - check_model, save_at_cycles, calc_esoh, starting_solution, @@ -196,7 +194,8 @@ def create_gif(self, number_of_images=80, duration=0.1, output_filename="plot.gi Name of the generated GIF file. """ - + if not hasattr(self, "sims"): + raise ValueError("The simulations have not been solved yet.") if self.quick_plot is None: self.quick_plot = pybamm.QuickPlot(self.sims) diff --git a/pybamm/callbacks.py b/pybamm/callbacks.py index 32607bb716..4e8c67c8be 100644 --- a/pybamm/callbacks.py +++ b/pybamm/callbacks.py @@ -1,10 +1,3 @@ -# -# Base class for callbacks and some useful callbacks for pybamm -# Callbacks are used to perform actions (e.g. logging, saving) -# at certain points in the simulation -# Inspired by Keras callbacks -# https://github.com/keras-team/keras/blob/master/keras/callbacks/callback.py -# import pybamm import numpy as np import inspect @@ -43,37 +36,37 @@ def on_experiment_start(self, logs): """ Called at the start of an experiment simulation. """ - pass + pass # pragma: no cover def on_cycle_start(self, logs): """ Called at the start of each cycle in an experiment simulation. """ - pass + pass # pragma: no cover def on_step_start(self, logs): """ Called at the start of each step in an experiment simulation. """ - pass + pass # pragma: no cover def on_step_end(self, logs): """ Called at the end of each step in an experiment simulation. """ - pass + pass # pragma: no cover def on_cycle_end(self, logs): """ Called at the end of each cycle in an experiment simulation. """ - pass + pass # pragma: no cover def on_experiment_end(self, logs): """ Called at the end of an experiment simulation. """ - pass + pass # pragma: no cover def on_experiment_error(self, logs): """ @@ -82,13 +75,19 @@ def on_experiment_error(self, logs): For example, this could be used to send an error alert with a bug report when running batch simulations in the cloud. """ - pass + pass # pragma: no cover - def on_experiment_infeasible(self, logs): + def on_experiment_infeasible_time(self, logs): """ - Called when an experiment simulation is infeasible. + Called when an experiment simulation is infeasible due to reaching maximum time. """ - pass + pass # pragma: no cover + + def on_experiment_infeasible_event(self, logs): + """ + Called when an experiment simulation is infeasible due to an event. + """ + pass # pragma: no cover ######################################################################################## @@ -99,8 +98,7 @@ class CallbackList(Callback): This is done without having to redefine the method each time by using the `callback_loop_decorator` decorator, which is applied to every method that starts - with `on_`, using the `inspect` module. See - https://stackoverflow.com/questions/1367514/how-to-decorate-a-method-inside-a-class. + with `on_`, using the `inspect` module. If better control over how the callbacks are called is required, it might be better to be more explicit with the for loop. @@ -183,7 +181,18 @@ def on_step_start(self, logs): ) def on_step_end(self, logs): - pass + time_stop = logs["stopping conditions"]["time"] + if time_stop is not None: + time_now = logs["experiment time"] + if time_now < time_stop: + self.logger.notice( + f"Time is now {time_now:.3f} s, will stop at {time_stop:.3f} s." + ) + else: + self.logger.notice( + f"Stopping experiment since time ({time_now:.3f} s) " + f"has reached stopping time ({time_stop:.3f} s)." + ) def on_cycle_end(self, logs): cap_stop = logs["stopping conditions"]["capacity"] @@ -223,7 +232,19 @@ def on_experiment_error(self, logs): error = logs["error"] pybamm.logger.error(f"Simulation error: {error}") - def on_experiment_infeasible(self, logs): + def on_experiment_infeasible_time(self, logs): + duration = logs["step duration"] + cycle_num = logs["cycle number"][0] + step_num = logs["step number"][0] + operating_conditions = logs["step operating conditions"] + self.logger.warning( + f"\n\n\tExperiment is infeasible: default duration ({duration} seconds) " + f"was reached during '{operating_conditions}'. The returned solution only " + f"contains up to step {step_num} of cycle {cycle_num}. " + "Please specify a duration in the step instructions." + ) + + def on_experiment_infeasible_event(self, logs): termination = logs["termination"] cycle_num = logs["cycle number"][0] step_num = logs["step number"][0] diff --git a/pybamm/citations.py b/pybamm/citations.py index ca260c5cfd..b76e9b5c83 100644 --- a/pybamm/citations.py +++ b/pybamm/citations.py @@ -7,11 +7,10 @@ import os import warnings from sys import _getframe -from pybamm.util import have_optional_dependency +from pybamm.util import import_optional_dependency class Citations: - """Entry point to citations management. This object may be used to record BibTeX citation information and then register that a particular citation is relevant for a particular simulation. @@ -39,14 +38,8 @@ def __init__(self): # Dict mapping citation tags for use when registering citations self._citation_tags = dict() - # store citation error - self._citation_err_msg = None - - try: - self.read_citations() - self._reset() - except Exception as e: # pragma: no cover - self._citation_err_msg = e + self.read_citations() + self._reset() def _reset(self): """Reset citations to default only (only for testing purposes)""" @@ -60,12 +53,12 @@ def _reset(self): self.register("Sulzer2021") self.register("Harris2020") + @staticmethod def _caller_name(): """ Returns the qualified name of classes that call :meth:`register` internally. Gets cached in order to reduce the number of calls. """ - # Attributed to https://stackoverflow.com/a/53490973 caller_name = _getframe().f_back.f_back.f_locals["self"].__class__.__qualname__ return caller_name @@ -73,29 +66,41 @@ def read_citations(self): """Reads the citations in `pybamm.CITATIONS.bib`. Other works can be cited by passing a BibTeX citation to :meth:`register`. """ - parse_file = have_optional_dependency("pybtex.database", "parse_file") - citations_file = os.path.join(pybamm.root_dir(), "pybamm", "CITATIONS.bib") - bib_data = parse_file(citations_file, bib_format="bibtex") - for key, entry in bib_data.entries.items(): - self._add_citation(key, entry) + try: + parse_file = import_optional_dependency("pybtex.database", "parse_file") + citations_file = os.path.join(pybamm.root_dir(), "pybamm", "CITATIONS.bib") + bib_data = parse_file(citations_file, bib_format="bibtex") + for key, entry in bib_data.entries.items(): + self._add_citation(key, entry) + except ModuleNotFoundError: # pragma: no cover + pybamm.logger.warning( + "Citations could not be read because the 'pybtex' library is not installed. " + "Install 'pybamm[cite]' to enable citation reading." + ) def _add_citation(self, key, entry): """Adds `entry` to `self._all_citations` under `key`, warning the user if a previous entry is overwritten """ - Entry = have_optional_dependency("pybtex.database", "Entry") - # Check input types are correct - if not isinstance(key, str) or not isinstance(entry, Entry): - raise TypeError() - - # Warn if overwriting a previous citation - new_citation = entry.to_string("bibtex") - if key in self._all_citations and new_citation != self._all_citations[key]: - warnings.warn(f"Replacing citation for {key}") - - # Add to database - self._all_citations[key] = new_citation + try: + Entry = import_optional_dependency("pybtex.database", "Entry") + # Check input types are correct + if not isinstance(key, str) or not isinstance(entry, Entry): + raise TypeError() + + # Warn if overwriting a previous citation + new_citation = entry.to_string("bibtex") + if key in self._all_citations and new_citation != self._all_citations[key]: + warnings.warn(f"Replacing citation for {key}", stacklevel=2) + + # Add to database + self._all_citations[key] = new_citation + except ModuleNotFoundError: # pragma: no cover + pybamm.logger.warning( + f"Could not add citation for '{key}' because the 'pybtex' library is not installed. " + "Install 'pybamm[cite]' to enable adding citations." + ) def _add_citation_tag(self, key, entry): """Adds a tag for a citation key in the dict, which represents the name of the @@ -122,23 +127,22 @@ def register(self, key): - The citation key for an entry in `pybamm/CITATIONS.bib` or - A BibTeX formatted citation """ - if self._citation_err_msg is None: - # Check if citation is a known key - if key in self._all_citations: - self._papers_to_cite.add(key) - # Add citation tags for the key for verbose output, but - # don't if they already exist in _citation_tags dict - if key not in self._citation_tags: - try: - caller = Citations._caller_name() - self._add_citation_tag(key, entry=caller) - # Don't add citation tags if the citation is registered manually - except KeyError: # pragma: no cover - pass - else: - # If citation is unknown, parse it later with pybtex - self._unknown_citations.add(key) - return + # Check if citation is a known key + if key in self._all_citations: + self._papers_to_cite.add(key) + # Add citation tags for the key for verbose output, but + # don't if they already exist in _citation_tags dict + if key not in self._citation_tags: + try: + caller = Citations._caller_name() + self._add_citation_tag(key, entry=caller) + # Don't add citation tags if the citation is registered manually + except KeyError: # pragma: no cover + pass + else: + # If citation is unknown, parse it later with pybtex + self._unknown_citations.add(key) + return def _parse_citation(self, key): """ @@ -150,24 +154,32 @@ def _parse_citation(self, key): key: str A BibTeX formatted citation """ - PybtexError = have_optional_dependency("pybtex.scanner", "PybtexError") - parse_string = have_optional_dependency("pybtex.database", "parse_string") try: - # Parse string as a bibtex citation, and check that a citation was found - bib_data = parse_string(key, bib_format="bibtex") - if not bib_data.entries: - raise PybtexError("no entries found") - - # Add and register all citations - for key, entry in bib_data.entries.items(): - # Add to _all_citations dictionary - self._add_citation(key, entry) - # Add to _papers_to_cite set - self._papers_to_cite.add(key) - return - except PybtexError: - # Unable to parse / unknown key - raise KeyError(f"Not a bibtex citation or known citation: {key}") + PybtexError = import_optional_dependency("pybtex.scanner", "PybtexError") + parse_string = import_optional_dependency("pybtex.database", "parse_string") + try: + # Parse string as a bibtex citation, and check that a citation was found + bib_data = parse_string(key, bib_format="bibtex") + if not bib_data.entries: + raise PybtexError("no entries found") + + # Add and register all citations + for key, entry in bib_data.entries.items(): + # Add to _all_citations dictionary + self._add_citation(key, entry) + # Add to _papers_to_cite set + self._papers_to_cite.add(key) + return + except PybtexError as error: + # Unable to parse / unknown key + raise KeyError( + f"Not a bibtex citation or known citation: {key}" + ) from error + except ModuleNotFoundError: # pragma: no cover + pybamm.logger.warning( + f"Could not parse citation for '{key}' because the 'pybtex' library is not installed. " + "Install 'pybamm[cite]' to enable citation parsing." + ) def _tag_citations(self): """Prints the citation tags for the citations that have been registered @@ -218,60 +230,57 @@ def print(self, filename=None, output_format="text", verbose=False): """ # Parse citations that were not known keys at registration, but do not # fail if they cannot be parsed - pybtex = have_optional_dependency("pybtex") try: - for key in self._unknown_citations: - self._parse_citation(key) - except KeyError: # pragma: no cover - warnings.warn( - message=f'\nCitation with key "{key}" is invalid. Please try again\n', - category=UserWarning, - ) - # delete the invalid citation from the set - self._unknown_citations.remove(key) + pybtex = import_optional_dependency("pybtex") + try: + for key in self._unknown_citations: + self._parse_citation(key) + except KeyError: # pragma: no cover + warnings.warn( + message=f'\nCitation with key "{key}" is invalid. Please try again\n', + category=UserWarning, + stacklevel=2, + ) + # delete the invalid citation from the set + self._unknown_citations.remove(key) - if output_format == "text": - citations = pybtex.format_from_strings( - self._cited, style="plain", output_backend="plaintext" - ) - elif output_format == "bibtex": - citations = "\n".join(self._cited) - else: - raise pybamm.OptionError( - f"Output format {output_format} not recognised." - "It should be 'text' or 'bibtex'." - ) + if output_format == "text": + citations = pybtex.format_from_strings( + self._cited, style="plain", output_backend="plaintext" + ) + elif output_format == "bibtex": + citations = "\n".join(self._cited) + else: + raise pybamm.OptionError( + f"Output format {output_format} not recognised." + "It should be 'text' or 'bibtex'." + ) - if filename is None: - print(citations) - if verbose: - self._tag_citations() # pragma: no cover - else: - with open(filename, "w") as f: - f.write(citations) + if filename is None: + print(citations) + if verbose: + self._tag_citations() # pragma: no cover + else: + with open(filename, "w") as f: + f.write(citations) + except ModuleNotFoundError: # pragma: no cover + pybamm.logger.warning( + "Could not print citations because the 'pybtex' library is not installed. " + "Please, install 'pybamm[cite]' to print citations." + ) def print_citations(filename=None, output_format="text", verbose=False): """See :meth:`Citations.print`""" - if citations._citation_err_msg is not None: - raise ImportError( - f"Citations could not be registered. If you are on Google Colab - " - "pybtex does not work with Google Colab due to a known bug - " - "https://bitbucket.org/pybtex-devs/pybtex/issues/148/. " - "Please manually cite all the references." - "\nError encountered -\n" - f"{citations._citation_err_msg}" - ) - else: - if verbose: # pragma: no cover - if filename is not None: # pragma: no cover - raise Exception( - "Verbose output is available only for the terminal and not for printing to files", - ) - else: - citations.print(filename, output_format, verbose=True) + if verbose: # pragma: no cover + if filename is not None: # pragma: no cover + raise Exception( + "Verbose output is available only for the terminal and not for printing to files", + ) else: - pybamm.citations.print(filename, output_format) + citations.print(filename, output_format, verbose=True) + else: + pybamm.citations.print(filename, output_format) citations = Citations() diff --git a/pybamm/discretisations/__init__.py b/pybamm/discretisations/__init__.py index e69de29bb2..5dc6969c2d 100644 --- a/pybamm/discretisations/__init__.py +++ b/pybamm/discretisations/__init__.py @@ -0,0 +1 @@ +__all__ = ['discretisation'] diff --git a/pybamm/discretisations/discretisation.py b/pybamm/discretisations/discretisation.py index 7be3b2bc53..24625cf8fc 100644 --- a/pybamm/discretisations/discretisation.py +++ b/pybamm/discretisations/discretisation.py @@ -26,14 +26,32 @@ class Discretisation: Parameters ---------- mesh : pybamm.Mesh - contains all submeshes to be used on each domain + contains all submeshes to be used on each domain spatial_methods : dict - a dictionary of the spatial methods to be used on each - domain. The keys correspond to the model domains and the - values to the spatial method. + a dictionary of the spatial methods to be used on each + domain. The keys correspond to the model domains and the + values to the spatial method. + check_model : bool, optional + If True, model checks are performed after discretisation. For large + systems these checks can be slow, so can be skipped by setting this + option to False. When developing, testing or debugging it is recommended + to leave this option as True as it may help to identify any errors. + Default is True. + remove_independent_variables_from_rhs : bool, optional + If True, model checks to see whether any variables from the RHS are used + in any other equation. If a variable meets all of the following criteria + (not used anywhere in the model, len(rhs)>1), then the variable + is moved to be explicitly integrated when called by the solution object. + Default is False. """ - def __init__(self, mesh=None, spatial_methods=None): + def __init__( + self, + mesh=None, + spatial_methods=None, + check_model=True, + remove_independent_variables_from_rhs=False, + ): self._mesh = mesh if mesh is None: self._spatial_methods = {} @@ -60,6 +78,10 @@ def __init__(self, mesh=None, spatial_methods=None): self._bcs = {} self.y_slices = {} self._discretised_symbols = {} + self._check_model_flag = check_model + self._remove_independent_variables_from_rhs_flag = ( + remove_independent_variables_from_rhs + ) @property def mesh(self): @@ -90,13 +112,7 @@ def bcs(self, value): # reset discretised_symbols self._discretised_symbols = {} - def process_model( - self, - model, - inplace=True, - check_model=True, - remove_independent_variables_from_rhs=True, - ): + def process_model(self, model, inplace=True): """ Discretise a model. Currently inplace, could be changed to return a new model. @@ -108,18 +124,6 @@ def process_model( inplace : bool, optional If True, discretise the model in place. Otherwise, return a new discretised model. Default is True. - check_model : bool, optional - If True, model checks are performed after discretisation. For large - systems these checks can be slow, so can be skipped by setting this - option to False. When developing, testing or debugging it is recommended - to leave this option as True as it may help to identify any errors. - Default is True. - remove_independent_variables_from_rhs : bool, optional - If True, model checks to see whether any variables from the RHS are used - in any other equation. If a variable meets all of the following criteria - (not used anywhere in the model, len(rhs)>1), then the variable - is moved to be explicitly integrated when called by the solution object. - Default is True. Returns ------- @@ -158,7 +162,7 @@ def process_model( # set variables (we require the full variable not just id) # Search Equations for Independence - if remove_independent_variables_from_rhs: + if self._remove_independent_variables_from_rhs_flag: model = self.remove_independent_variables_from_rhs(model) variables = list(model.rhs.keys()) + list(model.algebraic.keys()) # Find those RHS's that are constant @@ -240,7 +244,7 @@ def process_model( model_disc._geometry = getattr(self.mesh, "_geometry", None) # Check that resulting model makes sense - if check_model: + if self._check_model_flag: pybamm.logger.verbose(f"Performing model checks for {model.name}") self.check_model(model_disc) @@ -280,7 +284,7 @@ def set_variable_slices(self, variables): sec_points = spatial_method._get_auxiliary_domain_repeats( variable.domains ) - for i in range(sec_points): + for _ in range(sec_points): for child, mesh in meshes.items(): for domain_mesh in mesh: end += domain_mesh.npts_for_broadcast_to_nodes @@ -458,9 +462,7 @@ def process_boundary_conditions(self, model): if bcs["left"][0].value != 0 or bcs["left"][1] != "Neumann": raise pybamm.ModelError( "Boundary condition at r = 0 must be a homogeneous " - "Neumann condition for {} coordinates".format( - self.mesh[subdomain].coord_sys - ) + f"Neumann condition for {self.mesh[subdomain].coord_sys} coordinates" ) # Handle any boundary conditions applied on the tabs @@ -756,7 +758,7 @@ def _process_symbol(self, symbol): disc_right = self.process_symbol(right) if symbol.domain == []: return pybamm.simplify_if_constant( - symbol._binary_new_copy(disc_left, disc_right) + symbol.create_copy(new_children=[disc_left, disc_right]) ) else: return spatial_method.process_binary_operators( @@ -868,11 +870,11 @@ def _process_symbol(self, symbol): # After discretisation, we can make the symbol constant return disc_child else: - return symbol._unary_new_copy(disc_child) + return symbol.create_copy(new_children=[disc_child]) elif isinstance(symbol, pybamm.Function): disc_children = [self.process_symbol(child) for child in symbol.children] - return symbol._function_new_copy(disc_children) + return symbol.create_copy(disc_children) elif isinstance(symbol, pybamm.VariableDot): # Add symbol's reference and multiply by the symbol's scale @@ -888,14 +890,14 @@ def _process_symbol(self, symbol): # model.check_well_posedness, but won't be if debug_mode is False try: y_slices = self.y_slices[symbol] - except KeyError: + except KeyError as error: raise pybamm.ModelError( - """ - No key set for variable '{}'. Make sure it is included in either + f""" + No key set for variable '{symbol.name}'. Make sure it is included in either model.rhs or model.algebraic in an unmodified form (e.g. not Broadcasted) - """.format(symbol.name) - ) + """ + ) from error # Add symbol's reference and multiply by the symbol's scale # so that the state vector is of order 1 return symbol.reference + symbol.scale * pybamm.StateVector( @@ -1033,20 +1035,14 @@ def check_initial_conditions(self, model): raise pybamm.ModelError( "rhs and initial conditions must have the same shape after " "discretisation but rhs.shape = " - "{} and initial_conditions.shape = {} for variable '{}'.".format( - model.rhs[var].shape, model.initial_conditions[var].shape, var - ) + f"{model.rhs[var].shape} and initial_conditions.shape = {model.initial_conditions[var].shape} for variable '{var}'." ) for var in model.algebraic.keys(): if model.algebraic[var].shape != model.initial_conditions[var].shape: raise pybamm.ModelError( "algebraic and initial conditions must have the same shape after " "discretisation but algebraic.shape = " - "{} and initial_conditions.shape = {} for variable '{}'.".format( - model.algebraic[var].shape, - model.initial_conditions[var].shape, - var, - ) + f"{model.algebraic[var].shape} and initial_conditions.shape = {model.initial_conditions[var].shape} for variable '{var}'." ) def check_variables(self, model): @@ -1080,9 +1076,7 @@ def check_variables(self, model): raise pybamm.ModelError( "variable and its eqn must have the same shape after " "discretisation but variable.shape = " - "{} and rhs.shape = {} for variable '{}'. ".format( - var.shape, model.rhs[rhs_var].shape, var - ) + f"{var.shape} and rhs.shape = {model.rhs[rhs_var].shape} for variable '{var}'. " ) def is_variable_independent(self, var, all_vars_in_eqns): @@ -1113,6 +1107,7 @@ def remove_independent_variables_from_rhs(self, model): # only check children of variables, this will skip the variable itself # and catch any other cases + [child for var in model.variables.values() for child in var.children] + + [event.expression for event in model.events] ) all_vars_in_eqns = unpacker.unpack_list_of_symbols(eqns_to_check) all_vars_in_eqns = [var.name for var in all_vars_in_eqns] @@ -1132,6 +1127,7 @@ def remove_independent_variables_from_rhs(self, model): # in variables twice under different names for key in model.variables: if model.variables[key] == var: + print("here") model.variables[key] = model.variables[var.name] del model.rhs[var] del model.initial_conditions[var] diff --git a/pybamm/experiment/__init__.py b/pybamm/experiment/__init__.py index e69de29bb2..611abede0c 100644 --- a/pybamm/experiment/__init__.py +++ b/pybamm/experiment/__init__.py @@ -0,0 +1 @@ +__all__ = ['experiment', 'step'] diff --git a/pybamm/experiment/experiment.py b/pybamm/experiment/experiment.py index b04281d78d..39c49780e4 100644 --- a/pybamm/experiment/experiment.py +++ b/pybamm/experiment/experiment.py @@ -1,10 +1,6 @@ -# -# Experiment class -# - from __future__ import annotations import pybamm -from .step._steps_util import ( +from .step.base_step import ( _convert_time_to_seconds, _convert_temperature_to_kelvin, ) @@ -14,8 +10,8 @@ class Experiment: """ Base class for experimental conditions under which to run the model. In general, a list of operating conditions should be passed in. Each operating condition should - be either a `pybamm.step._Step` class, created using one of the methods - `pybamm.step.current`, `pybamm.step.c_rate`, `pybamm.step.voltage` + be either a `pybamm.step.BaseStep` class, which can be created using one of the + methods `pybamm.step.current`, `pybamm.step.c_rate`, `pybamm.step.voltage` , `pybamm.step.power`, `pybamm.step.resistance`, or `pybamm.step.string`, or a string, in which case the string is passed to `pybamm.step.string`. @@ -24,7 +20,7 @@ class Experiment: ---------- operating_conditions : list[str] List of strings representing the operating conditions. - period : string, optional + period : str, optional Period (1/frequency) at which to record outputs. Default is 1 minute. Can be overwritten by individual operating conditions. temperature: float, optional @@ -43,24 +39,11 @@ class Experiment: def __init__( self, - operating_conditions: list[str], + operating_conditions: list[str | tuple[str]], period: str = "1 minute", temperature: float | None = None, termination: list[str] | None = None, - drive_cycles=None, - cccv_handling=None, ): - if cccv_handling is not None: - raise ValueError( - "cccv_handling has been deprecated, use " - "`pybamm.step.cccv_ode(current, voltage)` instead to produce the " - "same behavior as the old `cccv_handling='ode'`" - ) - if drive_cycles is not None: - raise ValueError( - "drive_cycles should now be passed as an experiment step object, e.g. " - "`pybamm.step.current(drive_cycle)`" - ) # Save arguments for copying self.args = ( operating_conditions, @@ -69,58 +52,37 @@ def __init__( termination, ) - operating_conditions_cycles = [] + cycles = [] for cycle in operating_conditions: - # Check types and convert to list if not isinstance(cycle, tuple): cycle = (cycle,) - operating_conditions_cycles.append(cycle) + cycles.append(cycle) + self.cycles = cycles + self.cycle_lengths = [len(cycle) for cycle in cycles] - self.operating_conditions_cycles = operating_conditions_cycles - self.cycle_lengths = [len(cycle) for cycle in operating_conditions_cycles] + steps_unprocessed = [cond for cycle in cycles for cond in cycle] - self.operating_conditions_steps_unprocessed = self._set_next_start_time( - [cond for cycle in operating_conditions_cycles for cond in cycle] - ) - - # Convert strings to pybamm.step._Step objects - # We only do this once per unique step, do avoid unnecessary conversions + # Convert strings to pybamm.step.BaseStep objects + # We only do this once per unique step, to avoid unnecessary conversions # Assign experiment period and temperature if not specified in step self.period = _convert_time_to_seconds(period) self.temperature = _convert_temperature_to_kelvin(temperature) - processed_steps = {} - for step in self.operating_conditions_steps_unprocessed: - if repr(step) in processed_steps: - continue - elif isinstance(step, str): - processed_step = pybamm.step.string(step) - elif isinstance(step, pybamm.step._Step): - processed_step = step - - if processed_step.period is None: - processed_step.period = self.period - if processed_step.temperature is None: - processed_step.temperature = self.temperature - - processed_steps[repr(step)] = processed_step + processed_steps = self.process_steps( + steps_unprocessed, self.period, self.temperature + ) - self.operating_conditions_steps = [ - processed_steps[repr(step)] - for step in self.operating_conditions_steps_unprocessed - ] + self.steps = [processed_steps[repr(step)] for step in steps_unprocessed] + self.steps = self._set_next_start_time(self.steps) # Save the processed unique steps and the processed operating conditions # for every step self.unique_steps = set(processed_steps.values()) # Allocate experiment global variables - self.initial_start_time = self.operating_conditions_steps[0].start_time + self.initial_start_time = self.steps[0].start_time - if ( - self.operating_conditions_steps[0].end_time is not None - and self.initial_start_time is None - ): + if self.steps[0].end_time is not None and self.initial_start_time is None: raise ValueError( "When using experiments with `start_time`, the first step must have a " "`start_time`." @@ -129,8 +91,32 @@ def __init__( self.termination_string = termination self.termination = self.read_termination(termination) + @staticmethod + def process_steps(unprocessed_steps, period, temp): + processed_steps = {} + for step in unprocessed_steps: + if repr(step) in processed_steps: + continue + elif isinstance(step, str): + processed_step = pybamm.step.string(step) + elif isinstance(step, pybamm.step.BaseStep): + # Copy the step to avoid modifying the original with the period and + # temperature and any other changes + processed_step = step.copy() + else: + raise TypeError("Operating conditions must be a Step object or string.") + + if processed_step.period is None: + processed_step.period = period + if processed_step.temperature is None: + processed_step.temperature = temp + + processed_steps[repr(step)] = processed_step + + return processed_steps + def __str__(self): - return str(self.operating_conditions_cycles) + return str(self.cycles) def copy(self): return Experiment(*self.args) @@ -138,9 +124,25 @@ def copy(self): def __repr__(self): return f"pybamm.Experiment({self!s})" - def read_termination(self, termination): + @staticmethod + def read_termination(termination): """ Read the termination reason. If this condition is hit, the experiment will stop. + + Parameters + ---------- + termination : str or list[str], optional + A single string, or a list of strings, representing the conditions to terminate the experiment. + Only capacity or voltage can be provided as a termination reason. + e.g. '4 Ah capacity' or ['80% capacity', '2.5 V'] + + Returns + ------- + dict + A dictionary of the termination conditions. + e.g. {'capacity': (4.0, 'Ah')} or + {'capacity': (80.0, '%'), 'voltage': (2.5, 'V')} + """ if termination is None: return {} @@ -167,6 +169,26 @@ def read_termination(self, termination): elif term.endswith("V"): end_discharge_V = term.split("V")[0] termination_dict["voltage"] = (float(end_discharge_V), "V") + elif any( + [ + term.endswith(key) + for key in [ + "hour", + "hours", + "h", + "hr", + "minute", + "minutes", + "m", + "min", + "second", + "seconds", + "s", + "sec", + ] + ] + ): + termination_dict["time"] = _convert_time_to_seconds(term) else: raise ValueError( "Only capacity or voltage can be provided as a termination reason, " @@ -189,7 +211,7 @@ def search_tag(self, tag): A list of cycles in which the tag appears """ cycles = [] - for i, cycle in enumerate(self.operating_conditions_cycles): + for i, cycle in enumerate(self.cycles): for step in cycle: if tag in step.tags: cycles.append(i) @@ -197,26 +219,19 @@ def search_tag(self, tag): return cycles - def _set_next_start_time(self, operating_conditions): - if all(isinstance(i, str) for i in operating_conditions): - return operating_conditions - + @staticmethod + def _set_next_start_time(steps): end_time = None next_start_time = None - for op in reversed(operating_conditions): - if isinstance(op, str): - op = pybamm.step.string(op) - elif not isinstance(op, pybamm.step._Step): - raise TypeError( - "Operating conditions should be strings or _Step objects" - ) - - op.next_start_time = next_start_time - op.end_time = end_time + # Loop over the steps in reverse order, setting the end time of each step to the + # start time of the next step + for step in reversed(steps): + step.next_start_time = next_start_time + step.end_time = end_time - next_start_time = op.start_time + next_start_time = step.start_time if next_start_time: end_time = next_start_time - return operating_conditions + return steps diff --git a/pybamm/experiment/step/__init__.py b/pybamm/experiment/step/__init__.py index e3b9ff8bd0..1810844d3f 100644 --- a/pybamm/experiment/step/__init__.py +++ b/pybamm/experiment/step/__init__.py @@ -1,3 +1,5 @@ from .steps import * -from .steps import _Step +from .base_step import BaseStep, BaseStepExplicit, BaseStepImplicit from .step_termination import * + +__all__ = ['base_step', 'step_termination', 'steps'] diff --git a/pybamm/experiment/step/_steps_util.py b/pybamm/experiment/step/base_step.py similarity index 53% rename from pybamm/experiment/step/_steps_util.py rename to pybamm/experiment/step/base_step.py index f44cf52113..3c8ff3f1ae 100644 --- a/pybamm/experiment/step/_steps_util.py +++ b/pybamm/experiment/step/base_step.py @@ -5,6 +5,7 @@ import numpy as np from datetime import datetime from .step_termination import _read_termination +import numbers _examples = """ @@ -25,17 +26,15 @@ """ -class _Step: +class BaseStep: """ Class representing one step in an experiment. All experiment steps are functions that return an instance of this class. - This class is not intended to be used directly. + This class is not intended to be used directly, but can be subtyped to create a + custom experiment step. Parameters ---------- - typ : str - The type of step, can be "current", "voltage", "c_rate", "power", - or "resistance". value : float The value of the step, corresponding to the type of step. Can be a number, a 2-tuple (for cccv_ode), or a 2-column array (for drive cycles) @@ -52,15 +51,16 @@ class _Step: the value should be a valid temperature string, e.g. "25 oC". tags : str or list, optional A string or list of strings indicating the tags associated with the step. - datetime : str or datetime, optional - A string or list of strings indicating the tags associated with the step. + start_time : str or datetime, optional + The start time of the step. description : str, optional A description of the step. + direction : str, optional + The direction of the step, e.g. "Charge" or "Discharge" or "Rest". """ def __init__( self, - typ, value, duration=None, termination=None, @@ -69,14 +69,34 @@ def __init__( tags=None, start_time=None, description=None, + direction=None, ): - self.type = typ + self.input_duration = duration + self.input_value = value + # Check if drive cycle + self.is_drive_cycle = isinstance(value, np.ndarray) + if self.is_drive_cycle: + if value.ndim != 2 or value.shape[1] != 2: + raise ValueError( + "Drive cycle must be a 2-column array with time in the first column" + " and current/C-rate/power/voltage/resistance in the second" + ) + # Check that drive cycle starts at t=0 + t = value[:, 0] + if t[0] != 0: + raise ValueError("Drive cycle must start at t=0") + + # Record whether the step uses the default duration + # This will be used by the experiment to check whether the step is feasible + self.uses_default_duration = duration is None + # Set duration + if self.uses_default_duration: + duration = self.default_duration(value) + self.duration = _convert_time_to_seconds(duration) # Record all the args for repr and hash - self.repr_args = f"{typ}, {value}" - self.hash_args = f"{typ}, {value}" - if duration: - self.repr_args += f", duration={duration}" + self.repr_args = f"{value}, duration={duration}" + self.hash_args = f"{value}" if termination: self.repr_args += f", termination={termination}" self.hash_args += f", termination={termination}" @@ -91,42 +111,37 @@ def __init__( self.repr_args += f", start_time={start_time}" if description: self.repr_args += f", description={description}" + if direction: + self.repr_args += f", direction={direction}" + self.hash_args += f", direction={direction}" - # Check if drive cycle - self.is_drive_cycle = isinstance(value, np.ndarray) + # If drive cycle, repeat the drive cycle until the end of the experiment, + # and create an interpolant if self.is_drive_cycle: - if value.ndim != 2 or value.shape[1] != 2: - raise ValueError( - "Drive cycle must be a 2-column array with time in the first column" - " and current/C-rate/power/voltage/resistance in the second" - ) - if duration is not None: - t_max = _convert_time_to_seconds(duration) - self.duration = _convert_time_to_seconds(duration) - if t_max > value[-1, 0]: - # duration longer than drive cycle values so loop - nloop = np.ceil(t_max / value[-1, 0]).astype(int) - tstep = np.diff(value[:, 0])[0] - t = [] - y = [] - for i in range(nloop): - t.append(value[:, 0] + ((value[-1, 0] + tstep) * i)) - y.append(value[:, 1]) - t = np.asarray(t).flatten() - y = np.asarray(y).flatten() - else: - t, y = value[:, 0], value[:, 1] + t_max = self.duration + if t_max > value[-1, 0]: + # duration longer than drive cycle values so loop + nloop = np.ceil(t_max / value[-1, 0]).astype(int) + tstep = np.diff(value[:, 0])[0] + t = [] + y = [] + for i in range(nloop): + t.append(value[:, 0] + ((value[-1, 0] + tstep) * i)) + y.append(value[:, 1]) + t = np.asarray(t).flatten() + y = np.asarray(y).flatten() else: t, y = value[:, 0], value[:, 1] - self.duration = t.max() self.value = pybamm.Interpolant( - t, y, pybamm.t - pybamm.InputParameter("start time") + t, + y, + pybamm.t - pybamm.InputParameter("start time"), + name="Drive Cycle", ) self.period = np.diff(t).min() else: self.value = value - self.duration = _convert_time_to_seconds(duration) self.period = _convert_time_to_seconds(period) self.description = description @@ -157,6 +172,29 @@ def __init__( self.next_start_time = None self.end_time = None + self.direction = direction + + def copy(self): + """ + Return a copy of the step. + + Returns + ------- + :class:`pybamm.Step` + A copy of the step. + """ + return self.__class__( + self.input_value, + duration=self.input_duration, + termination=self.termination, + period=self.period, + temperature=self.temperature, + tags=self.tags, + start_time=self.start_time, + description=self.description, + direction=self.direction, + ) + def __str__(self): if self.description is not None: return self.description @@ -164,7 +202,7 @@ def __str__(self): return repr(self) def __repr__(self): - return f"_Step({self.repr_args})" + return f"Step({self.repr_args})" def basic_repr(self): """ @@ -172,7 +210,7 @@ def basic_repr(self): and temperature, which are the variables involved in processing the model. Also used for hashing. """ - return f"_Step({self.hash_args})" + return f"Step({self.hash_args})" def to_dict(self): """ @@ -184,7 +222,7 @@ def to_dict(self): A dictionary containing the step information. """ return { - "type": self.type, + "type": self.__class__.__name__, "value": self.value, "duration": self.duration, "termination": self.termination, @@ -196,14 +234,108 @@ def to_dict(self): } def __eq__(self, other): - return isinstance(other, _Step) and self.hash_args == other.hash_args + return isinstance(other, BaseStep) and self.hash_args == other.hash_args def __hash__(self): return hash(self.basic_repr()) - @property - def unit(self): - return _type_to_units[self.type] + def default_duration(self, value): + """ + Default duration for the step is one day (24 hours) or the duration of the + drive cycle + """ + if isinstance(value, np.ndarray): + t = value[:, 0] + return t[-1] + else: + return 24 * 3600 # one day in seconds + + def process_model(self, model, parameter_values): + new_model = model.new_copy() + new_parameter_values = parameter_values.copy() + new_model, new_parameter_values = self.set_up(new_model, new_parameter_values) + self.update_model_events(new_model) + + # Update temperature + if self.temperature is not None: + new_parameter_values["Ambient temperature [K]"] = self.temperature + + # Parameterise the model + parameterised_model = new_parameter_values.process_model( + new_model, inplace=False + ) + + return parameterised_model + + def update_model_events(self, new_model): + for term in self.termination: + event = term.get_event(new_model.variables, self) + if event is not None: + new_model.events.append(event) + + # Keep the min and max voltages as safeguards but add some tolerances + # so that they are not triggered before the voltage limits in the + # experiment + for i, event in enumerate(new_model.events): + if event.name in ["Minimum voltage [V]", "Maximum voltage [V]"]: + new_model.events[i] = pybamm.Event( + event.name, event.expression + 1, event.event_type + ) + + +class BaseStepExplicit(BaseStep): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def current_value(self, variables): + raise NotImplementedError + + def set_up(self, new_model, new_parameter_values): + new_parameter_values["Current function [A]"] = self.current_value( + new_model.variables + ) + return new_model, new_parameter_values + + +class BaseStepImplicit(BaseStep): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def get_parameter_values(self, variables): + return {} + + def get_submodel(self, model): + raise NotImplementedError + + def set_up(self, new_model, new_parameter_values): + # Create a new model where the current density is now a variable + # To do so, we replace all instances of the current density in the + # model with a current density variable, which is obtained from the + # FunctionControl submodel + # check which kind of external circuit model we need (differential + # or algebraic) + # Build the new submodel and update the model with it + submodel = self.get_submodel(new_model) + variables = new_model.variables + submodel.variables = submodel.get_fundamental_variables() + variables.update(submodel.variables) + submodel.variables.update(submodel.get_coupled_variables(variables)) + variables.update(submodel.variables) + submodel.set_rhs(variables) + submodel.set_algebraic(variables) + submodel.set_initial_conditions(variables) + new_model.rhs.update(submodel.rhs) + new_model.algebraic.update(submodel.algebraic) + new_model.initial_conditions.update(submodel.initial_conditions) + + # Set the "current function" to be the variable defined in the submodel + new_parameter_values["Current function [A]"] = submodel.variables["Current [A]"] + # Update any other parameters as necessary + new_parameter_values.update( + self.get_parameter_values(variables), check_already_exists=False + ) + + return new_model, new_parameter_values _type_to_units = { @@ -216,10 +348,16 @@ def unit(self): def _convert_time_to_seconds(time_and_units): """Convert a time in seconds, minutes or hours to a time in seconds""" - # If the time is a number, assume it is in seconds - if isinstance(time_and_units, (int, float)) or time_and_units is None: + if time_and_units is None: return time_and_units + # If the time is a number, assume it is in seconds + if isinstance(time_and_units, numbers.Number): + if time_and_units <= 0: + raise ValueError("time must be positive") + else: + return time_and_units + # Split number and units units = time_and_units.lstrip("0123456789.- ") time = time_and_units[: -len(units)] @@ -280,8 +418,8 @@ def _convert_electric(value_string): } try: typ = units_to_type[unit] - except KeyError: + except KeyError as error: raise ValueError( f"units must be 'A', 'V', 'W', 'Ohm', or 'C'. For example: {_examples}" - ) + ) from error return typ, value diff --git a/pybamm/experiment/step/step_termination.py b/pybamm/experiment/step/step_termination.py index 082711a305..d4ffbe1aab 100644 --- a/pybamm/experiment/step/step_termination.py +++ b/pybamm/experiment/step/step_termination.py @@ -1,5 +1,4 @@ import pybamm -import numpy as np class BaseTermination: @@ -18,7 +17,7 @@ class BaseTermination: def __init__(self, value): self.value = value - def get_event(self, variables, step_value): + def get_event(self, variables, step): """ Return a :class:`pybamm.Event` object corresponding to the termination event @@ -27,8 +26,8 @@ def get_event(self, variables, step_value): variables : dict Dictionary of model variables, to be used for selecting the variable(s) that determine the event - step_value : float or :class:`pybamm.Symbol` - Value of the step for which this is a termination event, to be used in some + step : :class:`pybamm.step.BaseStep` + Step for which this is a termination event, to be used in some cases to determine the sign of the event. """ raise NotImplementedError @@ -47,7 +46,7 @@ class CrateTermination(BaseTermination): (e.g. "C/10") is provided """ - def get_event(self, variables, step_value): + def get_event(self, variables, step): """ See :meth:`BaseTermination.get_event` """ @@ -64,7 +63,7 @@ class CurrentTermination(BaseTermination): (e.g. "1A") is provided """ - def get_event(self, variables, step_value): + def get_event(self, variables, step): """ See :meth:`BaseTermination.get_event` """ @@ -81,7 +80,7 @@ class VoltageTermination(BaseTermination): (e.g. "4.2V") is provided """ - def get_event(self, variables, step_value): + def get_event(self, variables, step): """ See :meth:`BaseTermination.get_event` """ @@ -90,24 +89,22 @@ def get_event(self, variables, step_value): # figure out whether the voltage event is greater than the starting # voltage (charge) or less (discharge) and set the sign of the # event accordingly - if isinstance(step_value, pybamm.Symbol): - inpt = {"start time": 0} - init_curr = step_value.evaluate(t=0, inputs=inpt).flatten()[0] - else: - init_curr = step_value - sign = np.sign(init_curr) - if sign > 0: - name = "Discharge" - else: - name = "Charge" - if sign != 0: - # Event should be positive at initial conditions for both - # charge and discharge - event = pybamm.Event( - f"{name} voltage cut-off [V] [experiment]", - sign * (variables["Battery voltage [V]"] - self.value), - ) - return event + direction = step.direction.capitalize() + if direction == "Charge": + sign = -1 + elif direction == "Discharge": + sign = 1 + elif direction == "Rest": + # No event for rest steps + return None + + # Event should be positive at initial conditions for both + # charge and discharge + event = pybamm.Event( + f"{direction} voltage cut-off [V] [experiment]", + sign * (variables["Battery voltage [V]"] - self.value), + ) + return event class CustomTermination(BaseTermination): @@ -143,7 +140,7 @@ def __init__(self, name, event_function): self.name = name self.event_function = event_function - def get_event(self, variables, step_value): + def get_event(self, variables, step): """ See :meth:`BaseTermination.get_event` """ diff --git a/pybamm/experiment/step/steps.py b/pybamm/experiment/step/steps.py index 2f2e9e31a4..580f672ece 100644 --- a/pybamm/experiment/step/steps.py +++ b/pybamm/experiment/step/steps.py @@ -1,16 +1,20 @@ -# -# Public functions to create steps for use in an experiment. -# -from ._steps_util import _Step, _convert_electric, _examples +import numpy as np +import pybamm +from .base_step import ( + BaseStepExplicit, + BaseStepImplicit, + _convert_electric, + _examples, +) -def string(string, **kwargs): +def string(text, **kwargs): """ Create a step from a string. Parameters ---------- - string : str + text : str The string to parse. Each operating condition should be of the form "Do this for this long" or "Do this until this happens". For example, "Charge at 1 C for 1 hour", or "Charge at 1 C until 4.2 V", or "Charge @@ -20,49 +24,48 @@ def string(string, **kwargs): "3 minutes" or "1 hour". The stopping conditions should be a circuit state, e.g. "1 A", "C/50" or "3 V". **kwargs - Any other keyword arguments are passed to the :class:`pybamm.step._Step` - class. + Any other keyword arguments are passed to the step class Returns ------- - :class:`pybamm.step._Step` + :class:`pybamm.step.BaseStep` A step parsed from the string. """ - if not isinstance(string, str): + if not isinstance(text, str): raise TypeError("Input to step.string() must be a string") - if "oC" in string: + if "oC" in text: raise ValueError( "Temperature must be specified as a keyword argument " "instead of in the string" ) # Save the original string - description = string + description = text # extract period - if "period)" in string: + if "period)" in text: if "period" in kwargs: raise ValueError( "Period cannot be specified both as a keyword argument " "and in the string" ) - string, period_full = string.split(" (") + text, period_full = text.split(" (") period, _ = period_full.split(" period)") kwargs["period"] = period # extract termination condition based on "until" keyword - if "until" in string: + if "until" in text: # e.g. "Charge at 4 A until 3.8 V" - string, termination = string.split(" until ") + text, termination = text.split(" until ") # sometimes we use "or until" instead of "until", so remove "or" - string = string.replace(" or", "") + text = text.replace(" or", "") else: termination = None # extract duration based on "for" keyword - if "for" in string: + if "for" in text: # e.g. "Charge at 4 A for 3 hours" - string, duration = string.split(" for ") + text, duration = text.split(" for ") else: duration = None @@ -73,10 +76,10 @@ def string(string, **kwargs): ) # read remaining instruction - if string.startswith("Rest"): - typ = "current" + if text.startswith("Rest"): + step_class = Current value = 0 - elif string.startswith("Run"): + elif text.startswith("Run"): raise ValueError( "Simulating drive cycles with 'Run' has been deprecated. Use the " "pybamm.step.current/voltage/power/c_rate/resistance() functions " @@ -86,7 +89,7 @@ def string(string, **kwargs): # split by what is before and after "at" # e.g. "Charge at 4 A" -> ["Charge", "4 A"] # e.g. "Discharge at C/2" -> ["Discharge", "C/2"] - instruction, value_string = string.split(" at ") + instruction, value_string = text.split(" at ") if instruction == "Charge": sign = -1 elif instruction in ["Discharge", "Hold"]: @@ -102,8 +105,16 @@ def string(string, **kwargs): # Make current positive for discharge and negative for charge value *= sign - return _Step( - typ, + # Use the appropriate step class + step_class = { + "current": Current, + "voltage": Voltage, + "power": Power, + "C-rate": CRate, + "resistance": Resistance, + }[typ] + + return step_class( value, duration=duration, termination=termination, @@ -112,109 +123,137 @@ def string(string, **kwargs): ) -def current(value, **kwargs): +class Current(BaseStepExplicit): """ - Create a current-controlled step. + Current-controlled step, see :class:`pybamm.step.BaseStep` for arguments. Current is positive for discharge and negative for charge. + """ - Parameters - ---------- - value : float - The current value in A. It can be a number or a 2-column array (for drive cycles). - **kwargs - Any other keyword arguments are passed to the :class:`pybamm.step._Step` - class. + def __init__(self, value, **kwargs): + kwargs["direction"] = value_based_charge_or_discharge(value) + super().__init__(value, **kwargs) - Returns - ------- - :class:`pybamm.step._Step` - A current-controlled step. + def current_value(self, variables): + return self.value + + +def current(value, **kwargs): + """ + Current-controlled step, see :class:`pybamm.step.Current`. """ - return _Step("current", value, **kwargs) + return Current(value, **kwargs) -def c_rate(value, **kwargs): +class CRate(BaseStepExplicit): """ - Create a C-rate controlled step. + C-rate-controlled step, see :class:`pybamm.step.BaseStep` for arguments. C-rate is positive for discharge and negative for charge. + """ - Parameters - ---------- - value : float - The C-rate value. It can be a number or a 2-column array (for drive cycles). - **kwargs - Any other keyword arguments are passed to the :class:`pybamm.step._Step` - class. + def __init__(self, value, **kwargs): + kwargs["direction"] = value_based_charge_or_discharge(value) + super().__init__(value, **kwargs) - Returns - ------- - :class:`pybamm.step._Step` - A C-rate controlled step. + def current_value(self, variables): + return self.value * pybamm.Parameter("Nominal cell capacity [A.h]") + + def default_duration(self, value): + # "value" is C-rate, so duration is "1 / value" hours in seconds + # with a 2x safety factor + return 1 / abs(value) * 3600 * 2 + + +def c_rate(value, **kwargs): """ - return _Step("C-rate", value, **kwargs) + C-rate-controlled step, see :class:`pybamm.step.CRate`. + """ + return CRate(value, **kwargs) -def voltage(value, **kwargs): +class Voltage(BaseStepImplicit): """ - Create a voltage-controlled step. + Voltage-controlled step, see :class:`pybamm.step.BaseStep` for arguments. Voltage should always be positive. + """ - Parameters - ---------- - value : float - The voltage value in V. It can be a number or a 2-column array (for drive cycles). - **kwargs - Any other keyword arguments are passed to the :class:`pybamm.step._Step` - class. + def get_parameter_values(self, variables): + return {"Voltage function [V]": self.value} - Returns - ------- - :class:`pybamm.step._Step` - A voltage-controlled step. + def get_submodel(self, model): + return pybamm.external_circuit.VoltageFunctionControl( + model.param, model.options + ) + + +def voltage(*args, **kwargs): """ - return _Step("voltage", value, **kwargs) + Voltage-controlled step, see :class:`pybamm.step.Voltage`. + """ + return Voltage(*args, **kwargs) -def power(value, **kwargs): +class Power(BaseStepImplicit): """ - Create a power-controlled step. + Power-controlled step. Power is positive for discharge and negative for charge. Parameters ---------- value : float - The power value in W. It can be a number or a 2-column array (for drive cycles). + The value of the power function [W]. **kwargs - Any other keyword arguments are passed to the :class:`pybamm.step._Step` - class. + Any other keyword arguments are passed to the step class + """ - Returns - ------- - :class:`pybamm.step._Step` - A power-controlled step. + def __init__(self, value, **kwargs): + kwargs["direction"] = value_based_charge_or_discharge(value) + super().__init__(value, **kwargs) + + def get_parameter_values(self, variables): + return {"Power function [W]": self.value} + + def get_submodel(self, model): + return pybamm.external_circuit.PowerFunctionControl(model.param, model.options) + + +def power(value, **kwargs): """ - return _Step("power", value, **kwargs) + Power-controlled step, see :class:`pybamm.step.Power`. + """ + return Power(value, **kwargs) -def resistance(value, **kwargs): +class Resistance(BaseStepImplicit): """ - Create a resistance-controlled step. + Resistance-controlled step. Resistance is positive for discharge and negative for charge. Parameters ---------- value : float - The resistance value in Ohm. It can be a number or a 2-column array (for drive cycles). + The value of the power function [W]. **kwargs - Any other keyword arguments are passed to the :class:`pybamm.step._Step` - class. + Any other keyword arguments are passed to the step class + """ - Returns - ------- - :class:`pybamm.step._Step` - A resistance-controlled step. + def __init__(self, value, **kwargs): + kwargs["direction"] = value_based_charge_or_discharge(value) + super().__init__(value, **kwargs) + + def get_parameter_values(self, variables): + return {"Resistance function [Ohm]": self.value} + + def get_submodel(self, model): + return pybamm.external_circuit.ResistanceFunctionControl( + model.param, model.options + ) + + +def resistance(value, **kwargs): """ - return _Step("resistance", value, **kwargs) + Resistance-controlled step, see :class:`pybamm.step.Resistance`. + """ + return Resistance(value, **kwargs) def rest(duration=None, **kwargs): @@ -222,4 +261,182 @@ def rest(duration=None, **kwargs): Create a rest step, equivalent to a constant current step with value 0 (see :meth:`pybamm.step.current`). """ - return current(0, duration=duration, **kwargs) + return Current(0, duration=duration, **kwargs) + + +class CustomStepExplicit(BaseStepExplicit): + """ + Custom step class where the current value is explicitly given as a function of + other variables. When using this class, the user must be careful not to create + an expression that depends on the current itself, as this will lead to a + circular dependency. For example, in some models, the voltage is an explicit + function of the current, so the user should not create a step that depends on + the voltage. An expression that works for one model may not work for another. + + Parameters + ---------- + current_value_function : callable + A function that takes in a dictionary of variables and returns the current + value. + duration : float, optional + The duration of the step in seconds. + termination : str or list, optional + A string or list of strings indicating the condition(s) that will terminate the + step. If a list, the step will terminate when any of the conditions are met. + period : float or string, optional + The period of the step. If a float, the value is in seconds. If a string, the + value should be a valid time string, e.g. "1 hour". + temperature : float or string, optional + The temperature of the step. If a float, the value is in Kelvin. If a string, + the value should be a valid temperature string, e.g. "25 oC". + tags : str or list, optional + A string or list of strings indicating the tags associated with the step. + start_time : str or datetime, optional + The start time of the step. + description : str, optional + A description of the step. + direction : str, optional + The direction of the step, e.g. "Charge" or "Discharge" or "Rest". + + Examples + -------- + Control the current to always be equal to a target power divided by voltage + (this is one way to implement a power control step): + + >>> def current_function(variables): + ... P = 4 + ... V = variables["Voltage [V]"] + ... return P / V + + Create the step with a 2.5 V termination condition: + + >>> step = pybamm.step.CustomStepExplicit(current_function, termination="2.5V") + """ + + def __init__(self, current_value_function, **kwargs): + super().__init__(None, **kwargs) + self.current_value_function = current_value_function + self.kwargs = kwargs + + def current_value(self, variables): + return self.current_value_function(variables) + + def copy(self): + return CustomStepExplicit(self.current_value_function, **self.kwargs) + + +class CustomStepImplicit(BaseStepImplicit): + """ + Custom step, see :class:`pybamm.step.BaseStep` for arguments. + + Parameters + ---------- + current_rhs_function : callable + A function that takes in a dictionary of variables and returns the equation + controlling the current. + + control : str, optional + Whether the control is algebraic or differential. Default is algebraic, in + which case the equation is + + .. math:: + 0 = f(\\text{{variables}}) + + where :math:`f` is the current_rhs_function. + + If control is "differential", the equation is + + .. math:: + \\frac{dI}{dt} = f(\\text{{variables}}) + + duration : float, optional + The duration of the step in seconds. + termination : str or list, optional + A string or list of strings indicating the condition(s) that will terminate the + step. If a list, the step will terminate when any of the conditions are met. + period : float or string, optional + The period of the step. If a float, the value is in seconds. If a string, the + value should be a valid time string, e.g. "1 hour". + temperature : float or string, optional + The temperature of the step. If a float, the value is in Kelvin. If a string, + the value should be a valid temperature string, e.g. "25 oC". + tags : str or list, optional + A string or list of strings indicating the tags associated with the step. + start_time : str or datetime, optional + The start time of the step. + description : str, optional + A description of the step. + direction : str, optional + The direction of the step, e.g. "Charge" or "Discharge" or "Rest". + + Examples + -------- + Control the current so that the voltage is constant (without using the built-in + voltage control): + + >>> def voltage_control(variables): + ... V = variables["Voltage [V]"] + ... return V - 4.2 + + Create the step with a duration of 1h. In this case we don't need to specify that + the control is algebraic, as this is the default. + + >>> step = pybamm.step.CustomStepImplicit(voltage_control, duration=3600) + + Alternatively, control the current by a differential equation to achieve a + target power: + + >>> def power_control(variables): + ... V = variables["Voltage [V]"] + ... # Large time constant to avoid large overshoot. The user should be careful + ... # to choose a time constant that is appropriate for the model being used, + ... # as well as choosing the appropriate sign for the time constant. + ... K_V = 100 + ... return K_V * (V - 4.2) + + Create the step with a 2.5 V termination condition. Now we need to specify that + the control is differential. + + >>> step = pybamm.step.CustomStepImplicit( + ... power_control, termination="2.5V", control="differential" + ... ) + """ + + def __init__(self, current_rhs_function, control="algebraic", **kwargs): + super().__init__(None, **kwargs) + self.current_rhs_function = current_rhs_function + if control not in ["algebraic", "differential"]: + raise ValueError("control must be either 'algebraic' or 'differential'") + self.control = control + self.kwargs = kwargs + + def get_submodel(self, model): + return pybamm.external_circuit.FunctionControl( + model.param, self.current_rhs_function, model.options, control=self.control + ) + + def copy(self): + return CustomStepImplicit( + self.current_rhs_function, self.control, **self.kwargs + ) + + +def value_based_charge_or_discharge(step_value): + """ + Determine whether the step is a charge or discharge step based on the value of the + step + """ + if isinstance(step_value, np.ndarray): + init_curr = step_value[0, 1] + elif isinstance(step_value, pybamm.Symbol): + inpt = {"start time": 0} + init_curr = step_value.evaluate(t=0, inputs=inpt).flatten()[0] + else: + init_curr = step_value + sign = np.sign(init_curr) + if sign == 0: + return "Rest" + elif sign > 0: + return "Discharge" + else: + return "Charge" diff --git a/pybamm/expression_tree/__init__.py b/pybamm/expression_tree/__init__.py index e69de29bb2..0b06746e61 100644 --- a/pybamm/expression_tree/__init__.py +++ b/pybamm/expression_tree/__init__.py @@ -0,0 +1,5 @@ +__all__ = ['array', 'averages', 'binary_operators', 'broadcasts', + 'concatenations', 'exceptions', 'functions', 'independent_variable', + 'input_parameter', 'interpolant', 'matrix', 'operations', + 'parameter', 'printing', 'scalar', 'state_vector', 'symbol', + 'unary_operators', 'variable', 'vector'] diff --git a/pybamm/expression_tree/array.py b/pybamm/expression_tree/array.py index 7694cbc170..e16b7d17aa 100644 --- a/pybamm/expression_tree/array.py +++ b/pybamm/expression_tree/array.py @@ -1,11 +1,13 @@ # # NumpyArray class # +from __future__ import annotations import numpy as np from scipy.sparse import csr_matrix, issparse import pybamm -from pybamm.util import have_optional_dependency +from pybamm.type_definitions import DomainType, AuxiliaryDomainType, DomainsType +import sympy class Array(pybamm.Symbol): @@ -36,13 +38,13 @@ class Array(pybamm.Symbol): def __init__( self, - entries, - name=None, - domain=None, - auxiliary_domains=None, - domains=None, - entries_string=None, - ): + entries: np.ndarray | list[float] | csr_matrix, + name: str | None = None, + domain: DomainType = None, + auxiliary_domains: AuxiliaryDomainType = None, + domains: DomainsType = None, + entries_string: str | None = None, + ) -> None: # if if isinstance(entries, list): entries = np.array(entries) @@ -59,8 +61,6 @@ def __init__( @classmethod def _from_json(cls, snippet: dict): - instance = cls.__new__(cls) - if isinstance(snippet["entries"], dict): matrix = csr_matrix( ( @@ -73,14 +73,12 @@ def _from_json(cls, snippet: dict): else: matrix = snippet["entries"] - instance.__init__( + return cls( matrix, name=snippet["name"], domains=snippet["domains"], ) - return instance - @property def entries(self): return self._entries @@ -100,7 +98,7 @@ def entries_string(self): return self._entries_string @entries_string.setter - def entries_string(self, value): + def entries_string(self, value: None | tuple): # We must include the entries in the hash, since different arrays can be # indistinguishable by class, name and domain alone # Slightly different syntax for sparse and non-sparse matrices @@ -110,10 +108,10 @@ def entries_string(self, value): entries = self._entries if issparse(entries): dct = entries.__dict__ - self._entries_string = ["shape", str(dct["_shape"])] + entries_string = ["shape", str(dct["_shape"])] for key in ["data", "indices", "indptr"]: - self._entries_string += [key, dct[key].tobytes()] - self._entries_string = tuple(self._entries_string) + entries_string += [key, dct[key].tobytes()] + self._entries_string = tuple(entries_string) # self._entries_string = str(entries.__dict__) else: self._entries_string = (entries.tobytes(),) @@ -124,13 +122,17 @@ def set_id(self): (self.__class__, self.name, *self.entries_string, *tuple(self.domain)) ) - def _jac(self, variable): + def _jac(self, variable) -> pybamm.Matrix: """See :meth:`pybamm.Symbol._jac()`.""" # Return zeros of correct size jac = csr_matrix((self.size, variable.evaluation_array.count(True))) return pybamm.Matrix(jac) - def create_copy(self): + def create_copy( + self, + new_children=None, + perform_simplifications: bool = True, + ): """See :meth:`pybamm.Symbol.new_copy()`.""" return self.__class__( self.entries, @@ -139,7 +141,13 @@ def create_copy(self): entries_string=self.entries_string, ) - def _base_evaluate(self, t=None, y=None, y_dot=None, inputs=None): + def _base_evaluate( + self, + t: float | None = None, + y: np.ndarray | None = None, + y_dot: np.ndarray | None = None, + inputs: dict | str | None = None, + ): """See :meth:`pybamm.Symbol._base_evaluate()`.""" return self._entries @@ -147,9 +155,8 @@ def is_constant(self): """See :meth:`pybamm.Symbol.is_constant()`.""" return True - def to_equation(self): + def to_equation(self) -> sympy.Array: """Returns the value returned by the node when evaluated.""" - sympy = have_optional_dependency("sympy") entries_list = self.entries.tolist() return sympy.Array(entries_list) @@ -178,7 +185,7 @@ def to_json(self): return json_dict -def linspace(start, stop, num=50, **kwargs): +def linspace(start: float, stop: float, num: int = 50, **kwargs) -> pybamm.Array: """ Creates a linearly spaced array by calling `numpy.linspace` with keyword arguments 'kwargs'. For a list of 'kwargs' see the @@ -187,7 +194,9 @@ def linspace(start, stop, num=50, **kwargs): return pybamm.Array(np.linspace(start, stop, num, **kwargs)) -def meshgrid(x, y, **kwargs): +def meshgrid( + x: pybamm.Array, y: pybamm.Array, **kwargs +) -> tuple[pybamm.Array, pybamm.Array]: """ Return coordinate matrices as from coordinate vectors by calling `numpy.meshgrid` with keyword arguments 'kwargs'. For a list of 'kwargs' diff --git a/pybamm/expression_tree/averages.py b/pybamm/expression_tree/averages.py index 81834d5871..5fa6c5f00f 100644 --- a/pybamm/expression_tree/averages.py +++ b/pybamm/expression_tree/averages.py @@ -1,6 +1,8 @@ # # Classes and methods for averaging # +from __future__ import annotations +from typing import Callable import pybamm @@ -14,13 +16,20 @@ class _BaseAverage(pybamm.Integral): The child node """ - def __init__(self, child, name, integration_variable): + def __init__( + self, + child: pybamm.Symbol, + name: str, + integration_variable: ( + list[pybamm.IndependentVariable] | pybamm.IndependentVariable + ), + ) -> None: super().__init__(child, integration_variable) self.name = name class XAverage(_BaseAverage): - def __init__(self, child): + def __init__(self, child: pybamm.Symbol) -> None: if all(n in child.domain[0] for n in ["negative", "particle"]): x = pybamm.standard_spatial_vars.x_n elif all(n in child.domain[0] for n in ["positive", "particle"]): @@ -30,56 +39,110 @@ def __init__(self, child): integration_variable = x super().__init__(child, "x-average", integration_variable) - def _unary_new_copy(self, child): - """See :meth:`UnaryOperator._unary_new_copy()`.""" - return x_average(child) + def _unary_new_copy( + self, child: pybamm.Symbol, perform_simplifications: bool = True + ): + """ + Creates a new copy of the operator with the child `child`. + + Uses the convenience function :meth:`x_average` to perform checks before + creating an XAverage object. + """ + if perform_simplifications: + return x_average(child) + else: + return XAverage(child) class YZAverage(_BaseAverage): - def __init__(self, child): + def __init__(self, child: pybamm.Symbol) -> None: y = pybamm.standard_spatial_vars.y z = pybamm.standard_spatial_vars.z - integration_variable = [y, z] + integration_variable: list[pybamm.IndependentVariable] = [y, z] super().__init__(child, "yz-average", integration_variable) - def _unary_new_copy(self, child): - """See :meth:`UnaryOperator._unary_new_copy()`.""" - return yz_average(child) + def _unary_new_copy( + self, child: pybamm.Symbol, perform_simplifications: bool = True + ): + """ + Creates a new copy of the operator with the child `child`. + + Uses the convenience function :meth:`yz_average` to perform checks before + creating an YZAverage object. + """ + if perform_simplifications: + return yz_average(child) + else: + return YZAverage(child) class ZAverage(_BaseAverage): - def __init__(self, child): - integration_variable = [pybamm.standard_spatial_vars.z] + def __init__(self, child: pybamm.Symbol) -> None: + integration_variable: list[pybamm.IndependentVariable] = [ + pybamm.standard_spatial_vars.z + ] super().__init__(child, "z-average", integration_variable) - def _unary_new_copy(self, child): - """See :meth:`UnaryOperator._unary_new_copy()`.""" - return z_average(child) + def _unary_new_copy( + self, child: pybamm.Symbol, perform_simplifications: bool = True + ): + """ + Creates a new copy of the operator with the child `child`. + + Uses the convenience function :meth:`z_average` to perform checks before + creating an ZAverage object. + """ + if perform_simplifications: + return z_average(child) + else: + return ZAverage(child) class RAverage(_BaseAverage): - def __init__(self, child): - integration_variable = [pybamm.SpatialVariable("r", child.domain)] + def __init__(self, child: pybamm.Symbol) -> None: + integration_variable: list[pybamm.IndependentVariable] = [ + pybamm.SpatialVariable("r", child.domain) + ] super().__init__(child, "r-average", integration_variable) - def _unary_new_copy(self, child): - """See :meth:`UnaryOperator._unary_new_copy()`.""" - return r_average(child) + def _unary_new_copy( + self, child: pybamm.Symbol, perform_simplifications: bool = True + ): + """ + Creates a new copy of the operator with the child `child`. + + Uses the convenience function :meth:`r_average` to perform checks before + creating an RAverage object. + """ + if perform_simplifications: + return r_average(child) + else: + return RAverage(child) class SizeAverage(_BaseAverage): - def __init__(self, child, f_a_dist): + def __init__(self, child: pybamm.Symbol, f_a_dist) -> None: R = pybamm.SpatialVariable("R", domains=child.domains, coord_sys="cartesian") - integration_variable = [R] + integration_variable: list[pybamm.IndependentVariable] = [R] super().__init__(child, "size-average", integration_variable) self.f_a_dist = f_a_dist - def _unary_new_copy(self, child): - """See :meth:`UnaryOperator._unary_new_copy()`.""" - return size_average(child, f_a_dist=self.f_a_dist) + def _unary_new_copy( + self, child: pybamm.Symbol, perform_simplifications: bool = True + ): + """ + Creates a new copy of the operator with the child `child`. + + Uses the convenience function :meth:`size_average` to perform checks before + creating an SizeAverage object. + """ + if perform_simplifications: + return size_average(child, f_a_dist=self.f_a_dist) + else: + return SizeAverage(child, f_a_dist=self.f_a_dist) -def x_average(symbol): +def x_average(symbol: pybamm.Symbol) -> pybamm.Symbol: """ Convenience function for creating an average in the x-direction. @@ -168,7 +231,7 @@ def x_average(symbol): return XAverage(symbol) -def z_average(symbol): +def z_average(symbol: pybamm.Symbol) -> pybamm.Symbol: """ Convenience function for creating an average in the z-direction. @@ -205,7 +268,7 @@ def z_average(symbol): return ZAverage(symbol) -def yz_average(symbol): +def yz_average(symbol: pybamm.Symbol) -> pybamm.Symbol: """ Convenience function for creating an average in the y-z-direction. @@ -239,11 +302,11 @@ def yz_average(symbol): return YZAverage(symbol) -def xyz_average(symbol): +def xyz_average(symbol: pybamm.Symbol) -> pybamm.Symbol: return yz_average(x_average(symbol)) -def r_average(symbol): +def r_average(symbol: pybamm.Symbol) -> pybamm.Symbol: """ Convenience function for creating an average in the r-direction. @@ -286,7 +349,9 @@ def r_average(symbol): return RAverage(symbol) -def size_average(symbol, f_a_dist=None): +def size_average( + symbol: pybamm.Symbol, f_a_dist: pybamm.Symbol | None = None +) -> pybamm.Symbol: """Convenience function for averaging over particle size R using the area-weighted particle-size distribution. @@ -339,7 +404,10 @@ def size_average(symbol, f_a_dist=None): return SizeAverage(symbol, f_a_dist) -def _sum_of_averages(symbol, average_function): +def _sum_of_averages( + symbol: pybamm.Addition | pybamm.Subtraction, + average_function: Callable[[pybamm.Symbol], pybamm.Symbol], +): if isinstance(symbol, pybamm.Addition): return average_function(symbol.left) + average_function(symbol.right) elif isinstance(symbol, pybamm.Subtraction): diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py index 3d70741785..1d630887b2 100644 --- a/pybamm/expression_tree/binary_operators.py +++ b/pybamm/expression_tree/binary_operators.py @@ -1,24 +1,32 @@ # # Binary operator classes # +from __future__ import annotations import numbers import numpy as np +import sympy from scipy.sparse import csr_matrix, issparse import functools import pybamm -from pybamm.util import have_optional_dependency +from typing import Callable, cast -def _preprocess_binary(left, right): - if isinstance(left, numbers.Number): +# create type alias(s) +from pybamm.type_definitions import ChildSymbol, ChildValue, Numeric + + +def _preprocess_binary( + left: ChildSymbol, right: ChildSymbol +) -> tuple[pybamm.Symbol, pybamm.Symbol]: + if isinstance(left, (float, int, np.number)): left = pybamm.Scalar(left) elif isinstance(left, np.ndarray): if left.ndim > 1: raise ValueError("left must be a 1D array") left = pybamm.Vector(left) - if isinstance(right, numbers.Number): + if isinstance(right, (float, int, np.number)): right = pybamm.Scalar(right) elif isinstance(right, np.ndarray): if right.ndim > 1: @@ -28,9 +36,7 @@ def _preprocess_binary(left, right): # Check both left and right are pybamm Symbols if not (isinstance(left, pybamm.Symbol) and isinstance(right, pybamm.Symbol)): raise NotImplementedError( - """BinaryOperator not implemented for symbols of type {} and {}""".format( - type(left), type(right) - ) + f"""BinaryOperator not implemented for symbols of type {type(left)} and {type(right)}""" ) # Do some broadcasting in special cases, to avoid having to do this manually @@ -60,8 +66,10 @@ class BinaryOperator(pybamm.Symbol): rhs child node (converted to :class:`Scalar` if Number) """ - def __init__(self, name, left, right): - left, right = _preprocess_binary(left, right) + def __init__( + self, name: str, left_child: ChildSymbol, right_child: ChildSymbol + ) -> None: + left, right = _preprocess_binary(left_child, right_child) domains = self.get_children_domains([left, right]) super().__init__(name, children=[left, right], domains=domains) @@ -105,28 +113,49 @@ def __str__(self): right_str = f"{self.right!s}" return f"{left_str} {self.name} {right_str}" - def create_copy(self): + def create_copy( + self, + new_children: list[pybamm.Symbol] | None = None, + perform_simplifications: bool = True, + ): """See :meth:`pybamm.Symbol.new_copy()`.""" - # process children - new_left = self.left.new_copy() - new_right = self.right.new_copy() + if new_children and len(new_children) != 2: + raise ValueError( + f"Symbol of type {type(self)} must have exactly two children." + ) + children = self._children_for_copying(new_children) + + if not perform_simplifications: + out = self.__class__(children[0], children[1]) + else: + # creates a new instance using the overloaded binary operator to perform + # additional simplifications, rather than just calling the constructor + out = self._binary_new_copy(children[0], children[1]) - # make new symbol, ensure domain(s) remain the same - out = self._binary_new_copy(new_left, new_right) out.copy_domains(self) return out - def _binary_new_copy(self, left, right): + def _binary_new_copy(self, left: ChildSymbol, right: ChildSymbol): """ - Default behaviour for new_copy. - This copies the behaviour of `_binary_evaluate`, but since `left` and `right` - are symbols creates a new symbol instead of returning a value. + Performs the overloaded binary operation on the two symbols `left` and `right`, + to create a binary class instance after performing appropriate simplifying + checks. + + Default behaviour for _binary_new_copy copies the behaviour of `_binary_evaluate`, + but since `left` and `right` are symbols this creates a new symbol instead of + returning a value. """ return self._binary_evaluate(left, right) - def evaluate(self, t=None, y=None, y_dot=None, inputs=None): + def evaluate( + self, + t: float | None = None, + y: np.ndarray | None = None, + y_dot: np.ndarray | None = None, + inputs: dict | str | None = None, + ): """See :meth:`pybamm.Symbol.evaluate()`.""" left = self.left.evaluate(t, y, y_dot, inputs) right = self.right.evaluate(t, y, y_dot, inputs) @@ -148,7 +177,7 @@ def _binary_evaluate(self, left, right): f"{self.__class__} does not implement _binary_evaluate." ) - def _evaluates_on_edges(self, dimension): + def _evaluates_on_edges(self, dimension: str) -> bool: """See :meth:`pybamm.Symbol._evaluates_on_edges()`.""" return self.left.evaluates_on_edges(dimension) or self.right.evaluates_on_edges( dimension @@ -164,7 +193,6 @@ def _sympy_operator(self, left, right): def to_equation(self): """Convert the node and its subtree into a SymPy equation.""" - sympy = have_optional_dependency("sympy") if self.print_name is not None: return sympy.Symbol(self.print_name) else: @@ -188,11 +216,15 @@ class Power(BinaryOperator): A node in the expression tree representing a `**` power operator. """ - def __init__(self, left, right): + def __init__( + self, + left: ChildSymbol, + right: ChildSymbol, + ): """See :meth:`pybamm.BinaryOperator.__init__()`.""" super().__init__("**", left, right) - def _diff(self, variable): + def _diff(self, variable: pybamm.Symbol): """See :meth:`pybamm.Symbol._diff()`.""" # apply chain rule and power rule base, exponent = self.orphans @@ -217,7 +249,11 @@ def _binary_jac(self, left_jac, right_jac): right * left_jac + left * pybamm.log(left) * right_jac ) - def _binary_evaluate(self, left, right): + def _binary_evaluate( + self, + left: ChildSymbol, + right: ChildSymbol, + ): """See :meth:`pybamm.BinaryOperator._binary_evaluate()`.""" # don't raise RuntimeWarning for NaNs with np.errstate(invalid="ignore"): @@ -229,19 +265,23 @@ class Addition(BinaryOperator): A node in the expression tree representing an addition operator. """ - def __init__(self, left, right): + def __init__( + self, + left: ChildSymbol, + right: ChildSymbol, + ): """See :meth:`pybamm.BinaryOperator.__init__()`.""" super().__init__("+", left, right) - def _diff(self, variable): + def _diff(self, variable: pybamm.Symbol): """See :meth:`pybamm.Symbol._diff()`.""" return self.left.diff(variable) + self.right.diff(variable) - def _binary_jac(self, left_jac, right_jac): + def _binary_jac(self, left_jac: ChildValue, right_jac: ChildValue): """See :meth:`pybamm.BinaryOperator._binary_jac()`.""" return left_jac + right_jac - def _binary_evaluate(self, left, right): + def _binary_evaluate(self, left: ChildValue, right: ChildValue): """See :meth:`pybamm.BinaryOperator._binary_evaluate()`.""" return left + right @@ -251,12 +291,16 @@ class Subtraction(BinaryOperator): A node in the expression tree representing a subtraction operator. """ - def __init__(self, left, right): + def __init__( + self, + left: ChildSymbol, + right: ChildSymbol, + ): """See :meth:`pybamm.BinaryOperator.__init__()`.""" super().__init__("-", left, right) - def _diff(self, variable): + def _diff(self, variable: pybamm.Symbol): """See :meth:`pybamm.Symbol._diff()`.""" return self.left.diff(variable) - self.right.diff(variable) @@ -264,7 +308,7 @@ def _binary_jac(self, left_jac, right_jac): """See :meth:`pybamm.BinaryOperator._binary_jac()`.""" return left_jac - right_jac - def _binary_evaluate(self, left, right): + def _binary_evaluate(self, left: ChildValue, right: ChildValue): """See :meth:`pybamm.BinaryOperator._binary_evaluate()`.""" return left - right @@ -276,12 +320,16 @@ class Multiplication(BinaryOperator): matrix multiplication (e.g. scipy.sparse.coo.coo_matrix) """ - def __init__(self, left, right): + def __init__( + self, + left: ChildSymbol, + right: ChildSymbol, + ): """See :meth:`pybamm.BinaryOperator.__init__()`.""" super().__init__("*", left, right) - def _diff(self, variable): + def _diff(self, variable: pybamm.Symbol): """See :meth:`pybamm.Symbol._diff()`.""" # apply product rule left, right = self.orphans @@ -313,7 +361,11 @@ class MatrixMultiplication(BinaryOperator): A node in the expression tree representing a matrix multiplication operator. """ - def __init__(self, left, right): + def __init__( + self, + left: ChildSymbol, + right: ChildSymbol, + ): """See :meth:`pybamm.BinaryOperator.__init__()`.""" super().__init__("@", left, right) @@ -348,7 +400,6 @@ def _binary_evaluate(self, left, right): def _sympy_operator(self, left, right): """Override :meth:`pybamm.BinaryOperator._sympy_operator`""" - sympy = have_optional_dependency("sympy") left = sympy.Matrix(left) right = sympy.Matrix(right) return left * right @@ -359,11 +410,15 @@ class Division(BinaryOperator): A node in the expression tree representing a division operator. """ - def __init__(self, left, right): + def __init__( + self, + left: ChildSymbol, + right: ChildSymbol, + ): """See :meth:`pybamm.BinaryOperator.__init__()`.""" super().__init__("/", left, right) - def _diff(self, variable): + def _diff(self, variable: pybamm.Symbol): """See :meth:`pybamm.Symbol._diff()`.""" # apply quotient rule top, bottom = self.orphans @@ -403,11 +458,15 @@ class Inner(BinaryOperator): by a particular discretisation. """ - def __init__(self, left, right): + def __init__( + self, + left: ChildSymbol, + right: ChildSymbol, + ): """See :meth:`pybamm.BinaryOperator.__init__()`.""" super().__init__("inner product", left, right) - def _diff(self, variable): + def _diff(self, variable: pybamm.Symbol): """See :meth:`pybamm.Symbol._diff()`.""" # apply product rule left, right = self.orphans @@ -435,18 +494,22 @@ def _binary_evaluate(self, left, right): else: return left * right - def _binary_new_copy(self, left, right): + def _binary_new_copy( + self, + left: ChildSymbol, + right: ChildSymbol, + ): """See :meth:`pybamm.BinaryOperator._binary_new_copy()`.""" return pybamm.inner(left, right) - def _evaluates_on_edges(self, dimension): + def _evaluates_on_edges(self, dimension: str) -> bool: """See :meth:`pybamm.Symbol._evaluates_on_edges()`.""" return False -def inner(left, right): +def inner(left_child, right_child): """Return inner product of two symbols.""" - left, right = _preprocess_binary(left, right) + left, right = _preprocess_binary(left_child, right_child) # simplify multiply by scalar zero, being careful about shape if pybamm.is_scalar_zero(left): return pybamm.zeros_like(right) @@ -472,7 +535,11 @@ class Equality(BinaryOperator): nodes. Returns 1 if the two nodes evaluate to the same thing and 0 otherwise. """ - def __init__(self, left, right): + def __init__( + self, + left: ChildSymbol, + right: ChildSymbol, + ): """See :meth:`pybamm.BinaryOperator.__init__()`.""" super().__init__("==", left, right) @@ -496,8 +563,15 @@ def _binary_evaluate(self, left, right): else: return int(left == right) - def _binary_new_copy(self, left, right): - """See :meth:`pybamm.BinaryOperator._binary_new_copy()`.""" + def _binary_new_copy( + self, + left: ChildSymbol, + right: ChildSymbol, + ): + """ + Overwrites `pybamm.BinaryOperator._binary_new_copy()` to return a new instance of + `pybamm.Equality` rather than using `binary_evaluate` to return a value. + """ return pybamm.Equality(left, right) @@ -518,7 +592,12 @@ class _Heaviside(BinaryOperator): DISCONTINUITY event will automatically be added by the solver. """ - def __init__(self, name, left, right): + def __init__( + self, + name: str, + left: ChildSymbol, + right: ChildSymbol, + ): """See :meth:`pybamm.BinaryOperator.__init__()`.""" super().__init__(name, left, right) @@ -549,7 +628,11 @@ def _evaluate_for_shape(self): class EqualHeaviside(_Heaviside): """A heaviside function with equality (return 1 when left = right)""" - def __init__(self, left, right): + def __init__( + self, + left: ChildSymbol, + right: ChildSymbol, + ): """See :meth:`pybamm.BinaryOperator.__init__()`.""" super().__init__("<=", left, right) @@ -567,7 +650,11 @@ def _binary_evaluate(self, left, right): class NotEqualHeaviside(_Heaviside): """A heaviside function without equality (return 0 when left = right)""" - def __init__(self, left, right): + def __init__( + self, + left: ChildSymbol, + right: ChildSymbol, + ): super().__init__("<", left, right) def __str__(self): @@ -584,10 +671,14 @@ def _binary_evaluate(self, left, right): class Modulo(BinaryOperator): """Calculates the remainder of an integer division.""" - def __init__(self, left, right): + def __init__( + self, + left: ChildSymbol, + right: ChildSymbol, + ): super().__init__("%", left, right) - def _diff(self, variable): + def _diff(self, variable: pybamm.Symbol): """See :meth:`pybamm.Symbol._diff()`.""" # apply chain rule and power rule left, right = self.orphans @@ -622,14 +713,18 @@ def _binary_evaluate(self, left, right): class Minimum(BinaryOperator): """Returns the smaller of two objects.""" - def __init__(self, left, right): + def __init__( + self, + left: ChildSymbol, + right: ChildSymbol, + ): super().__init__("minimum", left, right) def __str__(self): """See :meth:`pybamm.Symbol.__str__()`.""" return f"minimum({self.left!s}, {self.right!s})" - def _diff(self, variable): + def _diff(self, variable: pybamm.Symbol): """See :meth:`pybamm.Symbol._diff()`.""" left, right = self.orphans return (left <= right) * left.diff(variable) + (left > right) * right.diff( @@ -646,27 +741,34 @@ def _binary_evaluate(self, left, right): # don't raise RuntimeWarning for NaNs return np.minimum(left, right) - def _binary_new_copy(self, left, right): + def _binary_new_copy( + self, + left: ChildSymbol, + right: ChildSymbol, + ): """See :meth:`pybamm.BinaryOperator._binary_new_copy()`.""" return pybamm.minimum(left, right) def _sympy_operator(self, left, right): """Override :meth:`pybamm.BinaryOperator._sympy_operator`""" - sympy = have_optional_dependency("sympy") return sympy.Min(left, right) class Maximum(BinaryOperator): """Returns the greater of two objects.""" - def __init__(self, left, right): + def __init__( + self, + left: ChildSymbol, + right: ChildSymbol, + ): super().__init__("maximum", left, right) def __str__(self): """See :meth:`pybamm.Symbol.__str__()`.""" return f"maximum({self.left!s}, {self.right!s})" - def _diff(self, variable): + def _diff(self, variable: pybamm.Symbol): """See :meth:`pybamm.Symbol._diff()`.""" left, right = self.orphans return (left >= right) * left.diff(variable) + (left < right) * right.diff( @@ -683,20 +785,26 @@ def _binary_evaluate(self, left, right): # don't raise RuntimeWarning for NaNs return np.maximum(left, right) - def _binary_new_copy(self, left, right): + def _binary_new_copy( + self, + left: ChildSymbol, + right: ChildSymbol, + ): """See :meth:`pybamm.BinaryOperator._binary_new_copy()`.""" return pybamm.maximum(left, right) def _sympy_operator(self, left, right): """Override :meth:`pybamm.BinaryOperator._sympy_operator`""" - sympy = have_optional_dependency("sympy") return sympy.Max(left, right) -def _simplify_elementwise_binary_broadcasts(left, right): - left, right = _preprocess_binary(left, right) +def _simplify_elementwise_binary_broadcasts( + left_child: ChildSymbol, + right_child: ChildSymbol, +) -> tuple[pybamm.Symbol, pybamm.Symbol]: + left, right = _preprocess_binary(left_child, right_child) - def unpack_broadcast_recursive(symbol): + def unpack_broadcast_recursive(symbol: pybamm.Symbol) -> pybamm.Symbol: if isinstance(symbol, pybamm.Broadcast): if symbol.child.domain == []: return symbol.orphans[0] @@ -721,16 +829,20 @@ def unpack_broadcast_recursive(symbol): return left, right -def _simplified_binary_broadcast_concatenation(left, right, operator): +def _simplified_binary_broadcast_concatenation( + left: pybamm.Symbol, + right: pybamm.Symbol, + operator: Callable, +) -> pybamm.Broadcast | None: """ Check if there are concatenations or broadcasts that we can commute the operator with """ # Broadcast commutes with elementwise operators if isinstance(left, pybamm.Broadcast) and right.domain == []: - return left._unary_new_copy(operator(left.orphans[0], right)) + return left.create_copy([operator(left.orphans[0], right)]) elif isinstance(right, pybamm.Broadcast) and left.domain == []: - return right._unary_new_copy(operator(left, right.orphans[0])) + return right.create_copy([operator(left, right.orphans[0])]) # Concatenation commutes with elementwise operators # If one of the sides is constant then commute concatenation with the operator @@ -740,13 +852,11 @@ def _simplified_binary_broadcast_concatenation(left, right, operator): left, pybamm.ConcatenationVariable ): if right.evaluates_to_constant_number(): - return left._concatenation_new_copy( - [operator(child, right) for child in left.orphans] - ) + return left.create_copy([operator(child, right) for child in left.orphans]) elif isinstance(right, pybamm.Concatenation) and not isinstance( right, pybamm.ConcatenationVariable ): - return left._concatenation_new_copy( + return left.create_copy( [ operator(left_child, right_child) for left_child, right_child in zip(left.orphans, right.orphans) @@ -756,12 +866,14 @@ def _simplified_binary_broadcast_concatenation(left, right, operator): right, pybamm.ConcatenationVariable ): if left.evaluates_to_constant_number(): - return right._concatenation_new_copy( - [operator(left, child) for child in right.orphans] - ) + return right.create_copy([operator(left, child) for child in right.orphans]) + return None -def simplified_power(left, right): +def simplified_power( + left: ChildSymbol, + right: ChildSymbol, +): left, right = _simplify_elementwise_binary_broadcasts(left, right) # Check for Concatenations and Broadcasts @@ -803,7 +915,7 @@ def simplified_power(left, right): return pybamm.simplify_if_constant(pybamm.Power(left, right)) -def add(left, right): +def add(left: ChildSymbol, right: ChildSymbol): """ Note ---- @@ -876,7 +988,7 @@ def add(left, right): if isinstance(right, (Addition, Subtraction)) and right.left.is_constant(): # Simplify a + (b +- c) to (a + b) +- c if (a + b) is constant r_left, r_right = right.orphans - return right._binary_new_copy(left + r_left, r_right) + return right.create_copy([left + r_left, r_right]) if isinstance(left, Subtraction): if right == left.right: # Simplify (a - b) + b to a @@ -891,7 +1003,10 @@ def add(left, right): return pybamm.simplify_if_constant(Addition(left, right)) -def subtract(left, right): +def subtract( + left: ChildSymbol, + right: ChildSymbol, +): """ Note ---- @@ -946,7 +1061,7 @@ def subtract(left, right): if isinstance(right, (Addition, Subtraction)) and right.left.is_constant(): # Simplify a - (b +- c) to (a - b) -+ c if (a - b) is constant r_left, r_right = right.orphans - return right._binary_new_copy(left - r_left, -r_right) + return right.create_copy([left - r_left, -r_right]) elif isinstance(left, Addition): if right == left.right: # Simplify (b + a) - a to b @@ -973,7 +1088,10 @@ def subtract(left, right): return pybamm.simplify_if_constant(Subtraction(left, right)) -def multiply(left, right): +def multiply( + left: ChildSymbol, + right: ChildSymbol, +): left, right = _simplify_elementwise_binary_broadcasts(left, right) # Move constant to always be on the left @@ -1098,7 +1216,10 @@ def multiply(left, right): return Multiplication(left, right) -def divide(left, right): +def divide( + left: ChildSymbol, + right: ChildSymbol, +): left, right = _simplify_elementwise_binary_broadcasts(left, right) # anything divided by zero raises error @@ -1169,8 +1290,11 @@ def divide(left, right): return pybamm.simplify_if_constant(Division(left, right)) -def matmul(left, right): - left, right = _preprocess_binary(left, right) +def matmul( + left_child: ChildSymbol, + right_child: ChildSymbol, +): + left, right = _preprocess_binary(left_child, right_child) if pybamm.is_matrix_zero(left) or pybamm.is_matrix_zero(right): return pybamm.zeros_like(MatrixMultiplication(left, right)) @@ -1228,16 +1352,19 @@ def matmul(left, right): return pybamm.simplify_if_constant(MatrixMultiplication(left, right)) -def minimum(left, right): +def minimum( + left: ChildSymbol, + right: ChildSymbol, +) -> pybamm.Symbol: """ Returns the smaller of two objects, possibly with a smoothing approximation. Not to be confused with :meth:`pybamm.min`, which returns min function of child. """ # Check for Concatenations and Broadcasts left, right = _simplify_elementwise_binary_broadcasts(left, right) - out = _simplified_binary_broadcast_concatenation(left, right, minimum) - if out is not None: - return out + concat_out = _simplified_binary_broadcast_concatenation(left, right, minimum) + if concat_out is not None: + return concat_out mode = pybamm.settings.min_max_mode k = pybamm.settings.min_max_smoothing @@ -1252,16 +1379,19 @@ def minimum(left, right): return pybamm.simplify_if_constant(out) -def maximum(left, right): +def maximum( + left: ChildSymbol, + right: ChildSymbol, +): """ Returns the larger of two objects, possibly with a smoothing approximation. Not to be confused with :meth:`pybamm.max`, which returns max function of child. """ # Check for Concatenations and Broadcasts left, right = _simplify_elementwise_binary_broadcasts(left, right) - out = _simplified_binary_broadcast_concatenation(left, right, maximum) - if out is not None: - return out + concat_out = _simplified_binary_broadcast_concatenation(left, right, maximum) + if concat_out is not None: + return concat_out mode = pybamm.settings.min_max_mode k = pybamm.settings.min_max_smoothing @@ -1276,15 +1406,15 @@ def maximum(left, right): return pybamm.simplify_if_constant(out) -def _heaviside(left, right, equal): +def _heaviside(left: ChildSymbol, right: ChildSymbol, equal): """return a :class:`EqualHeaviside` object, or a smooth approximation.""" # Check for Concatenations and Broadcasts left, right = _simplify_elementwise_binary_broadcasts(left, right) - out = _simplified_binary_broadcast_concatenation( + concat_out = _simplified_binary_broadcast_concatenation( left, right, functools.partial(_heaviside, equal=equal) ) - if out is not None: - return out + if concat_out is not None: + return concat_out if ( left.is_constant() @@ -1307,15 +1437,19 @@ def _heaviside(left, right, equal): # (i.e. no need for smoothing) if k == "exact" or (left.is_constant() and right.is_constant()): if equal is True: - out = pybamm.EqualHeaviside(left, right) + out: pybamm.EqualHeaviside = pybamm.EqualHeaviside(left, right) else: - out = pybamm.NotEqualHeaviside(left, right) + out: pybamm.NotEqualHeaviside = pybamm.NotEqualHeaviside(left, right) # type: ignore[no-redef] else: out = pybamm.sigmoid(left, right, k) return pybamm.simplify_if_constant(out) -def softminus(left, right, k): +def softminus( + left: pybamm.Symbol, + right: pybamm.Symbol, + k: float, +): """ Softminus approximation to the minimum function. k is the smoothing parameter, set by `pybamm.settings.min_max_smoothing`. The recommended value is k=10. @@ -1323,7 +1457,11 @@ def softminus(left, right, k): return pybamm.log(pybamm.exp(-k * left) + pybamm.exp(-k * right)) / -k -def softplus(left, right, k): +def softplus( + left: pybamm.Symbol, + right: pybamm.Symbol, + k: float, +): """ Softplus approximation to the maximum function. k is the smoothing parameter, set by `pybamm.settings.min_max_smoothing`. The recommended value is k=10. @@ -1349,7 +1487,11 @@ def smooth_max(left, right, k): return (pybamm.sqrt((left - right) ** 2 + sigma) + (left + right)) / 2 -def sigmoid(left, right, k): +def sigmoid( + left: pybamm.Symbol, + right: pybamm.Symbol, + k: float, +): """ Sigmoidal approximation to the heaviside function. k is the smoothing parameter, set by `pybamm.settings.heaviside_smoothing`. The recommended value is k=10. @@ -1359,7 +1501,11 @@ def sigmoid(left, right, k): return (1 + pybamm.tanh(k * (right - left))) / 2 -def source(left, right, boundary=False): +def source( + left: Numeric | pybamm.Symbol, + right: pybamm.Symbol, + boundary=False, +): """ A convenience function for creating (part of) an expression tree representing a source term. This is necessary for spatial methods where the mass matrix @@ -1375,7 +1521,7 @@ def source(left, right, boundary=False): Parameters ---------- - left : :class:`Symbol` + left : :class:`Symbol`, numeric The left child node, which represents the expression for the source term. right : :class:`Symbol` The right child node. This is the symbol whose boundary conditions are @@ -1390,6 +1536,9 @@ def source(left, right, boundary=False): if isinstance(left, numbers.Number): left = pybamm.PrimaryBroadcast(left, "current collector") + # force type cast for mypy + left = cast(pybamm.Symbol, left) + if left.domain != ["current collector"] or right.domain != ["current collector"]: raise pybamm.DomainError( f"""'source' only implemented in the 'current collector' domain, diff --git a/pybamm/expression_tree/broadcasts.py b/pybamm/expression_tree/broadcasts.py index d117341710..6045c3f3e8 100644 --- a/pybamm/expression_tree/broadcasts.py +++ b/pybamm/expression_tree/broadcasts.py @@ -1,12 +1,19 @@ # # Unary operator classes and methods # -import numbers +from __future__ import annotations import numpy as np from scipy.sparse import csr_matrix +from typing import cast import pybamm +from pybamm.type_definitions import ( + DomainType, + AuxiliaryDomainType, + DomainsType, + Numeric, +) class Broadcast(pybamm.SpatialOperator): @@ -29,7 +36,12 @@ class Broadcast(pybamm.SpatialOperator): name of the node """ - def __init__(self, child, domains, name=None): + def __init__( + self, + child: pybamm.Symbol, + domains: dict[str, list[str] | str], + name: str | None = None, + ): if name is None: name = "broadcast" super().__init__(name, child, domains=domains) @@ -41,7 +53,7 @@ def broadcasts_to_nodes(self): else: return False - def _sympy_operator(self, child): + def _sympy_operator(self, child: pybamm.Symbol): """Override :meth:`pybamm.UnaryOperator._sympy_operator`""" return child @@ -50,6 +62,10 @@ def _diff(self, variable): # Differentiate the child and broadcast the result in the same way return self._unary_new_copy(self.child.diff(variable)) + def reduce_one_dimension(self): # pragma: no cover + """Reduce the broadcast by one dimension.""" + raise NotImplementedError + def to_json(self): raise NotImplementedError( "pybamm.Broadcast: Serialisation is only implemented for discretised models" @@ -61,6 +77,10 @@ def _from_json(cls, snippet): "pybamm.Broadcast: Please use a discretised model when reading in from JSON" ) + def _unary_new_copy(self, child: pybamm.Symbol, perform_simplifications=True): + """See :meth:`pybamm.UnaryOperator._unary_new_copy()`.""" + return self.__class__(child, self.broadcast_domain) + class PrimaryBroadcast(Broadcast): """ @@ -73,7 +93,7 @@ class PrimaryBroadcast(Broadcast): Parameters ---------- - child : :class:`Symbol` + child : :class:`Symbol`, numeric child node broadcast_domain : iterable of str Primary domain for broadcast. This will become the domain of the symbol @@ -81,10 +101,19 @@ class PrimaryBroadcast(Broadcast): name of the node """ - def __init__(self, child, broadcast_domain, name=None): + def __init__( + self, + child: Numeric | pybamm.Symbol, + broadcast_domain: list[str] | str, + name: str | None = None, + ): # Convert child to scalar if it is a number - if isinstance(child, numbers.Number): + if isinstance(child, (float, int, np.number)): child = pybamm.Scalar(child) + + # cast child to Symbol for mypy + child = cast(pybamm.Symbol, child) + # Convert domain to list if it's a string if isinstance(broadcast_domain, str): broadcast_domain = [broadcast_domain] @@ -94,7 +123,7 @@ def __init__(self, child, broadcast_domain, name=None): self.broadcast_type = "primary to nodes" super().__init__(child, domains, name=name) - def check_and_set_domains(self, child, broadcast_domain): + def check_and_set_domains(self, child: pybamm.Symbol, broadcast_domain: list[str]): """See :meth:`Broadcast.check_and_set_domains`""" # Can only do primary broadcast from current collector to electrode, # particle-size or particle or from electrode to particle-size or particle. @@ -149,10 +178,6 @@ def check_and_set_domains(self, child, broadcast_domain): return domains - def _unary_new_copy(self, child): - """See :meth:`pybamm.UnaryOperator._unary_new_copy()`.""" - return self.__class__(child, self.broadcast_domain) - def _evaluate_for_shape(self): """ Returns a vector of NaNs to represent the shape of a Broadcast. @@ -170,7 +195,12 @@ def reduce_one_dimension(self): class PrimaryBroadcastToEdges(PrimaryBroadcast): """A primary broadcast onto the edges of the domain.""" - def __init__(self, child, broadcast_domain, name=None): + def __init__( + self, + child: Numeric | pybamm.Symbol, + broadcast_domain: list[str] | str, + name: str | None = None, + ): name = name or "broadcast to edges" super().__init__(child, broadcast_domain, name) self.broadcast_type = "primary to edges" @@ -201,7 +231,12 @@ class SecondaryBroadcast(Broadcast): name of the node """ - def __init__(self, child, broadcast_domain, name=None): + def __init__( + self, + child: pybamm.Symbol, + broadcast_domain: list[str] | str, + name: str | None = None, + ): # Convert domain to list if it's a string if isinstance(broadcast_domain, str): broadcast_domain = [broadcast_domain] @@ -211,7 +246,7 @@ def __init__(self, child, broadcast_domain, name=None): self.broadcast_type = "secondary to nodes" super().__init__(child, domains, name=name) - def check_and_set_domains(self, child, broadcast_domain): + def check_and_set_domains(self, child: pybamm.Symbol, broadcast_domain: list[str]): """See :meth:`Broadcast.check_and_set_domains`""" if child.domain == []: raise TypeError( @@ -273,10 +308,6 @@ def check_and_set_domains(self, child, broadcast_domain): return domains - def _unary_new_copy(self, child): - """See :meth:`pybamm.UnaryOperator._unary_new_copy()`.""" - return SecondaryBroadcast(child, self.broadcast_domain) - def _evaluate_for_shape(self): """ Returns a vector of NaNs to represent the shape of a Broadcast. @@ -294,7 +325,12 @@ def reduce_one_dimension(self): class SecondaryBroadcastToEdges(SecondaryBroadcast): """A secondary broadcast onto the edges of a domain.""" - def __init__(self, child, broadcast_domain, name=None): + def __init__( + self, + child: pybamm.Symbol, + broadcast_domain: list[str] | str, + name: str | None = None, + ): name = name or "broadcast to edges" super().__init__(child, broadcast_domain, name) self.broadcast_type = "secondary to edges" @@ -325,7 +361,12 @@ class TertiaryBroadcast(Broadcast): name of the node """ - def __init__(self, child, broadcast_domain, name=None): + def __init__( + self, + child: pybamm.Symbol, + broadcast_domain: list[str] | str, + name: str | None = None, + ): # Convert domain to list if it's a string if isinstance(broadcast_domain, str): broadcast_domain = [broadcast_domain] @@ -335,7 +376,9 @@ def __init__(self, child, broadcast_domain, name=None): self.broadcast_type = "tertiary to nodes" super().__init__(child, domains, name=name) - def check_and_set_domains(self, child, broadcast_domain): + def check_and_set_domains( + self, child: pybamm.Symbol, broadcast_domain: list[str] | str + ): """See :meth:`Broadcast.check_and_set_domains`""" if child.domains["secondary"] == []: raise TypeError( @@ -382,10 +425,6 @@ def check_and_set_domains(self, child, broadcast_domain): return domains - def _unary_new_copy(self, child): - """See :meth:`pybamm.UnaryOperator._unary_new_copy()`.""" - return self.__class__(child, self.broadcast_domain) - def _evaluate_for_shape(self): """ Returns a vector of NaNs to represent the shape of a Broadcast. @@ -403,7 +442,12 @@ def reduce_one_dimension(self): class TertiaryBroadcastToEdges(TertiaryBroadcast): """A tertiary broadcast onto the edges of a domain.""" - def __init__(self, child, broadcast_domain, name=None): + def __init__( + self, + child: pybamm.Symbol, + broadcast_domain: list[str] | str, + name: str | None = None, + ): name = name or "broadcast to edges" super().__init__(child, broadcast_domain, name) self.broadcast_type = "tertiary to edges" @@ -417,15 +461,17 @@ class FullBroadcast(Broadcast): def __init__( self, - child, - broadcast_domain=None, - auxiliary_domains=None, - broadcast_domains=None, - name=None, + child_input: Numeric | pybamm.Symbol, + broadcast_domain: DomainType = None, + auxiliary_domains: AuxiliaryDomainType = None, + broadcast_domains: DomainsType = None, + name: str | None = None, ): # Convert child to scalar if it is a number - if isinstance(child, numbers.Number): - child = pybamm.Scalar(child) + if isinstance(child_input, (float, int, np.number)): + child: pybamm.Scalar = pybamm.Scalar(child_input) + else: + child: pybamm.Symbol = child_input # type: ignore[no-redef] if isinstance(auxiliary_domains, str): auxiliary_domains = {"secondary": auxiliary_domains} @@ -438,7 +484,7 @@ def __init__( self.broadcast_type = "full to nodes" super().__init__(child, domains, name=name) - def check_and_set_domains(self, child, broadcast_domains): + def check_and_set_domains(self, child: pybamm.Symbol, broadcast_domains: dict): """See :meth:`Broadcast.check_and_set_domains`""" if broadcast_domains["primary"] == []: raise pybamm.DomainError( @@ -452,7 +498,7 @@ def check_and_set_domains(self, child, broadcast_domains): return broadcast_domains - def _unary_new_copy(self, child): + def _unary_new_copy(self, child, perform_simplifications=True): """See :meth:`pybamm.UnaryOperator._unary_new_copy()`.""" return self.__class__(child, broadcast_domains=self.domains) @@ -489,11 +535,11 @@ class FullBroadcastToEdges(FullBroadcast): def __init__( self, - child, - broadcast_domain=None, - auxiliary_domains=None, - broadcast_domains=None, - name=None, + child: Numeric | pybamm.Symbol, + broadcast_domain: DomainType = None, + auxiliary_domains: AuxiliaryDomainType = None, + broadcast_domains: DomainsType = None, + name: str | None = None, ): name = name or "broadcast to edges" super().__init__( @@ -520,7 +566,7 @@ def reduce_one_dimension(self): ) -def full_like(symbols, fill_value): +def full_like(symbols: tuple[pybamm.Symbol, ...], fill_value: float) -> pybamm.Symbol: """ Returns an array with the same shape and domains as the sum of the input symbols, with a constant value given by `fill_value`. @@ -543,18 +589,18 @@ def full_like(symbols, fill_value): return pybamm.Scalar(fill_value) try: shape = sum_symbol.shape - # use vector or matrix - if shape[1] == 1: - array_type = pybamm.Vector - else: - array_type = pybamm.Matrix + # return dense array, except for a matrix of zeros if shape[1] != 1 and fill_value == 0: entries = csr_matrix(shape) else: entries = fill_value * np.ones(shape) - return array_type(entries, domains=sum_symbol.domains) + # use vector or matrix + if shape[1] == 1: + return pybamm.Vector(entries, domains=sum_symbol.domains) + else: + return pybamm.Matrix(entries, domains=sum_symbol.domains) except NotImplementedError: if ( @@ -571,7 +617,7 @@ def full_like(symbols, fill_value): return FullBroadcast(fill_value, broadcast_domains=sum_symbol.domains) -def zeros_like(*symbols): +def zeros_like(*symbols: pybamm.Symbol): """ Returns an array with the same shape and domains as the sum of the input symbols, with each entry equal to zero. @@ -584,7 +630,7 @@ def zeros_like(*symbols): return full_like(symbols, 0) -def ones_like(*symbols): +def ones_like(*symbols: pybamm.Symbol): """ Returns an array with the same shape and domains as the sum of the input symbols, with each entry equal to one. diff --git a/pybamm/expression_tree/concatenations.py b/pybamm/expression_tree/concatenations.py index 40cfe617ac..a1905f9080 100644 --- a/pybamm/expression_tree/concatenations.py +++ b/pybamm/expression_tree/concatenations.py @@ -1,14 +1,16 @@ # # Concatenation classes # +from __future__ import annotations import copy from collections import defaultdict import numpy as np +import sympy from scipy.sparse import issparse, vstack +from collections.abc import Sequence import pybamm -from pybamm.util import have_optional_dependency class Concatenation(pybamm.Symbol): @@ -21,7 +23,13 @@ class Concatenation(pybamm.Symbol): The symbols to concatenate """ - def __init__(self, *children, name=None, check_domain=True, concat_fun=None): + def __init__( + self, + *children: pybamm.Symbol, + name: str | None = None, + check_domain=True, + concat_fun=None, + ): # The second condition checks whether this is the base Concatenation class # or a subclass of Concatenation # (ConcatenationVariable, NumpyConcatenation, ...) @@ -44,13 +52,15 @@ def __init__(self, *children, name=None, check_domain=True, concat_fun=None): super().__init__(name, children, domains=domains) @classmethod - def _from_json(cls, *children, name, domains, concat_fun=None): + def _from_json(cls, snippet: dict): """Creates a new Concatenation instance from a json object""" instance = cls.__new__(cls) - instance.concatenation_function = concat_fun + instance.concatenation_function = snippet["concat_fun"] - super(Concatenation, instance).__init__(name, children, domains=domains) + super(Concatenation, instance).__init__( + snippet["name"], tuple(snippet["children"]), domains=snippet["domains"] + ) return instance @@ -62,7 +72,7 @@ def __str__(self): out = out[:-2] + ")" return out - def _diff(self, variable): + def _diff(self, variable: pybamm.Symbol): """See :meth:`pybamm.Symbol._diff()`.""" children_diffs = [child.diff(variable) for child in self.children] if len(children_diffs) == 1: @@ -72,9 +82,9 @@ def _diff(self, variable): return diff - def get_children_domains(self, children): + def get_children_domains(self, children: Sequence[pybamm.Symbol]): # combine domains from children - domain = [] + domain: list = [] for child in children: if not isinstance(child, pybamm.Symbol): raise TypeError(f"{child} is not a pybamm symbol") @@ -101,29 +111,44 @@ def get_children_domains(self, children): return domains - def _concatenation_evaluate(self, children_eval): + def _concatenation_evaluate(self, children_eval: list[np.ndarray]): """See :meth:`Concatenation._concatenation_evaluate()`.""" if len(children_eval) == 0: return np.array([]) else: return self.concatenation_function(children_eval) - def evaluate(self, t=None, y=None, y_dot=None, inputs=None): + def evaluate( + self, + t: float | None = None, + y: np.ndarray | None = None, + y_dot: np.ndarray | None = None, + inputs: dict | str | None = None, + ): """See :meth:`pybamm.Symbol.evaluate()`.""" - children = self.children - children_eval = [None] * len(children) - for idx, child in enumerate(children): - children_eval[idx] = child.evaluate(t, y, y_dot, inputs) + children_eval = [child.evaluate(t, y, y_dot, inputs) for child in self.children] return self._concatenation_evaluate(children_eval) - def create_copy(self): + def create_copy( + self, + new_children: list[pybamm.Symbol] | None = None, + perform_simplifications: bool = True, + ): """See :meth:`pybamm.Symbol.new_copy()`.""" - new_children = [child.new_copy() for child in self.children] - return self._concatenation_new_copy(new_children) + children = self._children_for_copying(new_children) - def _concatenation_new_copy(self, children): - """See :meth:`pybamm.Symbol.new_copy()`.""" - return concatenation(*children) + return self._concatenation_new_copy(children, perform_simplifications) + + def _concatenation_new_copy(self, children, perform_simplifications: bool = True): + """ + Creates a copy for the current concatenation class using the convenience + function :meth:`concatenation` to perform simplifications based on the new + children before creating the new copy. + """ + if perform_simplifications: + return concatenation(*children) + else: + return self.__class__(*children) def _concatenation_jac(self, children_jacs): """Calculate the Jacobian of a concatenation.""" @@ -146,7 +171,6 @@ def is_constant(self): def _sympy_operator(self, *children): """Apply appropriate SymPy operators.""" - sympy = have_optional_dependency("sympy") self.concat_latex = tuple(map(sympy.latex, children)) if self.print_name is not None: @@ -180,7 +204,7 @@ class NumpyConcatenation(Concatenation): The equations to concatenate """ - def __init__(self, *children): + def __init__(self, *children: pybamm.Symbol): children = list(children) # Turn objects that evaluate to scalars to objects that evaluate to vectors, # so that we can concatenate them @@ -197,12 +221,11 @@ def __init__(self, *children): @classmethod def _from_json(cls, snippet: dict): """See :meth:`pybamm.Concatenation._from_json()`.""" - instance = super()._from_json( - *snippet["children"], - name="numpy_concatenation", - domains=snippet["domains"], - concat_fun=np.concatenate, - ) + + snippet["name"] = "numpy_concatenation" + snippet["concat_fun"] = np.concatenate + + instance = super()._from_json(snippet) return instance @@ -214,9 +237,19 @@ def _concatenation_jac(self, children_jacs): else: return SparseStack(*children_jacs) - def _concatenation_new_copy(self, children): - """See :meth:`pybamm.Symbol.new_copy()`.""" - return numpy_concatenation(*children) + def _concatenation_new_copy( + self, + children, + perform_simplifications: bool = True, + ): + """See :meth:`pybamm.Concatenation._concatenation_new_copy()`.""" + if perform_simplifications: + return numpy_concatenation(*children) + else: + raise NotImplementedError( + f"{self.__class__.__name__} should always be copied using " + "simplification checks" + ) class DomainConcatenation(Concatenation): @@ -233,7 +266,7 @@ class DomainConcatenation(Concatenation): children : iterable of :class:`pybamm.Symbol` The symbols to concatenate - full_mesh : :class:`pybamm.BaseMesh` + full_mesh : :class:`pybamm.Mesh` The underlying mesh for discretisation, used to obtain the number of mesh points in each domain. @@ -242,7 +275,12 @@ class DomainConcatenation(Concatenation): from `copy_this`. `mesh` is not used in this case """ - def __init__(self, children, full_mesh, copy_this=None): + def __init__( + self, + children: Sequence[pybamm.Symbol], + full_mesh: pybamm.Mesh, + copy_this: pybamm.DomainConcatenation | None = None, + ): # Convert any constant symbols in children to a Vector of the right size for # concatenation children = list(children) @@ -277,11 +315,11 @@ def __init__(self, children, full_mesh, copy_this=None): @classmethod def _from_json(cls, snippet: dict): """See :meth:`pybamm.Concatenation._from_json()`.""" - instance = super()._from_json( - *snippet["children"], - name="domain_concatenation", - domains=snippet["domains"], - ) + + snippet["name"] = "domain_concatenation" + snippet["concat_fun"] = None + + instance = super()._from_json(snippet) def repack_defaultDict(slices): slices = defaultdict(list, slices) @@ -299,7 +337,7 @@ def repack_defaultDict(slices): return instance - def _get_auxiliary_domain_repeats(self, auxiliary_domains): + def _get_auxiliary_domain_repeats(self, auxiliary_domains: dict) -> int: """Helper method to read the 'auxiliary_domain' meshes.""" mesh_pts = 1 for level, dom in auxiliary_domains.items(): @@ -311,7 +349,7 @@ def _get_auxiliary_domain_repeats(self, auxiliary_domains): def full_mesh(self): return self._full_mesh - def create_slices(self, node): + def create_slices(self, node: pybamm.Symbol) -> defaultdict: slices = defaultdict(list) start = 0 end = 0 @@ -321,14 +359,14 @@ def create_slices(self, node): """Concatenation and children must have the same number of points in secondary dimensions""" ) - for i in range(second_pts): + for _ in range(second_pts): for dom in node.domain: end += self.full_mesh[dom].npts slices[dom].append(slice(start, end)) start = end return slices - def _concatenation_evaluate(self, children_eval): + def _concatenation_evaluate(self, children_eval: list[np.ndarray]): """See :meth:`Concatenation._concatenation_evaluate()`.""" # preallocate vector vector = np.empty((self._size, 1)) @@ -357,12 +395,16 @@ def _concatenation_jac(self, children_jacs): jacs.append(pybamm.Index(child_jac, child_slice[i])) return SparseStack(*jacs) - def _concatenation_new_copy(self, children): - """See :meth:`pybamm.Symbol.new_copy()`.""" - new_symbol = simplified_domain_concatenation( - children, self.full_mesh, copy_this=self - ) - return new_symbol + def _concatenation_new_copy( + self, children: list[pybamm.Symbol], perform_simplifications: bool = True + ): + """See :meth:`pybamm.Concatenation._concatenation_new_copy()`.""" + if perform_simplifications: + return simplified_domain_concatenation( + children, self.full_mesh, copy_this=self + ) + else: + return DomainConcatenation(children, self.full_mesh, copy_this=self) def to_json(self): """ @@ -418,8 +460,8 @@ def __init__(self, *children): concat_fun=concatenation_function, ) - def _concatenation_new_copy(self, children): - """See :meth:`pybamm.Symbol.new_copy()`.""" + def _concatenation_new_copy(self, children, perform_simplifications=True): + """See :meth:`pybamm.Concatenation._concatenation_new_copy()`.""" return SparseStack(*children) @@ -464,13 +506,13 @@ def __init__(self, *children): self.print_name = print_name -def substrings(s): +def substrings(s: str): for i in range(len(s)): for j in range(i, len(s)): yield s[i : j + 1] -def intersect(s1, s2): +def intersect(s1: str, s2: str): # find all the common strings between two strings all_intersects = set(substrings(s1)) & set(substrings(s2)) # intersect is the longest such intercept @@ -536,24 +578,29 @@ def numpy_concatenation(*children): return simplified_numpy_concatenation(*children) -def simplified_domain_concatenation(children, mesh, copy_this=None): +def simplified_domain_concatenation( + children: list[pybamm.Symbol], + mesh: pybamm.Mesh, + copy_this: DomainConcatenation | None = None, +): """Perform simplifications on a domain concatenation.""" # Create the DomainConcatenation to read domain and child domain concat = DomainConcatenation(children, mesh, copy_this=copy_this) # Simplify Concatenation of StateVectors to a single StateVector # The sum of the evalation arrays of the StateVectors must be exactly 1 if all(isinstance(child, pybamm.StateVector) for child in children): - longest_eval_array = len(children[-1]._evaluation_array) + sv_children: list[pybamm.StateVector] = children # type: ignore[assignment] + longest_eval_array = len(sv_children[-1]._evaluation_array) eval_arrays = {} - for child in children: + for child in sv_children: eval_arrays[child] = np.concatenate( [ child.evaluation_array, np.zeros(longest_eval_array - len(child.evaluation_array)), ] ) - first_start = children[0].y_slices[0].start - last_stop = children[-1].y_slices[-1].stop + first_start = sv_children[0].y_slices[0].start + last_stop = sv_children[-1].y_slices[-1].stop if all( sum(array for array in eval_arrays.values())[first_start:last_stop] == 1 ): @@ -564,7 +611,7 @@ def simplified_domain_concatenation(children, mesh, copy_this=None): return pybamm.simplify_if_constant(concat) -def domain_concatenation(children, mesh): +def domain_concatenation(children: list[pybamm.Symbol], mesh: pybamm.Mesh): """Helper function to create domain concatenations.""" # TODO: add option to turn off simplifications return simplified_domain_concatenation(children, mesh) diff --git a/pybamm/expression_tree/functions.py b/pybamm/expression_tree/functions.py index f95f190b43..4e087e9725 100644 --- a/pybamm/expression_tree/functions.py +++ b/pybamm/expression_tree/functions.py @@ -1,14 +1,16 @@ # # Function classes and methods # -import numbers +from __future__ import annotations import numpy as np from scipy import special +import sympy from typing import Callable +from collections.abc import Sequence +from typing_extensions import TypeVar import pybamm -from pybamm.util import have_optional_dependency class Function(pybamm.Symbol): @@ -23,25 +25,21 @@ class Function(pybamm.Symbol): func(child0.evaluate(t, y, u), child1.evaluate(t, y, u), etc). children : :class:`pybamm.Symbol` The children nodes to apply the function to - derivative : str, optional - Which derivative to use when differentiating ("autograd" or "derivative"). - Default is "autograd". differentiated_function : method, optional The function which was differentiated to obtain this one. Default is None. """ def __init__( self, - function, - *children, - name=None, - derivative="autograd", - differentiated_function=None, + function: Callable, + *children: pybamm.Symbol, + name: str | None = None, + differentiated_function: Callable | None = None, ): # Turn numbers into scalars children = list(children) for idx, child in enumerate(children): - if isinstance(child, numbers.Number): + if isinstance(child, (float, int, np.number)): children[idx] = pybamm.Scalar(child) if name is not None: @@ -54,7 +52,6 @@ def __init__( domains = self.get_children_domains(children) self.function = function - self.derivative = derivative self.differentiated_function = differentiated_function super().__init__(name, children=children, domains=domains) @@ -67,13 +64,13 @@ def __str__(self): out = out[:-2] + ")" return out - def diff(self, variable): + def diff(self, variable: pybamm.Symbol): """See :meth:`pybamm.Symbol.diff()`.""" if variable == self: return pybamm.Scalar(1) else: children = self.orphans - partial_derivatives = [None] * len(children) + partial_derivatives: list[None | pybamm.Symbol] = [None] * len(children) for i, child in enumerate(self.children): # if variable appears in the function, differentiate # function, and apply chain rule @@ -87,39 +84,19 @@ def diff(self, variable): derivative = sum(partial_derivatives) if derivative == 0: - derivative = pybamm.Scalar(0) + return pybamm.Scalar(0) return derivative - def _function_diff(self, children, idx): + def _function_diff(self, children: Sequence[pybamm.Symbol], idx: float): """ Derivative with respect to child number 'idx'. See :meth:`pybamm.Symbol._diff()`. """ - autograd = have_optional_dependency("autograd") - # Store differentiated function, needed in case we want to convert to CasADi - if self.derivative == "autograd": - return Function( - autograd.elementwise_grad(self.function, idx), - *children, - differentiated_function=self.function, - ) - elif self.derivative == "derivative": - if len(children) > 1: - raise ValueError( - """ - differentiation using '.derivative()' not implemented for functions - with more than one child - """ - ) - else: - # keep using "derivative" as derivative - return pybamm.Function( - self.function.derivative(), - *children, - derivative="derivative", - differentiated_function=self.function, - ) + raise NotImplementedError( + "Derivative of base Function class is not implemented. " + "Please implement in child class." + ) def _function_jac(self, children_jacs): """Calculate the Jacobian of a function.""" @@ -142,14 +119,20 @@ def _function_jac(self, children_jacs): return jacobian - def evaluate(self, t=None, y=None, y_dot=None, inputs=None): + def evaluate( + self, + t: float | None = None, + y: np.ndarray | None = None, + y_dot: np.ndarray | None = None, + inputs: dict | str | None = None, + ): """See :meth:`pybamm.Symbol.evaluate()`.""" evaluated_children = [ child.evaluate(t, y, y_dot, inputs) for child in self.children ] return self._function_evaluate(evaluated_children) - def _evaluates_on_edges(self, dimension): + def _evaluates_on_edges(self, dimension: str) -> bool: """See :meth:`pybamm.Symbol._evaluates_on_edges()`.""" return any(child.evaluates_on_edges(dimension) for child in self.children) @@ -168,12 +151,27 @@ def _evaluate_for_shape(self): def _function_evaluate(self, evaluated_children): return self.function(*evaluated_children) - def create_copy(self): + def create_copy( + self, + new_children: list[pybamm.Symbol] | None = None, + perform_simplifications: bool = True, + ): """See :meth:`pybamm.Symbol.new_copy()`.""" - children_copy = [child.new_copy() for child in self.children] - return self._function_new_copy(children_copy) + children = self._children_for_copying(new_children) - def _function_new_copy(self, children): + if not perform_simplifications: + return pybamm.Function( + self.function, + *children, + name=self.name, + differentiated_function=self.differentiated_function, + ) + else: + # performs additional simplifications, rather than just calling the + # constructor + return self._function_new_copy(children) + + def _function_new_copy(self, children: list) -> Function: """ Returns a new copy of the function. @@ -192,7 +190,6 @@ def _function_new_copy(self, children): self.function, *children, name=self.name, - derivative=self.derivative, differentiated_function=self.differentiated_function, ) ) @@ -203,7 +200,6 @@ def _sympy_operator(self, child): def to_equation(self): """Convert the node and its subtree into a SymPy equation.""" - sympy = have_optional_dependency("sympy") if self.print_name is not None: return sympy.Symbol(self.print_name) else: @@ -225,22 +221,6 @@ def _from_json(cls, snippet): ) -def simplified_function(func_class, child): - """ - Simplifications implemented before applying the function. - Currently only implemented for one-child functions. - """ - if isinstance(child, pybamm.Broadcast): - # Move the function inside the broadcast - # Apply recursively - func_child_not_broad = pybamm.simplify_if_constant( - simplified_function(func_class, child.orphans[0]) - ) - return child._unary_new_copy(func_child_not_broad) - else: - return pybamm.simplify_if_constant(func_class(child)) - - class SpecificFunction(Function): """ Parent class for the specific functions, which implement their own `diff` @@ -254,11 +234,11 @@ class SpecificFunction(Function): The child to apply the function to """ - def __init__(self, function, child): + def __init__(self, function: Callable, child: pybamm.Symbol): super().__init__(function, child) @classmethod - def _from_json(cls, function: Callable, snippet: dict): + def _from_json(cls, snippet: dict): """ Reconstructs a SpecificFunction instance during deserialisation of a JSON file. @@ -272,7 +252,9 @@ def _from_json(cls, function: Callable, snippet: dict): instance = cls.__new__(cls) - super(SpecificFunction, instance).__init__(function, snippet["children"][0]) + super(SpecificFunction, instance).__init__( + snippet["function"], snippet["children"][0] + ) return instance @@ -282,7 +264,6 @@ def _function_new_copy(self, children): def _sympy_operator(self, child): """Apply appropriate SymPy operators.""" - sympy = have_optional_dependency("sympy") class_name = self.__class__.__name__.lower() sympy_function = getattr(sympy, class_name) return sympy_function(child) @@ -301,6 +282,25 @@ def to_json(self): return json_dict +SF = TypeVar("SF", bound=SpecificFunction) + + +def simplified_function(func_class: type[SF], child: pybamm.Symbol): + """ + Simplifications implemented before applying the function. + Currently only implemented for one-child functions. + """ + if isinstance(child, pybamm.Broadcast): + # Move the function inside the broadcast + # Apply recursively + func_child_not_broad = pybamm.simplify_if_constant( + simplified_function(func_class, child.orphans[0]) + ) + return child._unary_new_copy(func_child_not_broad) + else: + return pybamm.simplify_if_constant(func_class(child)) # type: ignore[call-arg, arg-type] + + class Arcsinh(SpecificFunction): """Arcsinh function.""" @@ -310,7 +310,8 @@ def __init__(self, child): @classmethod def _from_json(cls, snippet: dict): """See :meth:`pybamm.SpecificFunction._from_json()`.""" - instance = super()._from_json(np.arcsinh, snippet) + snippet["function"] = np.arcsinh + instance = super()._from_json(snippet) return instance def _function_diff(self, children, idx): @@ -319,11 +320,10 @@ def _function_diff(self, children, idx): def _sympy_operator(self, child): """Override :meth:`pybamm.Function._sympy_operator`""" - sympy = have_optional_dependency("sympy") return sympy.asinh(child) -def arcsinh(child): +def arcsinh(child: pybamm.Symbol): """Returns arcsinh function of child.""" return simplified_function(Arcsinh, child) @@ -337,7 +337,8 @@ def __init__(self, child): @classmethod def _from_json(cls, snippet: dict): """See :meth:`pybamm.SpecificFunction._from_json()`.""" - instance = super()._from_json(np.arctan, snippet) + snippet["function"] = np.arctan + instance = super()._from_json(snippet) return instance def _function_diff(self, children, idx): @@ -346,11 +347,10 @@ def _function_diff(self, children, idx): def _sympy_operator(self, child): """Override :meth:`pybamm.Function._sympy_operator`""" - sympy = have_optional_dependency("sympy") return sympy.atan(child) -def arctan(child): +def arctan(child: pybamm.Symbol): """Returns hyperbolic tan function of child.""" return simplified_function(Arctan, child) @@ -364,7 +364,8 @@ def __init__(self, child): @classmethod def _from_json(cls, snippet: dict): """See :meth:`pybamm.SpecificFunction._from_json()`.""" - instance = super()._from_json(np.cos, snippet) + snippet["function"] = np.cos + instance = super()._from_json(snippet) return instance def _function_diff(self, children, idx): @@ -372,7 +373,7 @@ def _function_diff(self, children, idx): return -sin(children[0]) -def cos(child): +def cos(child: pybamm.Symbol): """Returns cosine function of child.""" return simplified_function(Cos, child) @@ -386,7 +387,8 @@ def __init__(self, child): @classmethod def _from_json(cls, snippet: dict): """See :meth:`pybamm.SpecificFunction._from_json()`.""" - instance = super()._from_json(np.cosh, snippet) + snippet["function"] = np.cosh + instance = super()._from_json(snippet) return instance def _function_diff(self, children, idx): @@ -394,7 +396,7 @@ def _function_diff(self, children, idx): return sinh(children[0]) -def cosh(child): +def cosh(child: pybamm.Symbol): """Returns hyperbolic cosine function of child.""" return simplified_function(Cosh, child) @@ -408,7 +410,8 @@ def __init__(self, child): @classmethod def _from_json(cls, snippet: dict): """See :meth:`pybamm.SpecificFunction._from_json()`.""" - instance = super()._from_json(special.erf, snippet) + snippet["function"] = special.erf + instance = super()._from_json(snippet) return instance def _function_diff(self, children, idx): @@ -416,12 +419,12 @@ def _function_diff(self, children, idx): return 2 / np.sqrt(np.pi) * exp(-(children[0] ** 2)) -def erf(child): +def erf(child: pybamm.Symbol): """Returns error function of child.""" return simplified_function(Erf, child) -def erfc(child): +def erfc(child: pybamm.Symbol): """Returns complementary error function of child.""" return 1 - simplified_function(Erf, child) @@ -435,7 +438,8 @@ def __init__(self, child): @classmethod def _from_json(cls, snippet: dict): """See :meth:`pybamm.SpecificFunction._from_json()`.""" - instance = super()._from_json(np.exp, snippet) + snippet["function"] = np.exp + instance = super()._from_json(snippet) return instance def _function_diff(self, children, idx): @@ -443,7 +447,7 @@ def _function_diff(self, children, idx): return exp(children[0]) -def exp(child): +def exp(child: pybamm.Symbol): """Returns exponential function of child.""" return simplified_function(Exp, child) @@ -457,7 +461,8 @@ def __init__(self, child): @classmethod def _from_json(cls, snippet: dict): """See :meth:`pybamm.SpecificFunction._from_json()`.""" - instance = super()._from_json(np.log, snippet) + snippet["function"] = np.log + instance = super()._from_json(snippet) return instance def _function_evaluate(self, evaluated_children): @@ -479,7 +484,7 @@ def log(child, base="e"): return log_child / np.log(base) -def log10(child): +def log10(child: pybamm.Symbol): """Returns logarithmic function of child, with base 10.""" return log(child, base=10) @@ -493,7 +498,8 @@ def __init__(self, child): @classmethod def _from_json(cls, snippet: dict): """See :meth:`pybamm.SpecificFunction._from_json()`.""" - instance = super()._from_json(np.max, snippet) + snippet["function"] = np.max + instance = super()._from_json(snippet) return instance def _evaluate_for_shape(self): @@ -502,7 +508,7 @@ def _evaluate_for_shape(self): return np.nan * np.ones((1, 1)) -def max(child): +def max(child: pybamm.Symbol): """ Returns max function of child. Not to be confused with :meth:`pybamm.maximum`, which returns the larger of two objects. @@ -519,7 +525,8 @@ def __init__(self, child): @classmethod def _from_json(cls, snippet: dict): """See :meth:`pybamm.SpecificFunction._from_json()`.""" - instance = super()._from_json(np.min, snippet) + snippet["function"] = np.min + instance = super()._from_json(snippet) return instance def _evaluate_for_shape(self): @@ -528,7 +535,7 @@ def _evaluate_for_shape(self): return np.nan * np.ones((1, 1)) -def min(child): +def min(child: pybamm.Symbol): """ Returns min function of child. Not to be confused with :meth:`pybamm.minimum`, which returns the smaller of two objects. @@ -536,7 +543,7 @@ def min(child): return pybamm.simplify_if_constant(Min(child)) -def sech(child): +def sech(child: pybamm.Symbol): """Returns hyperbolic sec function of child.""" return 1 / simplified_function(Cosh, child) @@ -550,7 +557,8 @@ def __init__(self, child): @classmethod def _from_json(cls, snippet: dict): """See :meth:`pybamm.SpecificFunction._from_json()`.""" - instance = super()._from_json(np.sin, snippet) + snippet["function"] = np.sin + instance = super()._from_json(snippet) return instance def _function_diff(self, children, idx): @@ -558,7 +566,7 @@ def _function_diff(self, children, idx): return cos(children[0]) -def sin(child): +def sin(child: pybamm.Symbol): """Returns sine function of child.""" return simplified_function(Sin, child) @@ -572,7 +580,8 @@ def __init__(self, child): @classmethod def _from_json(cls, snippet: dict): """See :meth:`pybamm.SpecificFunction._from_json()`.""" - instance = super()._from_json(np.sinh, snippet) + snippet["function"] = np.sinh + instance = super()._from_json(snippet) return instance def _function_diff(self, children, idx): @@ -580,7 +589,7 @@ def _function_diff(self, children, idx): return cosh(children[0]) -def sinh(child): +def sinh(child: pybamm.Symbol): """Returns hyperbolic sine function of child.""" return simplified_function(Sinh, child) @@ -594,7 +603,8 @@ def __init__(self, child): @classmethod def _from_json(cls, snippet: dict): """See :meth:`pybamm.SpecificFunction._from_json()`.""" - instance = super()._from_json(np.sqrt, snippet) + snippet["function"] = np.sqrt + instance = super()._from_json(snippet) return instance def _function_evaluate(self, evaluated_children): @@ -607,7 +617,7 @@ def _function_diff(self, children, idx): return 1 / (2 * sqrt(children[0])) -def sqrt(child): +def sqrt(child: pybamm.Symbol): """Returns square root function of child.""" return simplified_function(Sqrt, child) @@ -621,7 +631,8 @@ def __init__(self, child): @classmethod def _from_json(cls, snippet: dict): """See :meth:`pybamm.SpecificFunction._from_json()`.""" - instance = super()._from_json(np.tanh, snippet) + snippet["function"] = np.tanh + instance = super()._from_json(snippet) return instance def _function_diff(self, children, idx): @@ -629,6 +640,52 @@ def _function_diff(self, children, idx): return sech(children[0]) ** 2 -def tanh(child): +def tanh(child: pybamm.Symbol): """Returns hyperbolic tan function of child.""" return simplified_function(Tanh, child) + + +def normal_pdf( + x: pybamm.Symbol, mu: pybamm.Symbol | float, sigma: pybamm.Symbol | float +): + """ + Returns the normal probability density function at x. + + Parameters + ---------- + x : pybamm.Symbol + The value at which to evaluate the normal distribution + mu : pybamm.Symbol or float + The mean of the normal distribution + sigma : pybamm.Symbol or float + The standard deviation of the normal distribution + + Returns + ------- + pybamm.Symbol + The value of the normal distribution at x + """ + return 1 / (np.sqrt(2 * np.pi) * sigma) * np.exp(-0.5 * ((x - mu) / sigma) ** 2) + + +def normal_cdf( + x: pybamm.Symbol, mu: pybamm.Symbol | float, sigma: pybamm.Symbol | float +): + """ + Returns the normal cumulative distribution function at x. + + Parameters + ---------- + x : pybamm.Symbol + The value at which to evaluate the normal distribution + mu : pybamm.Symbol or float + The mean of the normal distribution + sigma : pybamm.Symbol or float + The standard deviation of the normal distribution + + Returns + ------- + pybamm.Symbol + The value of the normal distribution at x + """ + return 0.5 * (1 + special.erf((x - mu) / (sigma * np.sqrt(2)))) diff --git a/pybamm/expression_tree/independent_variable.py b/pybamm/expression_tree/independent_variable.py index dccb627eed..015b6ae49d 100644 --- a/pybamm/expression_tree/independent_variable.py +++ b/pybamm/expression_tree/independent_variable.py @@ -1,8 +1,12 @@ # # IndependentVariable class # +from __future__ import annotations +import sympy +import numpy as np + import pybamm -from pybamm.util import have_optional_dependency +from pybamm.type_definitions import DomainType, AuxiliaryDomainType, DomainsType KNOWN_COORD_SYS = ["cartesian", "cylindrical polar", "spherical polar"] @@ -28,35 +32,44 @@ class IndependentVariable(pybamm.Symbol): deprecated. """ - def __init__(self, name, domain=None, auxiliary_domains=None, domains=None): + def __init__( + self, + name: str, + domain: DomainType = None, + auxiliary_domains: AuxiliaryDomainType = None, + domains: DomainsType = None, + ) -> None: super().__init__( name, domain=domain, auxiliary_domains=auxiliary_domains, domains=domains ) @classmethod def _from_json(cls, snippet: dict): - instance = cls.__new__(cls) - - instance.__init__(snippet["name"], domains=snippet["domains"]) - - return instance + return cls(snippet["name"], domains=snippet["domains"]) def _evaluate_for_shape(self): """See :meth:`pybamm.Symbol.evaluate_for_shape_using_domain()`""" return pybamm.evaluate_for_shape_using_domain(self.domains) - def _jac(self, variable): + def _jac(self, variable) -> pybamm.Scalar: """See :meth:`pybamm.Symbol._jac()`.""" return pybamm.Scalar(0) - def to_equation(self): + def to_equation(self) -> sympy.Symbol: """Convert the node and its subtree into a SymPy equation.""" - sympy = have_optional_dependency("sympy") if self.print_name is not None: return sympy.Symbol(self.print_name) else: return sympy.Symbol(self.name) + def create_copy( + self, + new_children=None, + perform_simplifications=True, + ): + """See :meth:`pybamm.Symbol.new_copy()`.""" + return self.__class__(self.name, domains=self.domains) + class Time(IndependentVariable): """ @@ -68,17 +81,23 @@ def __init__(self): @classmethod def _from_json(cls, snippet: dict): - instance = cls.__new__(cls) + return cls() - instance.__init__() - - return instance - - def create_copy(self): + def create_copy( + self, + new_children=None, + perform_simplifications=True, + ): """See :meth:`pybamm.Symbol.new_copy()`.""" return Time() - def _base_evaluate(self, t=None, y=None, y_dot=None, inputs=None): + def _base_evaluate( + self, + t: float | None = None, + y: np.ndarray | None = None, + y_dot: np.ndarray | None = None, + inputs: dict | str | None = None, + ): """See :meth:`pybamm.Symbol._base_evaluate()`.""" if t is None: raise ValueError("t must be provided") @@ -93,7 +112,6 @@ def _evaluate_for_shape(self): def to_equation(self): """Convert the node and its subtree into a SymPy equation.""" - sympy = have_optional_dependency("sympy") return sympy.Symbol("t") @@ -118,8 +136,13 @@ class SpatialVariable(IndependentVariable): """ def __init__( - self, name, domain=None, auxiliary_domains=None, domains=None, coord_sys=None - ): + self, + name: str, + domain: DomainType = None, + auxiliary_domains: AuxiliaryDomainType = None, + domains: DomainsType = None, + coord_sys=None, + ) -> None: self.coord_sys = coord_sys super().__init__( name, domain=domain, auxiliary_domains=auxiliary_domains, domains=domains @@ -147,7 +170,11 @@ def __init__( ): raise pybamm.DomainError(f"domain cannot be particle if name is '{name}'") - def create_copy(self): + def create_copy( + self, + new_children=None, + perform_simplifications=True, + ): """See :meth:`pybamm.Symbol.new_copy()`.""" return self.__class__(self.name, domains=self.domains, coord_sys=self.coord_sys) @@ -174,8 +201,13 @@ class SpatialVariableEdge(SpatialVariable): """ def __init__( - self, name, domain=None, auxiliary_domains=None, domains=None, coord_sys=None - ): + self, + name: str, + domain: DomainType = None, + auxiliary_domains: AuxiliaryDomainType = None, + domains: DomainsType = None, + coord_sys=None, + ) -> None: super().__init__(name, domain, auxiliary_domains, domains, coord_sys) def _evaluates_on_edges(self, dimension): diff --git a/pybamm/expression_tree/input_parameter.py b/pybamm/expression_tree/input_parameter.py index 2680276c60..dc0d69e204 100644 --- a/pybamm/expression_tree/input_parameter.py +++ b/pybamm/expression_tree/input_parameter.py @@ -1,11 +1,14 @@ # # Parameter classes # +from __future__ import annotations import numbers import numpy as np import scipy.sparse import pybamm +from pybamm.type_definitions import DomainType + class InputParameter(pybamm.Symbol): """ @@ -25,7 +28,12 @@ class InputParameter(pybamm.Symbol): The size of the input parameter expected, defaults to 1 (scalar input) """ - def __init__(self, name, domain=None, expected_size=None): + def __init__( + self, + name: str, + domain: DomainType = None, + expected_size: int | None = None, + ) -> None: # Expected size defaults to 1 if no domain else None (gets set later) if expected_size is None: if domain is None: @@ -37,17 +45,17 @@ def __init__(self, name, domain=None, expected_size=None): @classmethod def _from_json(cls, snippet: dict): - instance = cls.__new__(cls) - - instance.__init__( + return cls( snippet["name"], domain=snippet["domain"], expected_size=snippet["expected_size"], ) - return instance - - def create_copy(self): + def create_copy( + self, + new_children=None, + perform_simplifications=True, + ) -> pybamm.InputParameter: """See :meth:`pybamm.Symbol.new_copy()`.""" new_input_parameter = InputParameter( self.name, self.domain, expected_size=self._expected_size @@ -66,7 +74,7 @@ def _evaluate_for_shape(self): else: return np.nan * np.ones((self._expected_size, 1)) - def _jac(self, variable): + def _jac(self, variable: pybamm.StateVector) -> pybamm.Matrix: """See :meth:`pybamm.Symbol._jac()`.""" n_variable = variable.evaluation_array.count(True) nan_vector = self._evaluate_for_shape() @@ -77,7 +85,13 @@ def _jac(self, variable): zero_matrix = scipy.sparse.csr_matrix((n_self, n_variable)) return pybamm.Matrix(zero_matrix) - def _base_evaluate(self, t=None, y=None, y_dot=None, inputs=None): + def _base_evaluate( + self, + t: float | None = None, + y: np.ndarray | None = None, + y_dot: np.ndarray | None = None, + inputs: dict | str | None = None, + ): # inputs should be a dictionary # convert 'None' to empty dictionary for more informative error if inputs is None: @@ -90,8 +104,8 @@ def _base_evaluate(self, t=None, y=None, y_dot=None, inputs=None): try: input_eval = inputs[self.name] # raise more informative error if can't find name in dict - except KeyError: - raise KeyError(f"Input parameter '{self.name}' not found") + except KeyError as error: + raise KeyError(f"Input parameter '{self.name}' not found") from error if isinstance(input_eval, numbers.Number): input_size = 1 @@ -106,9 +120,7 @@ def _base_evaluate(self, t=None, y=None, y_dot=None, inputs=None): return input_eval else: raise ValueError( - "Input parameter '{}' was given an object of size '{}'".format( - self.name, input_size - ) + f"Input parameter '{self.name}' was given an object of size '{input_size}'" + f" but was expecting an object of size '{self._expected_size}'." ) diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py index 7efe10413c..48ef49f874 100644 --- a/pybamm/expression_tree/interpolant.py +++ b/pybamm/expression_tree/interpolant.py @@ -1,9 +1,11 @@ # # Interpolating class # +from __future__ import annotations import numpy as np from scipy import interpolate -import warnings +from collections.abc import Sequence +import numbers import pybamm @@ -41,22 +43,15 @@ class Interpolant(pybamm.Function): def __init__( self, - x, - y, - children, - name=None, - interpolator="linear", - extrapolate=True, - entries_string=None, + x: np.ndarray | Sequence[np.ndarray], + y: np.ndarray, + children: Sequence[pybamm.Symbol] | pybamm.Time, + name: str | None = None, + interpolator: str | None = "linear", + extrapolate: bool = True, + entries_string: str | None = None, + _num_derivatives: int = 0, ): - # "cubic spline" has been renamed to "cubic" - if interpolator == "cubic spline": - interpolator = "cubic" - warnings.warn( - "The 'cubic spline' interpolator has been renamed to 'cubic'.", - DeprecationWarning, - ) - # Check interpolator is valid if interpolator not in ["linear", "cubic", "pchip"]: raise ValueError(f"interpolator '{interpolator}' not recognised") @@ -101,15 +96,15 @@ def __init__( x1 = x[0] else: x1 = x - x = [x] + x: list[np.ndarray] = [x] # type: ignore[no-redef] x2 = None if x1.shape[0] != y.shape[0]: raise ValueError( "len(x1) should equal y=shape[0], " f"but x1.shape={x1.shape} and y.shape={y.shape}" ) - # children should be a list not a symbol - if isinstance(children, pybamm.Symbol): + # children should be a list not a symbol or a number + if isinstance(children, (pybamm.Symbol, numbers.Number)): children = [children] # Either a single x is provided and there is one child # or x is a 2-tuple and there are two children @@ -127,14 +122,15 @@ def __init__( self.dimension = 1 if interpolator == "linear": if extrapolate is False: - fill_value = np.nan + fill_value_1: float | str = np.nan elif extrapolate is True: - fill_value = "extrapolate" + fill_value_1 = "extrapolate" interpolating_function = interpolate.interp1d( x1, - y.T, + y, bounds_error=False, - fill_value=fill_value, + fill_value=fill_value_1, + axis=0, ) elif interpolator == "cubic": interpolating_function = interpolate.CubicSpline( @@ -194,9 +190,13 @@ def __init__( self.x = x self.y = y self.entries_string = entries_string - super().__init__( - interpolating_function, *children, name=name, derivative="derivative" - ) + + # Differentiate the interpolating function if necessary + self._num_derivatives = _num_derivatives + for _ in range(_num_derivatives): + interpolating_function = interpolating_function.derivative() + + super().__init__(interpolating_function, *children, name=name) # Store information as attributes self.interpolator = interpolator @@ -205,24 +205,22 @@ def __init__( @classmethod def _from_json(cls, snippet: dict): """Create an Interpolant object from JSON data""" - instance = cls.__new__(cls) + + x1 = [] if len(snippet["x"]) == 1: - x = [np.array(x) for x in snippet["x"]] - else: - x = tuple(np.array(x) for x in snippet["x"]) + x1 = [np.array(x) for x in snippet["x"]] - instance.__init__( - x, + return cls( + x1 if x1 else tuple(np.array(x) for x in snippet["x"]), np.array(snippet["y"]), snippet["children"], name=snippet["name"], interpolator=snippet["interpolator"], extrapolate=snippet["extrapolate"], + _num_derivatives=snippet["_num_derivatives"], ) - return instance - @property def entries_string(self): return self._entries_string @@ -249,11 +247,14 @@ def set_id(self): self.entries_string, *tuple([child.id for child in self.children]), *tuple(self.domain), + self._num_derivatives, ) ) - def _function_new_copy(self, children): - """See :meth:`Function._function_new_copy()`""" + def create_copy(self, new_children=None, perform_simplifications=True): + """See :meth:`pybamm.Symbol.new_copy()`.""" + children = self._children_for_copying(new_children) + return pybamm.Interpolant( self.x, self.y, @@ -262,6 +263,7 @@ def _function_new_copy(self, children): interpolator=self.interpolator, extrapolate=self.extrapolate, entries_string=self.entries_string, + _num_derivatives=self._num_derivatives, ) def _function_evaluate(self, evaluated_children): @@ -317,6 +319,27 @@ def _function_evaluate(self, evaluated_children): else: # pragma: no cover raise ValueError(f"Invalid dimension: {self.dimension}") + def _function_diff(self, children: Sequence[pybamm.Symbol], idx: float): + """ + Derivative with respect to child number 'idx'. + See :meth:`pybamm.Symbol._diff()`. + """ + if len(children) > 1: + raise NotImplementedError( + "differentiation not implemented for functions with more than one child" + ) + else: + # keep using "derivative" as derivative + return Interpolant( + self.x, + self.y, + children, + name=self.name, + interpolator=self.interpolator, + extrapolate=self.extrapolate, + _num_derivatives=self._num_derivatives + 1, + ) + def to_json(self): """ Method to serialise an Interpolant object into JSON. @@ -329,6 +352,7 @@ def to_json(self): "y": self.y.tolist(), "interpolator": self.interpolator, "extrapolate": self.extrapolate, + "_num_derivatives": self._num_derivatives, } return json_dict diff --git a/pybamm/expression_tree/matrix.py b/pybamm/expression_tree/matrix.py index 8b36bca53e..435d06d84f 100644 --- a/pybamm/expression_tree/matrix.py +++ b/pybamm/expression_tree/matrix.py @@ -1,10 +1,12 @@ # # Matrix class # +from __future__ import annotations import numpy as np from scipy.sparse import csr_matrix, issparse import pybamm +from pybamm.type_definitions import DomainType, AuxiliaryDomainType, DomainsType class Matrix(pybamm.Array): @@ -14,13 +16,13 @@ class Matrix(pybamm.Array): def __init__( self, - entries, - name=None, - domain=None, - auxiliary_domains=None, - domains=None, - entries_string=None, - ): + entries: np.ndarray | list[float] | csr_matrix, + name: str | None = None, + domain: DomainType = None, + auxiliary_domains: AuxiliaryDomainType = None, + domains: DomainsType = None, + entries_string: str | None = None, + ) -> None: if isinstance(entries, list): entries = np.array(entries) if name is None: diff --git a/pybamm/expression_tree/operations/__init__.py b/pybamm/expression_tree/operations/__init__.py index e69de29bb2..e1dd103840 100644 --- a/pybamm/expression_tree/operations/__init__.py +++ b/pybamm/expression_tree/operations/__init__.py @@ -0,0 +1,2 @@ +__all__ = ['convert_to_casadi', 'evaluate_python', 'jacobian', 'latexify', + 'serialise', 'unpack_symbols'] diff --git a/pybamm/expression_tree/operations/convert_to_casadi.py b/pybamm/expression_tree/operations/convert_to_casadi.py index d29ae994f2..274fd95154 100644 --- a/pybamm/expression_tree/operations/convert_to_casadi.py +++ b/pybamm/expression_tree/operations/convert_to_casadi.py @@ -1,6 +1,8 @@ # # Convert a PyBaMM expression tree to a CasADi expression tree # +from __future__ import annotations + import pybamm import casadi import numpy as np @@ -13,7 +15,14 @@ def __init__(self, casadi_symbols=None): pybamm.citations.register("Andersson2019") - def convert(self, symbol, t, y, y_dot, inputs): + def convert( + self, + symbol: pybamm.Symbol, + t: casadi.MX, + y: casadi.MX, + y_dot: casadi.MX, + inputs: dict | None, + ) -> casadi.MX: """ This function recurses down the tree, converting the PyBaMM expression tree to a CasADi expression tree @@ -148,15 +157,31 @@ def _convert(self, symbol, t, y, y_dot, inputs): ) if len(converted_children) == 1: - return casadi.interpolant( - "LUT", solver, symbol.x, symbol.y.flatten() - )(*converted_children) + if solver == "linear": + test = casadi.MX.interpn_linear( + symbol.x, symbol.y.flatten(), converted_children + ) + if test.shape[0] == 1 and test.shape[1] > 1: + # for some reason, pybamm.Interpolant always returns a column vector, so match that + test = test.T + return test + else: + return casadi.interpolant( + "LUT", solver, symbol.x, symbol.y.flatten() + )(*converted_children) elif len(converted_children) in [2, 3]: - LUT = casadi.interpolant( - "LUT", solver, symbol.x, symbol.y.ravel(order="F") - ) - res = LUT(casadi.hcat(converted_children).T).T - return res + if solver == "linear": + return casadi.MX.interpn_linear( + symbol.x, + symbol.y.ravel(order="F"), + converted_children, + ) + else: + LUT = casadi.interpolant( + "LUT", solver, symbol.x, symbol.y.ravel(order="F") + ) + res = LUT(casadi.hcat(converted_children).T).T + return res else: # pragma: no cover raise ValueError( f"Invalid converted_children count: {len(converted_children)}" @@ -206,8 +231,8 @@ def _convert(self, symbol, t, y, y_dot, inputs): else: raise TypeError( - """ - Cannot convert symbol of type '{}' to CasADi. Symbols must all be + f""" + Cannot convert symbol of type '{type(symbol)}' to CasADi. Symbols must all be 'linear algebra' at this stage. - """.format(type(symbol)) + """ ) diff --git a/pybamm/expression_tree/operations/evaluate_python.py b/pybamm/expression_tree/operations/evaluate_python.py index d7a43486f0..6d13761756 100644 --- a/pybamm/expression_tree/operations/evaluate_python.py +++ b/pybamm/expression_tree/operations/evaluate_python.py @@ -1,8 +1,10 @@ # # Write a symbol to python # +from __future__ import annotations import numbers from collections import OrderedDict +from numpy.typing import ArrayLike import numpy as np import scipy.sparse @@ -38,7 +40,9 @@ class JaxCooMatrix: where x is the number of rows, and y the number of columns of the matrix """ - def __init__(self, row, col, data, shape): + def __init__( + self, row: ArrayLike, col: ArrayLike, data: ArrayLike, shape: tuple[int, int] + ): if not pybamm.have_jax(): # pragma: no cover raise ModuleNotFoundError( "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-jaxsolver" @@ -68,7 +72,7 @@ def dot_product(self, b): result = jax.numpy.zeros((self.shape[0], 1), dtype=b.dtype) return result.at[self.row].add(self.data.reshape(-1, 1) * b[self.col]) - def scalar_multiply(self, b): + def scalar_multiply(self, b: float): """ multiply of matrix with a scalar b @@ -91,7 +95,7 @@ def __matmul__(self, b): return self.dot_product(b) -def create_jax_coo_matrix(value): +def create_jax_coo_matrix(value: scipy.sparse): """ Creates a JaxCooMatrix from a scipy.sparse matrix @@ -131,7 +135,12 @@ def is_scalar(arg): return np.all(np.array(arg.shape) == 1) -def find_symbols(symbol, constant_symbols, variable_symbols, output_jax=False): +def find_symbols( + symbol: pybamm.Symbol, + constant_symbols: OrderedDict, + variable_symbols: OrderedDict, + output_jax=False, +): """ This function converts an expression tree to a dictionary of node id's and strings specifying valid python code to calculate that nodes value, given y and t. @@ -353,15 +362,15 @@ def find_symbols(symbol, constant_symbols, variable_symbols, output_jax=False): else: raise NotImplementedError( - "Conversion to python not implemented for a symbol of type '{}'".format( - type(symbol) - ) + f"Conversion to python not implemented for a symbol of type '{type(symbol)}'" ) variable_symbols[symbol.id] = symbol_str -def to_python(symbol, debug=False, output_jax=False): +def to_python( + symbol: pybamm.Symbol, debug=False, output_jax=False +) -> tuple[OrderedDict, str]: """ This function converts an expression tree into a dict of constant input values, and valid python code that acts like the tree's :func:`pybamm.Symbol.evaluate` function @@ -387,17 +396,15 @@ def to_python(symbol, debug=False, output_jax=False): operations are used """ - constant_values = OrderedDict() - variable_symbols = OrderedDict() + constant_values: OrderedDict = OrderedDict() + variable_symbols: OrderedDict = OrderedDict() find_symbols(symbol, constant_values, variable_symbols, output_jax) line_format = "{} = {}" if debug: # pragma: no cover variable_lines = [ - "print('{}'); ".format( - line_format.format(id_to_python_variable(symbol_id, False), symbol_line) - ) + f"print('{line_format.format(id_to_python_variable(symbol_id, False), symbol_line)}'); " + line_format.format(id_to_python_variable(symbol_id, False), symbol_line) + "; print(type({0}),np.shape({0}))".format( id_to_python_variable(symbol_id, False) @@ -427,7 +434,7 @@ class EvaluatorPython: """ - def __init__(self, symbol): + def __init__(self, symbol: pybamm.Symbol): constants, python_str = pybamm.to_python(symbol, debug=False) # extract constants in generated function @@ -519,7 +526,7 @@ class EvaluatorJax: """ - def __init__(self, symbol): + def __init__(self, symbol: pybamm.Symbol): if not pybamm.have_jax(): # pragma: no cover raise ModuleNotFoundError( "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-jaxsolver" @@ -585,7 +592,8 @@ def __init__(self, symbol): self._static_argnums = tuple(static_argnums) self._jit_evaluate = jax.jit( - self._evaluate_jax, static_argnums=self._static_argnums + self._evaluate_jax, # type:ignore[attr-defined] + static_argnums=self._static_argnums, ) def get_jacobian(self): diff --git a/pybamm/expression_tree/operations/jacobian.py b/pybamm/expression_tree/operations/jacobian.py index a191e2c74d..fd31284703 100644 --- a/pybamm/expression_tree/operations/jacobian.py +++ b/pybamm/expression_tree/operations/jacobian.py @@ -1,6 +1,7 @@ # # Calculate the Jacobian of a symbol # +from __future__ import annotations import pybamm @@ -18,11 +19,15 @@ class Jacobian: whether or not the Jacobian clears the domain (default True) """ - def __init__(self, known_jacs=None, clear_domain=True): + def __init__( + self, + known_jacs: dict[pybamm.Symbol, pybamm.Symbol] | None = None, + clear_domain: bool = True, + ): self._known_jacs = known_jacs or {} self._clear_domain = clear_domain - def jac(self, symbol, variable): + def jac(self, symbol: pybamm.Symbol, variable: pybamm.Symbol) -> pybamm.Symbol: """ This function recurses down the tree, computing the Jacobian using the Jacobians defined in classes derived from pybamm.Symbol. E.g. the @@ -52,7 +57,7 @@ def jac(self, symbol, variable): self._known_jacs[symbol] = jac return jac - def _jac(self, symbol, variable): + def _jac(self, symbol: pybamm.Symbol, variable: pybamm.Symbol): """See :meth:`Jacobian.jac()`.""" if isinstance(symbol, pybamm.BinaryOperator): @@ -64,12 +69,12 @@ def _jac(self, symbol, variable): jac = symbol._binary_jac(left_jac, right_jac) elif isinstance(symbol, pybamm.UnaryOperator): - child_jac = self.jac(symbol.child, variable) + child_jac = self.jac(symbol.child, variable) # type: ignore[has-type] # _unary_jac defined in derived classes for specific rules jac = symbol._unary_jac(child_jac) elif isinstance(symbol, pybamm.Function): - children_jacs = [None] * len(symbol.children) + children_jacs: list[None | pybamm.Symbol] = [None] * len(symbol.children) for i, child in enumerate(symbol.children): children_jacs[i] = self.jac(child, variable) # _function_jac defined in function class @@ -85,10 +90,10 @@ def _jac(self, symbol, variable): else: try: jac = symbol._jac(variable) - except NotImplementedError: + except NotImplementedError as error: raise NotImplementedError( f"Cannot calculate Jacobian of symbol of type '{type(symbol)}'" - ) + ) from error # Jacobian by default removes the domain(s) if self._clear_domain: diff --git a/pybamm/expression_tree/operations/latexify.py b/pybamm/expression_tree/operations/latexify.py index 572f01a560..04b1a72e41 100644 --- a/pybamm/expression_tree/operations/latexify.py +++ b/pybamm/expression_tree/operations/latexify.py @@ -1,13 +1,15 @@ # # Latexify class # +from __future__ import annotations + import copy import re import warnings import pybamm from pybamm.expression_tree.printing.sympy_overrides import custom_print_func -from pybamm.util import have_optional_dependency +import sympy def get_rng_min_max_name(rng, min_or_max): @@ -36,19 +38,19 @@ class Latexify: >>> model = pybamm.lithium_ion.SPM() This will returns all model equations in png - >>> model.latexify("equations.png") + >>> model.latexify("equations.png") # doctest: +SKIP This will return all the model equations in latex - >>> model.latexify() + >>> model.latexify() # doctest: +SKIP This will return the list of all the model equations - >>> model.latexify(newline=False) + >>> model.latexify(newline=False) # doctest: +SKIP This will return first five model equations - >>> model.latexify(newline=False)[1:5] + >>> model.latexify(newline=False)[1:5] # doctest: +SKIP """ - def __init__(self, model, filename=None, newline=True): + def __init__(self, model, filename: str | None = None, newline: bool = True): self.model = model self.filename = filename self.newline = newline @@ -74,10 +76,10 @@ def _get_geometry_displays(self, var): rng_min = get_rng_min_max_name(rng, "min") # Take range maximum from the last domain - for var_name, rng in self.model.default_geometry[var.domain[-1]].items(): + for _, rng in self.model.default_geometry[var.domain[-1]].items(): rng_max = get_rng_min_max_name(rng, "max") - geo_latex = f"\quad {rng_min} < {name} < {rng_max}" + geo_latex = rf"\quad {rng_min} < {name} < {rng_max}" geo.append(geo_latex) return geo @@ -87,7 +89,6 @@ def _get_bcs_displays(self, var): Returns a list of boundary condition equations with ranges in front of the equations. """ - sympy = have_optional_dependency("sympy") bcs_eqn_list = [] bcs = self.model.boundary_conditions.get(var, None) @@ -118,7 +119,6 @@ def _get_bcs_displays(self, var): def _get_param_var(self, node): """Returns a list of parameters and a list of variables.""" - sympy = have_optional_dependency("sympy") param_list = [] var_list = [] dfs_nodes = [node] @@ -161,7 +161,6 @@ def _get_param_var(self, node): return param_list, var_list def latexify(self, output_variables=None): - sympy = have_optional_dependency("sympy") # Voltage is the default output variable if it exists if output_variables is None: if "Voltage [V]" in self.model.variables: @@ -304,7 +303,8 @@ def latexify(self, output_variables=None): # When equations are too huge, set output resolution to default except RuntimeError: # pragma: no cover warnings.warn( - "RuntimeError - Setting the output resolution to default" + "RuntimeError - Setting the output resolution to default", + stacklevel=2, ) return sympy.preview( eqn_new_line, diff --git a/pybamm/expression_tree/operations/serialise.py b/pybamm/expression_tree/operations/serialise.py index 53505dbb1f..0507b3304e 100644 --- a/pybamm/expression_tree/operations/serialise.py +++ b/pybamm/expression_tree/operations/serialise.py @@ -7,8 +7,6 @@ import numpy as np import re -from typing import Optional - class Serialise: """ @@ -48,15 +46,17 @@ def default(self, node: dict): class _MeshEncoder(json.JSONEncoder): """Converts PyBaMM meshes into a JSON-serialisable format""" - def default(self, node: dict): + def default(self, node: pybamm.Mesh): node_dict = {"py/object": str(type(node))[8:-2], "py/id": id(node)} if isinstance(node, pybamm.Mesh): node_dict.update(node.to_json()) - node_dict["sub_meshes"] = {} + submeshes = {} for k, v in node.items(): if len(k) == 1 and "ghost cell" not in k[0]: - node_dict["sub_meshes"][k[0]] = self.default(v) + submeshes[k[0]] = self.default(v) + + node_dict["sub_meshes"] = submeshes return node_dict @@ -80,9 +80,9 @@ class _EmptyDict(dict): def save_model( self, model: pybamm.BaseModel, - mesh: Optional[pybamm.Mesh] = None, - variables: Optional[pybamm.FuzzyDict] = None, - filename: Optional[str] = None, + mesh: pybamm.Mesh | None = None, + variables: pybamm.FuzzyDict | None = None, + filename: str | None = None, ): """Saves a discretised model to a JSON file. @@ -114,7 +114,7 @@ def save_model( "pybamm_version": pybamm.__version__, "name": model.name, "options": model.options, - "bounds": [bound.tolist() for bound in model.bounds], + "bounds": [bound.tolist() for bound in model.bounds], # type: ignore[attr-defined] "concatenated_rhs": self._SymbolEncoder().default(model._concatenated_rhs), "concatenated_algebraic": self._SymbolEncoder().default( model._concatenated_algebraic @@ -144,7 +144,7 @@ def save_model( json.dump(model_json, f) def load_model( - self, filename: str, battery_model: Optional[pybamm.BaseModel] = None + self, filename: str, battery_model: pybamm.BaseModel | None = None ) -> pybamm.BaseModel: """ Loads a discretised, ready to solve model into PyBaMM. @@ -247,12 +247,15 @@ def _get_pybamm_class(self, snippet: dict): try: empty_class = self._Empty() empty_class.__class__ = class_ + + return empty_class + except TypeError: # Mesh objects have a different layouts - empty_class = self._EmptyDict() - empty_class.__class__ = class_ + empty_dict_class = self._EmptyDict() + empty_dict_class.__class__ = class_ - return empty_class + return empty_dict_class def _deconstruct_pybamm_dicts(self, dct: dict): """ diff --git a/pybamm/expression_tree/operations/unpack_symbols.py b/pybamm/expression_tree/operations/unpack_symbols.py index 825cb2db40..97e1d08d98 100644 --- a/pybamm/expression_tree/operations/unpack_symbols.py +++ b/pybamm/expression_tree/operations/unpack_symbols.py @@ -1,6 +1,12 @@ # # Helper function to unpack a symbol # +from __future__ import annotations +from typing import TYPE_CHECKING +from collections.abc import Sequence + +if TYPE_CHECKING: # pragma: no cover + import pybamm class SymbolUnpacker: @@ -16,11 +22,17 @@ class SymbolUnpacker: cached unpacked equations """ - def __init__(self, classes_to_find, unpacked_symbols=None): + def __init__( + self, + classes_to_find: Sequence[pybamm.Symbol] | pybamm.Symbol, + unpacked_symbols: dict | None = None, + ): self.classes_to_find = classes_to_find - self._unpacked_symbols = unpacked_symbols or {} + self._unpacked_symbols: dict = unpacked_symbols or {} - def unpack_list_of_symbols(self, list_of_symbols): + def unpack_list_of_symbols( + self, list_of_symbols: Sequence[pybamm.Symbol] + ) -> set[pybamm.Symbol]: """ Unpack a list of symbols. See :meth:`SymbolUnpacker.unpack()` @@ -41,7 +53,9 @@ def unpack_list_of_symbols(self, list_of_symbols): return all_instances - def unpack_symbol(self, symbol): + def unpack_symbol( + self, symbol: Sequence[pybamm.Symbol] | pybamm.Symbol + ) -> list[pybamm.Symbol]: """ This function recurses down the tree, unpacking the symbols and saving the ones that have a class in `self.classes_to_find`. diff --git a/pybamm/expression_tree/parameter.py b/pybamm/expression_tree/parameter.py index 787b7b5007..14560da0b8 100644 --- a/pybamm/expression_tree/parameter.py +++ b/pybamm/expression_tree/parameter.py @@ -1,13 +1,15 @@ # # Parameter classes # -import numbers +from __future__ import annotations import sys import numpy as np +from typing import Literal + +import sympy import pybamm -from pybamm.util import have_optional_dependency class Parameter(pybamm.Symbol): @@ -23,28 +25,31 @@ class Parameter(pybamm.Symbol): name of the node """ - def __init__(self, name): + def __init__(self, name: str) -> None: super().__init__(name) - def create_copy(self): + def create_copy( + self, + new_children=None, + perform_simplifications=True, + ) -> pybamm.Parameter: """See :meth:`pybamm.Symbol.new_copy()`.""" return Parameter(self.name) - def _evaluate_for_shape(self): + def _evaluate_for_shape(self) -> float: """ Returns the scalar 'NaN' to represent the shape of a parameter. See :meth:`pybamm.Symbol.evaluate_for_shape()` """ return np.nan - def is_constant(self): + def is_constant(self) -> Literal[False]: """See :meth:`pybamm.Symbol.is_constant()`.""" # Parameter is not constant since it can become an InputParameter return False - def to_equation(self): + def to_equation(self) -> sympy.Symbol: """Convert the node and its subtree into a SymPy equation.""" - sympy = have_optional_dependency("sympy") if self.print_name is not None: return sympy.Symbol(self.print_name) else: @@ -91,18 +96,18 @@ class FunctionParameter(pybamm.Symbol): def __init__( self, - name, - inputs, - diff_variable=None, + name: str, + inputs: dict[str, pybamm.Symbol], + diff_variable: pybamm.Symbol | None = None, print_name="calculate", - ): + ) -> None: # assign diff variable self.diff_variable = diff_variable children_list = list(inputs.values()) # Turn numbers into scalars for idx, child in enumerate(children_list): - if isinstance(child, numbers.Number): + if isinstance(child, (float, int, np.number)): children_list[idx] = pybamm.Scalar(child) domains = self.get_children_domains(children_list) @@ -116,30 +121,34 @@ def __init__( self.print_name = print_name else: frame = sys._getframe().f_back - print_name = frame.f_code.co_name - if print_name.startswith("_"): - self.print_name = None - else: - try: - parent_param = frame.f_locals["self"] - except KeyError: - parent_param = None - if hasattr(parent_param, "domain") and parent_param.domain is not None: - # add "_n" or "_s" or "_p" if this comes from a Parameter class with - # a domain - d = parent_param.domain[0] - print_name += f"_{d}" - self.print_name = print_name - - @property - def input_names(self): - return self._input_names + if frame is not None: + print_name = frame.f_code.co_name + if print_name.startswith("_"): + self.print_name = None + else: + try: + parent_param = frame.f_locals["self"] + except KeyError: + parent_param = None + if ( + hasattr(parent_param, "domain") + and parent_param.domain is not None + ): + # add "_n" or "_s" or "_p" if this comes from a Parameter class with + # a domain + d = parent_param.domain[0] + print_name += f"_{d}" + self.print_name = print_name def print_input_names(self): if self._input_names: for inp in self._input_names: print(inp) + @property + def input_names(self): + return self._input_names + @input_names.setter def input_names(self, inp=None): if inp: @@ -172,7 +181,7 @@ def set_id(self): ) ) - def diff(self, variable): + def diff(self, variable: pybamm.Symbol) -> pybamm.FunctionParameter: """See :meth:`pybamm.Symbol.diff()`.""" # return a new FunctionParameter, that knows it will need to be differentiated # when the parameters are set @@ -188,39 +197,19 @@ def diff(self, variable): print_name=self.print_name + "'", ) - def create_copy(self): + def create_copy(self, new_children=None, perform_simplifications=True): """See :meth:`pybamm.Symbol.new_copy()`.""" - out = self._function_parameter_new_copy( - self._input_names, self.orphans, print_name=self.print_name - ) - return out - - def _function_parameter_new_copy( - self, input_names, children, print_name="calculate" - ): - """ - Returns a new copy of the function parameter. - - Inputs - ------ - input_names : : list - A list of str of the names of the children/function inputs - children : : list - A list of the children of the function - - Returns - ------- - :class:`pybamm.FunctionParameter` - A new copy of the function parameter - """ - input_dict = {input_names[i]: children[i] for i in range(len(input_names))} + input_dict = { + self._input_names[i]: self.children[i] + for i in range(len(self._input_names)) + } return FunctionParameter( self.name, input_dict, diff_variable=self.diff_variable, - print_name=print_name, + print_name=self.print_name, ) def _evaluate_for_shape(self): @@ -231,9 +220,8 @@ def _evaluate_for_shape(self): # add 1e-16 to avoid division by zero return sum(child.evaluate_for_shape() for child in self.children) + 1e-16 - def to_equation(self): + def to_equation(self) -> sympy.Symbol: """Convert the node and its subtree into a SymPy equation.""" - sympy = have_optional_dependency("sympy") if self.print_name is not None: return sympy.Symbol(self.print_name) else: diff --git a/pybamm/expression_tree/printing/__init__.py b/pybamm/expression_tree/printing/__init__.py index e69de29bb2..d5d4656374 100644 --- a/pybamm/expression_tree/printing/__init__.py +++ b/pybamm/expression_tree/printing/__init__.py @@ -0,0 +1 @@ +__all__ = ['print_name', 'sympy_overrides'] diff --git a/pybamm/expression_tree/printing/sympy_overrides.py b/pybamm/expression_tree/printing/sympy_overrides.py index 58ac356399..14a49a7a71 100644 --- a/pybamm/expression_tree/printing/sympy_overrides.py +++ b/pybamm/expression_tree/printing/sympy_overrides.py @@ -14,7 +14,7 @@ def _print_Derivative(self, expr): eqn = super()._print_Derivative(expr) if getattr(expr, "force_partial", False) and "partial" not in eqn: var1, var2 = re.findall(r"^\\frac{(\w+)}{(\w+) .+", eqn)[0] - eqn = eqn.replace(var1, "\partial").replace(var2, "\partial") + eqn = eqn.replace(var1, r"\partial").replace(var2, r"\partial") return eqn diff --git a/pybamm/expression_tree/scalar.py b/pybamm/expression_tree/scalar.py index 73dccf7d6c..052878bb5b 100644 --- a/pybamm/expression_tree/scalar.py +++ b/pybamm/expression_tree/scalar.py @@ -1,10 +1,13 @@ # # Scalar class # +from __future__ import annotations import numpy as np +import sympy +from typing import Literal import pybamm -from pybamm.util import have_optional_dependency +from pybamm.type_definitions import Numeric class Scalar(pybamm.Symbol): @@ -21,7 +24,11 @@ class Scalar(pybamm.Symbol): """ - def __init__(self, value, name=None): + def __init__( + self, + value: Numeric, + name: str | None = None, + ) -> None: # set default name if not provided self.value = value if name is None: @@ -31,11 +38,7 @@ def __init__(self, value, name=None): @classmethod def _from_json(cls, snippet: dict): - instance = cls.__new__(cls) - - instance.__init__(snippet["value"], name=snippet["name"]) - - return instance + return cls(snippet["value"], name=snippet["name"]) def __str__(self): return str(self.value) @@ -60,25 +63,36 @@ def set_id(self): # indistinguishable by class and name alone self._id = hash((self.__class__, str(self.value))) - def _base_evaluate(self, t=None, y=None, y_dot=None, inputs=None): + def _base_evaluate( + self, + t: float | None = None, + y: np.ndarray | None = None, + y_dot: np.ndarray | None = None, + inputs: dict | str | None = None, + ): """See :meth:`pybamm.Symbol._base_evaluate()`.""" return self._value - def _jac(self, variable): + def _jac(self, variable: pybamm.Variable) -> pybamm.Scalar: """See :meth:`pybamm.Symbol._jac()`.""" return pybamm.Scalar(0) - def create_copy(self): + def create_copy( + self, + new_children=None, + perform_simplifications=True, + ): """See :meth:`pybamm.Symbol.new_copy()`.""" + if new_children is not None: + raise ValueError("Cannot create a copy of a scalar with new children") return Scalar(self.value, self.name) - def is_constant(self): + def is_constant(self) -> Literal[True]: """See :meth:`pybamm.Symbol.is_constant()`.""" return True def to_equation(self): """Returns the value returned by the node when evaluated.""" - sympy = have_optional_dependency("sympy") if self.print_name is not None: return sympy.Symbol(self.print_name) else: diff --git a/pybamm/expression_tree/state_vector.py b/pybamm/expression_tree/state_vector.py index 348f908b45..334ebb4334 100644 --- a/pybamm/expression_tree/state_vector.py +++ b/pybamm/expression_tree/state_vector.py @@ -1,10 +1,12 @@ # # State Vector class # +from __future__ import annotations import numpy as np from scipy.sparse import csr_matrix, vstack import pybamm +from pybamm.type_definitions import DomainType, AuxiliaryDomainType, DomainsType class StateVectorBase(pybamm.Symbol): @@ -34,13 +36,13 @@ class StateVectorBase(pybamm.Symbol): def __init__( self, - *y_slices, + *y_slices: slice, base_name="y", - name=None, - domain=None, - auxiliary_domains=None, - domains=None, - evaluation_array=None, + name: str | None = None, + domain: DomainType = None, + auxiliary_domains: AuxiliaryDomainType = None, + domains: DomainsType = None, + evaluation_array: list[bool] | None = None, ): for y_slice in y_slices: if not isinstance(y_slice, slice): @@ -71,19 +73,15 @@ def __init__( @classmethod def _from_json(cls, snippet: dict): - instance = cls.__new__(cls) - y_slices = [slice(s["start"], s["stop"], s["step"]) for s in snippet["y_slice"]] - instance.__init__( + return cls( *y_slices, name=snippet["name"], domains=snippet["domains"], evaluation_array=snippet["evaluation_array"], ) - return instance - @property def y_slices(self): return self._y_slices @@ -126,7 +124,7 @@ def set_id(self): ) ) - def _jac_diff_vector(self, variable): + def _jac_diff_vector(self, variable: pybamm.StateVectorBase): """ Differentiate a slice of a StateVector of size m with respect to another slice of a different StateVector of size n. This returns a (sparse) zero matrix of @@ -147,7 +145,7 @@ def _jac_diff_vector(self, variable): # Return zeros of correct size since no entries match return pybamm.Matrix(csr_matrix((slices_size, variable_size))) - def _jac_same_vector(self, variable): + def _jac_same_vector(self, variable: pybamm.StateVectorBase): """ Differentiate a slice of a StateVector of size m with respect to another slice of a StateVector of size n. This returns a (sparse) matrix of size @@ -192,7 +190,11 @@ def _jac_same_vector(self, variable): ) return pybamm.Matrix(jac) - def create_copy(self): + def create_copy( + self, + new_children=None, + perform_simplifications=True, + ): """See :meth:`pybamm.Symbol.new_copy()`.""" return StateVector( *self.y_slices, @@ -259,12 +261,12 @@ class StateVector(StateVectorBase): def __init__( self, - *y_slices, - name=None, - domain=None, - auxiliary_domains=None, - domains=None, - evaluation_array=None, + *y_slices: slice, + name: str | None = None, + domain: DomainType = None, + auxiliary_domains: AuxiliaryDomainType = None, + domains: DomainsType = None, + evaluation_array: list[bool] | None = None, ): super().__init__( *y_slices, @@ -276,7 +278,13 @@ def __init__( evaluation_array=evaluation_array, ) - def _base_evaluate(self, t=None, y=None, y_dot=None, inputs=None): + def _base_evaluate( + self, + t: float | None = None, + y: np.ndarray | None = None, + y_dot: np.ndarray | None = None, + inputs: dict | str | None = None, + ): """See :meth:`pybamm.Symbol._base_evaluate()`.""" if y is None: raise TypeError("StateVector cannot evaluate input 'y=None'") @@ -290,7 +298,7 @@ def _base_evaluate(self, t=None, y=None, y_dot=None, inputs=None): out = out[:, np.newaxis] return out - def diff(self, variable): + def diff(self, variable: pybamm.Symbol): if variable == self: return pybamm.Scalar(1) if variable == pybamm.t: @@ -303,7 +311,7 @@ def diff(self, variable): else: return pybamm.Scalar(0) - def _jac(self, variable): + def _jac(self, variable: pybamm.StateVector | pybamm.StateVectorDot): if isinstance(variable, pybamm.StateVector): return self._jac_same_vector(variable) elif isinstance(variable, pybamm.StateVectorDot): @@ -337,12 +345,12 @@ class StateVectorDot(StateVectorBase): def __init__( self, - *y_slices, - name=None, - domain=None, - auxiliary_domains=None, - domains=None, - evaluation_array=None, + *y_slices: slice, + name: str | None = None, + domain: DomainType = None, + auxiliary_domains: AuxiliaryDomainType = None, + domains: DomainsType = None, + evaluation_array: list[bool] | None = None, ): super().__init__( *y_slices, @@ -354,7 +362,13 @@ def __init__( evaluation_array=evaluation_array, ) - def _base_evaluate(self, t=None, y=None, y_dot=None, inputs=None): + def _base_evaluate( + self, + t: float | None = None, + y: np.ndarray | None = None, + y_dot: np.ndarray | None = None, + inputs: dict | str | None = None, + ): """See :meth:`pybamm.Symbol._base_evaluate()`.""" if y_dot is None: raise TypeError("StateVectorDot cannot evaluate input 'y_dot=None'") @@ -368,7 +382,7 @@ def _base_evaluate(self, t=None, y=None, y_dot=None, inputs=None): out = out[:, np.newaxis] return out - def diff(self, variable): + def diff(self, variable: pybamm.Symbol): if variable == self: return pybamm.Scalar(1) elif variable == pybamm.t: @@ -378,7 +392,7 @@ def diff(self, variable): else: return pybamm.Scalar(0) - def _jac(self, variable): + def _jac(self, variable: pybamm.StateVector | pybamm.StateVectorDot): if isinstance(variable, pybamm.StateVectorDot): return self._jac_same_vector(variable) elif isinstance(variable, pybamm.StateVector): diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py index 9d68b5f439..df549747c9 100644 --- a/pybamm/expression_tree/symbol.py +++ b/pybamm/expression_tree/symbol.py @@ -1,21 +1,36 @@ # # Base Symbol Class for the expression tree # +from __future__ import annotations import numbers +import warnings import numpy as np +import sympy from scipy.sparse import csr_matrix, issparse -from functools import lru_cache, cached_property +from functools import cached_property +from typing import TYPE_CHECKING, cast +from collections.abc import Sequence import pybamm -from pybamm.util import have_optional_dependency +from pybamm.util import import_optional_dependency from pybamm.expression_tree.printing.print_name import prettify_print_name +if TYPE_CHECKING: # pragma: no cover + import casadi + from pybamm.type_definitions import ( + ChildSymbol, + ChildValue, + DomainType, + AuxiliaryDomainType, + DomainsType, + ) + DOMAIN_LEVELS = ["primary", "secondary", "tertiary", "quaternary"] -EMPTY_DOMAINS = {k: [] for k in DOMAIN_LEVELS} +EMPTY_DOMAINS: dict[str, list] = {k: [] for k in DOMAIN_LEVELS} -def domain_size(domain): +def domain_size(domain: list[str] | str): """ Get the domain size. @@ -43,7 +58,7 @@ def domain_size(domain): return size -def create_object_of_size(size, typ="vector"): +def create_object_of_size(size: int, typ="vector"): """Return object, consisting of NaNs, of the right shape.""" if typ == "vector": return np.nan * np.ones((size, 1)) @@ -51,7 +66,7 @@ def create_object_of_size(size, typ="vector"): return np.nan * np.ones((size, size)) -def evaluate_for_shape_using_domain(domains, typ="vector"): +def evaluate_for_shape_using_domain(domains: dict[str, list[str] | str], typ="vector"): """ Return a vector of the appropriate shape, based on the domains. Domain 'sizes' can clash, but are unlikely to, and won't cause failures if they do. @@ -63,11 +78,11 @@ def evaluate_for_shape_using_domain(domains, typ="vector"): return create_object_of_size(_domain_sizes, typ) -def is_constant(symbol): +def is_constant(symbol: Symbol): return isinstance(symbol, numbers.Number) or symbol.is_constant() -def is_scalar_x(expr, x): +def is_scalar_x(expr: Symbol, x: int): """ Utility function to test if an expression evaluates to a constant scalar value """ @@ -78,28 +93,28 @@ def is_scalar_x(expr, x): return False -def is_scalar_zero(expr): +def is_scalar_zero(expr: Symbol): """ Utility function to test if an expression evaluates to a constant scalar zero """ return is_scalar_x(expr, 0) -def is_scalar_one(expr): +def is_scalar_one(expr: Symbol): """ Utility function to test if an expression evaluates to a constant scalar one """ return is_scalar_x(expr, 1) -def is_scalar_minus_one(expr): +def is_scalar_minus_one(expr: Symbol): """ Utility function to test if an expression evaluates to a constant scalar minus one """ return is_scalar_x(expr, -1) -def is_matrix_x(expr, x): +def is_matrix_x(expr: Symbol, x: int): """ Utility function to test if an expression evaluates to a constant matrix value """ @@ -122,28 +137,28 @@ def is_matrix_x(expr, x): return False -def is_matrix_zero(expr): +def is_matrix_zero(expr: Symbol): """ Utility function to test if an expression evaluates to a constant matrix zero """ return is_matrix_x(expr, 0) -def is_matrix_one(expr): +def is_matrix_one(expr: Symbol): """ Utility function to test if an expression evaluates to a constant matrix one """ return is_matrix_x(expr, 1) -def is_matrix_minus_one(expr): +def is_matrix_minus_one(expr: Symbol): """ Utility function to test if an expression evaluates to a constant matrix minus one """ return is_matrix_x(expr, -1) -def simplify_if_constant(symbol): +def simplify_if_constant(symbol: pybamm.Symbol): """ Utility function to simplify an expression tree if it evalutes to a constant scalar, vector or matrix @@ -156,7 +171,9 @@ def simplify_if_constant(symbol): or (isinstance(result, np.ndarray) and result.ndim == 0) or isinstance(result, np.bool_) ): - return pybamm.Scalar(result) + # type-narrow for Scalar + new_result = cast(float, result) + return pybamm.Scalar(new_result) elif isinstance(result, np.ndarray) or issparse(result): if result.ndim == 1 or result.shape[1] == 1: return pybamm.Vector(result, domains=symbol.domains) @@ -200,11 +217,11 @@ class Symbol: def __init__( self, - name, - children=None, - domain=None, - auxiliary_domains=None, - domains=None, + name: str, + children: Sequence[Symbol] | None = None, + domain: DomainType = None, + auxiliary_domains: AuxiliaryDomainType = None, + domains: DomainsType = None, ): super().__init__() self.name = name @@ -213,13 +230,13 @@ def __init__( children = [] self._children = children - # Keep a separate "oprhans" attribute for backwards compatibility + # Keep a separate "orphans" attribute for backwards compatibility self._orphans = children # Set domains (and hence id) self.domains = self.read_domain_or_domains(domain, auxiliary_domains, domains) - self._saved_evaluates_on_edges = {} + self._saved_evaluates_on_edges: dict = {} self._print_name = None # Test shape on everything but nodes that contain the base Symbol class or @@ -244,14 +261,10 @@ def _from_json(cls, snippet: dict): At minimum, should contain "name", "children" and "domains". """ - instance = cls.__new__(cls) - - instance.__init__( + return cls( snippet["name"], children=snippet["children"], domains=snippet["domains"] ) - return instance - @property def children(self): """ @@ -268,7 +281,7 @@ def name(self): return self._name @name.setter - def name(self, value): + def name(self, value: str): assert isinstance(value, str) self._name = value @@ -276,30 +289,6 @@ def name(self, value): def domains(self): return self._domains - @property - def domain(self): - """ - list of applicable domains. - - Returns - ------- - iterable of str - """ - return self._domains["primary"] - - @domain.setter - def domain(self, domain): - raise NotImplementedError( - "Cannot set domain directly, use domains={'primary': domain} instead" - ) - - @property - def auxiliary_domains(self): - """Returns auxiliary domains.""" - raise NotImplementedError( - "symbol.auxiliary_domains has been deprecated, use symbol.domains instead" - ) - @domains.setter def domains(self, domains): try: @@ -345,6 +334,30 @@ def domains(self, domains): self._domains = domains self.set_id() + @property + def domain(self): + """ + list of applicable domains. + + Returns + ------- + iterable of str + """ + return self._domains["primary"] + + @domain.setter + def domain(self, domain): + raise NotImplementedError( + "Cannot set domain directly, use domains={'primary': domain} instead" + ) + + @property + def auxiliary_domains(self): + """Returns auxiliary domains.""" + raise NotImplementedError( + "symbol.auxiliary_domains has been deprecated, use symbol.domains instead" + ) + @property def secondary_domain(self): """Helper function to get the secondary domain of a symbol.""" @@ -360,7 +373,7 @@ def quaternary_domain(self): """Helper function to get the quaternary domain of a symbol.""" return self._domains["quaternary"] - def copy_domains(self, symbol): + def copy_domains(self, symbol: Symbol): """Copy the domains from a given symbol, bypassing checks.""" if self._domains != symbol._domains: self._domains = symbol._domains @@ -372,9 +385,9 @@ def clear_domains(self): self._domains = EMPTY_DOMAINS self.set_id() - def get_children_domains(self, children): + def get_children_domains(self, children: Sequence[Symbol]): """Combine domains from children, at all levels.""" - domains = {} + domains: dict = {} for child in children: for level in child.domains.keys(): if child.domains[level] == []: @@ -393,7 +406,12 @@ def get_children_domains(self, children): return domains - def read_domain_or_domains(self, domain, auxiliary_domains, domains): + def read_domain_or_domains( + self, + domain: DomainType, + auxiliary_domains: AuxiliaryDomainType, + domains: DomainsType, + ): if domains is None: if isinstance(domain, str): domain = [domain] @@ -463,14 +481,14 @@ def render(self): # pragma: no cover """ Print out a visual representation of the tree (this node and its children) """ - anytree = have_optional_dependency("anytree") + anytree = import_optional_dependency("anytree") for pre, _, node in anytree.RenderTree(self): if isinstance(node, pybamm.Scalar) and node.name != str(node.value): print(f"{pre}{node.name} = {node.value}") else: print(f"{pre}{node.name}") - def visualise(self, filename): + def visualise(self, filename: str): """ Produces a .png file of the tree (this node and its children) with the name filename @@ -482,7 +500,7 @@ def visualise(self, filename): filename to output, must end in ".png" """ - DotExporter = have_optional_dependency("anytree.exporter", "DotExporter") + DotExporter = import_optional_dependency("anytree.exporter", "DotExporter") # check that filename ends in .png. if filename[-4:] != ".png": raise ValueError("filename should end in .png") @@ -497,12 +515,12 @@ def visualise(self, filename): # raise error but only through logger so that test passes pybamm.logger.error("Please install graphviz>=2.42.2 to use dot exporter") - def relabel_tree(self, symbol, counter): + def relabel_tree(self, symbol: Symbol, counter: int): """ Finds all children of a symbol and assigns them a new id so that they can be visualised properly using the graphviz output """ - anytree = have_optional_dependency("anytree") + anytree = import_optional_dependency("anytree") name = symbol.name if name == "div": name = "∇⋅" @@ -545,7 +563,7 @@ def pre_order(self): a b """ - anytree = have_optional_dependency("anytree") + anytree = import_optional_dependency("anytree") return anytree.PreOrderIter(self) def __str__(self): @@ -554,79 +572,73 @@ def __str__(self): def __repr__(self): """returns the string `__class__(id, name, children, domain)`""" - return ("{!s}({}, {!s}, children={!s}, domains={!s})").format( - self.__class__.__name__, - hex(self.id), - self._name, - [str(child) for child in self.children], - {k: v for k, v in self.domains.items() if v != []}, - ) + return f"{self.__class__.__name__!s}({hex(self.id)}, {self._name!s}, children={[str(child) for child in self.children]!s}, domains={({k: v for k, v in self.domains.items() if v != []})!s})" - def __add__(self, other): + def __add__(self, other: ChildSymbol) -> pybamm.Addition: """return an :class:`Addition` object.""" return pybamm.add(self, other) - def __radd__(self, other): + def __radd__(self, other: ChildSymbol) -> pybamm.Addition: """return an :class:`Addition` object.""" return pybamm.add(other, self) - def __sub__(self, other): + def __sub__(self, other: ChildSymbol) -> pybamm.Subtraction: """return a :class:`Subtraction` object.""" return pybamm.subtract(self, other) - def __rsub__(self, other): + def __rsub__(self, other: ChildSymbol) -> pybamm.Subtraction: """return a :class:`Subtraction` object.""" return pybamm.subtract(other, self) - def __mul__(self, other): + def __mul__(self, other: ChildSymbol) -> pybamm.Multiplication: """return a :class:`Multiplication` object.""" return pybamm.multiply(self, other) - def __rmul__(self, other): + def __rmul__(self, other: ChildSymbol) -> pybamm.Multiplication: """return a :class:`Multiplication` object.""" return pybamm.multiply(other, self) - def __matmul__(self, other): + def __matmul__(self, other: ChildSymbol) -> pybamm.MatrixMultiplication: """return a :class:`MatrixMultiplication` object.""" return pybamm.matmul(self, other) - def __rmatmul__(self, other): + def __rmatmul__(self, other: ChildSymbol) -> pybamm.MatrixMultiplication: """return a :class:`MatrixMultiplication` object.""" return pybamm.matmul(other, self) - def __truediv__(self, other): + def __truediv__(self, other: ChildSymbol) -> pybamm.Division: """return a :class:`Division` object.""" return pybamm.divide(self, other) - def __rtruediv__(self, other): + def __rtruediv__(self, other: ChildSymbol) -> pybamm.Division: """return a :class:`Division` object.""" return pybamm.divide(other, self) - def __pow__(self, other): + def __pow__(self, other: ChildSymbol) -> pybamm.Power: """return a :class:`Power` object.""" return pybamm.simplified_power(self, other) - def __rpow__(self, other): + def __rpow__(self, other: Symbol) -> pybamm.Power: """return a :class:`Power` object.""" return pybamm.simplified_power(other, self) - def __lt__(self, other): + def __lt__(self, other: Symbol | float) -> pybamm.NotEqualHeaviside: """return a :class:`NotEqualHeaviside` object, or a smooth approximation.""" return pybamm.expression_tree.binary_operators._heaviside(self, other, False) - def __le__(self, other): + def __le__(self, other: Symbol) -> pybamm.EqualHeaviside: """return a :class:`EqualHeaviside` object, or a smooth approximation.""" return pybamm.expression_tree.binary_operators._heaviside(self, other, True) - def __gt__(self, other): + def __gt__(self, other: Symbol) -> pybamm.NotEqualHeaviside: """return a :class:`NotEqualHeaviside` object, or a smooth approximation.""" return pybamm.expression_tree.binary_operators._heaviside(other, self, False) - def __ge__(self, other): + def __ge__(self, other: Symbol) -> pybamm.EqualHeaviside: """return a :class:`EqualHeaviside` object, or a smooth approximation.""" return pybamm.expression_tree.binary_operators._heaviside(other, self, True) - def __neg__(self): + def __neg__(self) -> pybamm.Negate: """return a :class:`Negate` object.""" if isinstance(self, pybamm.Negate): # Double negative is a positive @@ -634,7 +646,7 @@ def __neg__(self): elif isinstance(self, pybamm.Broadcast): # Move negation inside the broadcast # Apply recursively - return self._unary_new_copy(-self.orphans[0]) + return self.create_copy([-self.orphans[0]]) elif isinstance(self, pybamm.Subtraction): # negation flips the subtraction return self.right - self.left @@ -645,7 +657,7 @@ def __neg__(self): else: return pybamm.simplify_if_constant(pybamm.Negate(self)) - def __abs__(self): + def __abs__(self) -> pybamm.AbsoluteValue: """return an :class:`AbsoluteValue` object, or a smooth approximation.""" if isinstance(self, pybamm.AbsoluteValue): # No need to apply abs a second time @@ -654,7 +666,7 @@ def __abs__(self): # Move absolute value inside the broadcast # Apply recursively abs_self_not_broad = abs(self.orphans[0]) - return self._unary_new_copy(abs_self_not_broad) + return self.create_copy([abs_self_not_broad]) else: k = pybamm.settings.abs_smoothing # Return exact approximation if that is the setting or the outcome is a @@ -665,12 +677,21 @@ def __abs__(self): out = pybamm.smooth_absolute_value(self, k) return pybamm.simplify_if_constant(out) - def __mod__(self, other): + def __mod__(self, other: Symbol) -> pybamm.Modulo: """return an :class:`Modulo` object.""" return pybamm.simplify_if_constant(pybamm.Modulo(self, other)) def __bool__(self): - raise NotImplementedError("Boolean operator not defined for Symbols.") + raise NotImplementedError( + "Boolean operator not defined for Symbols. You might be seeing this message because you are trying to " + "specify an if statement based on the value of a symbol, e.g." + "\nif x < 0:\n" + "\ty = 1\n" + "else:\n" + "\ty = 2\n" + "In this case, use heaviside functions instead:" + "\ny = 1 * (x < 0) + 2 * (x >= 0)" + ) def __array_ufunc__(self, ufunc, method, *inputs, **kwargs): """ @@ -679,7 +700,7 @@ def __array_ufunc__(self, ufunc, method, *inputs, **kwargs): """ return getattr(pybamm, ufunc.__name__)(*inputs, **kwargs) - def diff(self, variable): + def diff(self, variable: Symbol): """ Differentiate a symbol with respect to a variable. For any symbol that can be differentiated, return `1` if differentiating with respect to yourself, @@ -708,7 +729,12 @@ def _diff(self, variable): """ raise NotImplementedError - def jac(self, variable, known_jacs=None, clear_domain=True): + def jac( + self, + variable: pybamm.Symbol, + known_jacs: dict[pybamm.Symbol, pybamm.Symbol] | None = None, + clear_domain=True, + ): """ Differentiate a symbol with respect to a (slice of) a StateVector or StateVectorDot. @@ -729,7 +755,13 @@ def _jac(self, variable): """ raise NotImplementedError - def _base_evaluate(self, t=None, y=None, y_dot=None, inputs=None): + def _base_evaluate( + self, + t: float | None = None, + y: np.ndarray | None = None, + y_dot: np.ndarray | None = None, + inputs: dict | str | None = None, + ): """ evaluate expression tree. @@ -755,7 +787,13 @@ def _base_evaluate(self, t=None, y=None, y_dot=None, inputs=None): f"{self!s} of type {type(self)}" ) - def evaluate(self, t=None, y=None, y_dot=None, inputs=None): + def evaluate( + self, + t: float | None = None, + y: np.ndarray | None = None, + y_dot: np.ndarray | None = None, + inputs: dict | str | None = None, + ) -> ChildValue: """Evaluate expression tree (wrapper to allow using dict of known values). Parameters @@ -807,7 +845,7 @@ def is_constant(self): # Default behaviour is False return False - def evaluate_ignoring_errors(self, t=0): + def evaluate_ignoring_errors(self, t: float | None = 0): """ Evaluates the expression. If a node exists in the tree that cannot be evaluated as a scalar or vector (e.g. Time, Parameter, Variable, StateVector), then None @@ -840,7 +878,7 @@ def evaluate_ignoring_errors(self, t=0): return None raise pybamm.ShapeError( f"Cannot find shape (original error: {error})" - ) # pragma: no cover + ) from error # pragma: no cover return result def evaluates_to_number(self): @@ -859,8 +897,7 @@ def evaluates_to_number(self): def evaluates_to_constant_number(self): return self.evaluates_to_number() and self.is_constant() - @lru_cache - def evaluates_on_edges(self, dimension): + def evaluates_on_edges(self, dimension: str) -> bool: """ Returns True if a symbol evaluates on an edge, i.e. symbol contains a gradient operator, but not a divergence operator, and is not an IndefiniteIntegral. @@ -878,15 +915,20 @@ def evaluates_on_edges(self, dimension): Whether the symbol evaluates on edges (in the finite volume discretisation sense) """ - eval_on_edges = self._evaluates_on_edges(dimension) - self._saved_evaluates_on_edges[dimension] = eval_on_edges - return eval_on_edges + if dimension not in self._saved_evaluates_on_edges: + self._saved_evaluates_on_edges[dimension] = self._evaluates_on_edges( + dimension + ) + + return self._saved_evaluates_on_edges[dimension] def _evaluates_on_edges(self, dimension): # Default behaviour: return False return False - def has_symbol_of_classes(self, symbol_classes): + def has_symbol_of_classes( + self, symbol_classes: tuple[type[Symbol], ...] | type[Symbol] + ): """ Returns True if equation has a term of the class(es) `symbol_class`. @@ -897,30 +939,63 @@ def has_symbol_of_classes(self, symbol_classes): """ return any(isinstance(symbol, symbol_classes) for symbol in self.pre_order()) - def to_casadi(self, t=None, y=None, y_dot=None, inputs=None, casadi_symbols=None): + def to_casadi( + self, + t: casadi.MX | None = None, + y: casadi.MX | None = None, + y_dot: casadi.MX | None = None, + inputs: dict | None = None, + casadi_symbols: Symbol | None = None, + ): """ Convert the expression tree to a CasADi expression tree. See :class:`pybamm.CasadiConverter`. """ return pybamm.CasadiConverter(casadi_symbols).convert(self, t, y, y_dot, inputs) - def create_copy(self): + def _children_for_copying(self, children: list[Symbol] | None = None) -> Symbol: """ - Make a new copy of a symbol, to avoid Tree corruption errors while bypassing - copy.deepcopy(), which is slow. + Gets existing children for a symbol being copied if they aren't provided. """ - raise NotImplementedError( - f"""method self.new_copy() not implemented - for symbol {self!s} of type {type(self)}""" - ) + if children is None: + children = [child.create_copy() for child in self.children] + return children - def new_copy(self): + def create_copy( + self, + new_children: list[pybamm.Symbol] | None = None, + perform_simplifications: bool = True, + ): """ - Returns `create_copy` with added attributes + Make a new copy of a symbol, to avoid Tree corruption errors while bypassing + copy.deepcopy(), which is slow. + + If new_children are provided, they are used instead of the existing children. + + If `perform_simplifications` = True, some classes (e.g. `BinaryOperator`, + `UnaryOperator`, `Concatenation`) will perform simplifications and error checks + based on the new children before copying the symbol. This may result in a + different symbol being returned than the one copied. + + Turning off this behaviour to ensure the symbol remains unchanged is + discouraged. """ - obj = self.create_copy() - obj._print_name = self.print_name - return obj + children = self._children_for_copying(new_children) + return self.__class__(self.name, children, domains=self.domains) + + def new_copy( + self, + new_children: list[Symbol] | None = None, + perform_simplifications: bool = True, + ): + """ """ + warnings.warn( + "The 'new_copy' function for expression tree symbols is deprecated, use " + "'create_copy' instead.", + DeprecationWarning, + stacklevel=2, + ) + return self.create_copy(new_children, perform_simplifications) @cached_property def size(self): @@ -994,7 +1069,7 @@ def test_shape(self): try: self.shape_for_testing except ValueError as e: - raise pybamm.ShapeError(f"Cannot find shape (original error: {e})") + raise pybamm.ShapeError(f"Cannot find shape (original error: {e})") from e @property def print_name(self): @@ -1006,7 +1081,6 @@ def print_name(self, name): self._print_name = prettify_print_name(name) def to_equation(self): - sympy = have_optional_dependency("sympy") return sympy.Symbol(str(self.name)) def to_json(self): diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py index 950ac16318..ace1cd9942 100644 --- a/pybamm/expression_tree/unary_operators.py +++ b/pybamm/expression_tree/unary_operators.py @@ -1,12 +1,14 @@ # # Unary operator classes and methods # -import numbers +from __future__ import annotations import numpy as np from scipy.sparse import csr_matrix, issparse +import sympy import pybamm -from pybamm.util import have_optional_dependency +from pybamm.util import import_optional_dependency +from pybamm.type_definitions import DomainsType class UnaryOperator(pybamm.Symbol): @@ -22,10 +24,17 @@ class UnaryOperator(pybamm.Symbol): name of the node child : :class:`Symbol` child node + domains : dict + A dictionary equivalent to {'primary': domain, auxiliary_domains}. """ - def __init__(self, name, child, domains=None): - if isinstance(child, numbers.Number): + def __init__( + self, + name: str, + child: pybamm.Symbol, + domains: DomainsType = None, + ): + if isinstance(child, (float, int, np.number)): child = pybamm.Scalar(child) domains = domains or child.domains @@ -51,12 +60,23 @@ def __str__(self): """See :meth:`pybamm.Symbol.__str__()`.""" return f"{self.name}({self.child!s})" - def create_copy(self): + def create_copy( + self, + new_children: list[pybamm.Symbol] | None = None, + perform_simplifications: bool = True, + ): """See :meth:`pybamm.Symbol.new_copy()`.""" - new_child = self.child.new_copy() - return self._unary_new_copy(new_child) + if new_children and len(new_children) > 1: + raise ValueError( + f"Unary operator of type {type(self)} must have exactly one child." + ) + child = self._children_for_copying(new_children)[0] + + new_symbol = self._unary_new_copy(child, perform_simplifications) + new_symbol.copy_domains(self) + return new_symbol - def _unary_new_copy(self, child): + def _unary_new_copy(self, child, perform_simplifications=True): """Make a new copy of the unary operator, with child `child`""" return self.__class__(child) @@ -70,7 +90,13 @@ def _unary_evaluate(self, child): f"{self.__class__} does not implement _unary_evaluate." ) - def evaluate(self, t=None, y=None, y_dot=None, inputs=None): + def evaluate( + self, + t: float | None = None, + y: np.ndarray | None = None, + y_dot: np.ndarray | None = None, + inputs: dict | str | None = None, + ): """See :meth:`pybamm.Symbol.evaluate()`.""" child = self.child.evaluate(t, y, y_dot, inputs) return self._unary_evaluate(child) @@ -82,7 +108,7 @@ def _evaluate_for_shape(self): """ return self.children[0].evaluate_for_shape() - def _evaluates_on_edges(self, dimension): + def _evaluates_on_edges(self, dimension: str) -> bool: """See :meth:`pybamm.Symbol._evaluates_on_edges()`.""" return self.child.evaluates_on_edges(dimension) @@ -96,7 +122,6 @@ def _sympy_operator(self, child): def to_equation(self): """Convert the node and its subtree into a SymPy equation.""" - sympy = have_optional_dependency("sympy") if self.print_name is not None: return sympy.Symbol(self.print_name) else: @@ -117,7 +142,7 @@ def __str__(self): """See :meth:`pybamm.Symbol.__str__()`.""" return f"{self.name}{self.child!s}" - def _diff(self, variable): + def _diff(self, variable: pybamm.Symbol): """See :meth:`pybamm.Symbol._diff()`.""" return -self.child.diff(variable) @@ -129,9 +154,17 @@ def _unary_evaluate(self, child): """See :meth:`UnaryOperator._unary_evaluate()`.""" return -child - def _unary_new_copy(self, child): - """See :meth:`UnaryOperator._unary_new_copy()`.""" - return -child + def _unary_new_copy(self, child, perform_simplifications: bool = True): + """ + Creates a new copy of the operator with the child `child`. + + Uses the overridden :meth:`__neg__` to cover scenarios where the child + is some specific symbol types. + """ + if perform_simplifications: + return -child + else: + return Negate(child) class AbsoluteValue(UnaryOperator): @@ -155,9 +188,17 @@ def _unary_evaluate(self, child): """See :meth:`UnaryOperator._unary_evaluate()`.""" return np.abs(child) - def _unary_new_copy(self, child): - """See :meth:`UnaryOperator._unary_new_copy()`.""" - return abs(child) + def _unary_new_copy(self, child, perform_simplifications: bool = True): + """ + Creates a new copy of the operator with the child `child`. + + Uses the overridden :meth:`__abs__` to cover scenarios where the child + is some specific symbol types. + """ + if perform_simplifications: + return abs(child) + else: + return AbsoluteValue(child) class Sign(UnaryOperator): @@ -189,9 +230,17 @@ def _unary_evaluate(self, child): with np.errstate(invalid="ignore"): return np.sign(child) - def _unary_new_copy(self, child): - """See :meth:`UnaryOperator._unary_new_copy()`.""" - return sign(child) + def _unary_new_copy(self, child, perform_simplifications: bool = True): + """ + Creates a new copy of the operator with the child `child`. + + Uses the convenience function :meth:`sign` to cover scenarios where the child is + a concatenation or broadcast, and simplifies the symbol. + """ + if perform_simplifications: + return sign(child) + else: + return Sign(child) class Floor(UnaryOperator): @@ -293,21 +342,18 @@ def __init__(self, child, index, name=None, check_size=True): @classmethod def _from_json(cls, snippet: dict): """See :meth:`pybamm.UnaryOperator._from_json()`.""" - instance = cls.__new__(cls) - index = slice( snippet["index"]["start"], snippet["index"]["stop"], snippet["index"]["step"], ) - instance.__init__( + return cls( snippet["children"][0], index, name=snippet["name"], check_size=snippet["check_size"], ) - return instance def _unary_jac(self, child_jac): """See :meth:`pybamm.UnaryOperator._unary_jac()`.""" @@ -339,8 +385,9 @@ def _unary_evaluate(self, child): """See :meth:`UnaryOperator._unary_evaluate()`.""" return child[self.slice] - def _unary_new_copy(self, child): + def _unary_new_copy(self, child, perform_simplifications=True): """See :meth:`UnaryOperator._unary_new_copy()`.""" + # this new_index = self.__class__(child, self.index, check_size=False) # Keep same domains new_index.copy_domains(self) @@ -349,7 +396,7 @@ def _unary_new_copy(self, child): def _evaluate_for_shape(self): return self._unary_evaluate(self.children[0].evaluate_for_shape()) - def _evaluates_on_edges(self, dimension): + def _evaluates_on_edges(self, dimension: str) -> bool: """See :meth:`pybamm.Symbol._evaluates_on_edges()`.""" return False @@ -389,9 +436,16 @@ class with a :class:`Matrix` name of the node child : :class:`Symbol` child node + domains : dict + A dictionary equivalent to {'primary': domain, auxiliary_domains}. """ - def __init__(self, name, child, domains=None): + def __init__( + self, + name: str, + child: pybamm.Symbol, + domains: dict[str, list[str] | str] | None = None, + ): super().__init__(name, child, domains) def to_json(self): @@ -426,17 +480,27 @@ def __init__(self, child): ) super().__init__("grad", child) - def _evaluates_on_edges(self, dimension): + def _evaluates_on_edges(self, dimension: str) -> bool: """See :meth:`pybamm.Symbol._evaluates_on_edges()`.""" return True - def _unary_new_copy(self, child): - """See :meth:`UnaryOperator._unary_new_copy()`.""" - return grad(child) + def _unary_new_copy(self, child, perform_simplifications: bool = True): + """ + Creates a new copy of the operator with the child `child`. + + Uses the convenience function :meth:`grad` to cover scenarios where the gradient + is zero, or the child is a broadcast object. + """ + if perform_simplifications: + return grad(child) + else: + return Gradient(child) def _sympy_operator(self, child): """Override :meth:`pybamm.UnaryOperator._sympy_operator`""" - sympy_Gradient = have_optional_dependency("sympy.vector.operators", "Gradient") + sympy_Gradient = import_optional_dependency( + "sympy.vector.operators", "Gradient" + ) return sympy_Gradient(child) @@ -460,17 +524,25 @@ def __init__(self, child): ) super().__init__("div", child) - def _evaluates_on_edges(self, dimension): + def _evaluates_on_edges(self, dimension: str) -> bool: """See :meth:`pybamm.Symbol._evaluates_on_edges()`.""" return False - def _unary_new_copy(self, child): - """See :meth:`UnaryOperator._unary_new_copy()`.""" - return div(child) + def _unary_new_copy(self, child, perform_simplifications: bool = True): + """ + Creates a new copy of the operator with the child `child`. + + Uses the convenience function :meth:`div` to cover scenarios where divergence is + 0 or interacts with other functions. + """ + if perform_simplifications: + return div(child) + else: + return Divergence(child) def _sympy_operator(self, child): """Override :meth:`pybamm.UnaryOperator._sympy_operator`""" - sympy_Divergence = have_optional_dependency( + sympy_Divergence = import_optional_dependency( "sympy.vector.operators", "Divergence" ) return sympy_Divergence(child) @@ -485,7 +557,7 @@ class Laplacian(SpatialOperator): def __init__(self, child): super().__init__("laplacian", child) - def _evaluates_on_edges(self, dimension): + def _evaluates_on_edges(self, dimension: str) -> bool: """See :meth:`pybamm.Symbol._evaluates_on_edges()`.""" return False @@ -501,7 +573,7 @@ class GradientSquared(SpatialOperator): def __init__(self, child): super().__init__("grad squared", child) - def _evaluates_on_edges(self, dimension): + def _evaluates_on_edges(self, dimension: str) -> bool: """See :meth:`pybamm.Symbol._evaluates_on_edges()`.""" return False @@ -551,7 +623,13 @@ class Integral(SpatialOperator): The variable over which to integrate """ - def __init__(self, child, integration_variable): + def __init__( + self, + child, + integration_variable: ( + list[pybamm.IndependentVariable] | pybamm.IndependentVariable + ), + ): if not isinstance(integration_variable, list): integration_variable = [integration_variable] @@ -637,22 +715,20 @@ def set_id(self): ) ) - def _unary_new_copy(self, child): + def _unary_new_copy(self, child, perform_simplifications=True): """See :meth:`UnaryOperator._unary_new_copy()`.""" - return self.__class__(child, self.integration_variable) def _evaluate_for_shape(self): """See :meth:`pybamm.Symbol.evaluate_for_shape_using_domain()`""" return pybamm.evaluate_for_shape_using_domain(self.domains) - def _evaluates_on_edges(self, dimension): + def _evaluates_on_edges(self, dimension: str) -> bool: """See :meth:`pybamm.Symbol._evaluates_on_edges()`.""" return False def _sympy_operator(self, child): """Override :meth:`pybamm.UnaryOperator._sympy_operator`""" - sympy = have_optional_dependency("sympy") return sympy.Integral(child, sympy.Symbol("xn")) @@ -736,9 +812,7 @@ class BackwardIndefiniteIntegral(BaseIndefiniteIntegral): def __init__(self, child, integration_variable): super().__init__(child, integration_variable) # Overwrite the name - self.name = "{} integrated backward w.r.t {}".format( - child.name, self.integration_variable[0].name - ) + self.name = f"{child.name} integrated backward w.r.t {self.integration_variable[0].name}" if isinstance(integration_variable, pybamm.SpatialVariable): self.name += f" on {self.integration_variable[0].domain}" @@ -782,9 +856,8 @@ def set_id(self): ) ) - def _unary_new_copy(self, child): + def _unary_new_copy(self, child, perform_simplifications=True): """See :meth:`UnaryOperator._unary_new_copy()`.""" - return self.__class__(child, vector_type=self.vector_type) def _evaluate_for_shape(self): @@ -835,16 +908,15 @@ def set_id(self): (self.__class__, self.name, self.children[0].id, *tuple(self.domain)) ) - def _unary_new_copy(self, child): + def _unary_new_copy(self, child, perform_simplifications=True): """See :meth:`UnaryOperator._unary_new_copy()`.""" - return self.__class__(child, region=self.region) def _evaluate_for_shape(self): """See :meth:`pybamm.Symbol.evaluate_for_shape_using_domain()`""" return pybamm.evaluate_for_shape_using_domain(self.domains) - def _evaluates_on_edges(self, dimension): + def _evaluates_on_edges(self, dimension: str) -> bool: """See :meth:`pybamm.Symbol._evaluates_on_edges()`.""" return False @@ -882,11 +954,11 @@ def set_id(self): ) ) - def _evaluates_on_edges(self, dimension): + def _evaluates_on_edges(self, dimension: str) -> bool: """See :meth:`pybamm.Symbol._evaluates_on_edges()`.""" return False - def _unary_new_copy(self, child): + def _unary_new_copy(self, child, perform_simplifications=True): """See :meth:`UnaryOperator._unary_new_copy()`.""" return self.__class__(child, self.side, self.domain) @@ -945,7 +1017,7 @@ def set_id(self): ) ) - def _unary_new_copy(self, child): + def _unary_new_copy(self, child, perform_simplifications=True): """See :meth:`UnaryOperator._unary_new_copy()`.""" return self.__class__(child, self.side) @@ -970,13 +1042,20 @@ class BoundaryValue(BoundaryOperator): def __init__(self, child, side): super().__init__("boundary value", child, side) - def _unary_new_copy(self, child): - """See :meth:`UnaryOperator._unary_new_copy()`.""" - return boundary_value(child, self.side) + def _unary_new_copy(self, child, perform_simplifications: bool = True): + """ + Creates a new copy of the operator with the child `child`. + + Uses the convenience function :meth:`boundary_value` to perform checks before + creating a BoundaryValue object. + """ + if perform_simplifications: + return boundary_value(child, self.side) + else: + return BoundaryValue(child, self.side) def _sympy_operator(self, child): """Override :meth:`pybamm.UnaryOperator._sympy_operator`""" - sympy = have_optional_dependency("sympy") if ( self.child.domain[0] in ["negative particle", "positive particle"] and self.side == "right" @@ -1003,13 +1082,9 @@ def __init__(self, children, initial_condition): @classmethod def _from_json(cls, snippet: dict): - instance = cls.__new__(cls) - - instance.__init__(snippet["children"][0], snippet["initial_condition"]) - - return instance + return cls(snippet["children"][0], snippet["initial_condition"]) - def _unary_new_copy(self, child): + def _unary_new_copy(self, child, perform_simplifications=True): return self.__class__(child, self.initial_condition) def is_constant(self): @@ -1091,7 +1166,7 @@ def _unary_jac(self, child_jac): """See :meth:`pybamm.UnaryOperator._unary_jac()`.""" return pybamm.Scalar(0) - def _unary_new_copy(self, child): + def _unary_new_copy(self, child, perform_simplifications=True): """See :meth:`UnaryOperator._unary_new_copy()`.""" return self.__class__(child, self.position) @@ -1119,7 +1194,7 @@ def __init__(self, name, child): ) super().__init__(name, child) - def _evaluates_on_edges(self, dimension): + def _evaluates_on_edges(self, dimension: str) -> bool: """See :meth:`pybamm.Symbol._evaluates_on_edges()`.""" return True @@ -1148,11 +1223,7 @@ class NotConstant(UnaryOperator): def __init__(self, child): super().__init__("not_constant", child) - def _unary_new_copy(self, child): - """See :meth:`pybamm.Symbol.new_copy()`.""" - return NotConstant(child) - - def _diff(self, variable): + def _diff(self, variable: pybamm.Symbol): """See :meth:`pybamm.Symbol._diff()`.""" return self.child.diff(variable) @@ -1198,6 +1269,11 @@ def grad(symbol): else: new_child = pybamm.PrimaryBroadcast(0, symbol.child.domain) return pybamm.PrimaryBroadcastToEdges(new_child, symbol.domain) + elif isinstance(symbol, pybamm.SecondaryBroadcast): + # Take gradient of the child + # then broadcast back to the originalsymbol's secondary domain + # We can do this because gradient only acts on the primary domain + return pybamm.SecondaryBroadcast(grad(symbol.child), symbol.secondary_domain) elif isinstance(symbol, pybamm.FullBroadcast): return pybamm.FullBroadcastToEdges(0, broadcast_domains=symbol.domains) else: @@ -1234,8 +1310,6 @@ def div(symbol): left, right = symbol.orphans if isinstance(left, pybamm.Negate): return -div(symbol._binary_new_copy(left.orphans[0], right)) - # elif isinstance(right, pybamm.Negate): - # return -div(symbol._binary_new_copy(left, right.orphans[0])) # Last resort return Divergence(symbol) diff --git a/pybamm/expression_tree/variable.py b/pybamm/expression_tree/variable.py index 35193782e3..4d08686245 100644 --- a/pybamm/expression_tree/variable.py +++ b/pybamm/expression_tree/variable.py @@ -1,11 +1,17 @@ # # Variable class # - +from __future__ import annotations import numpy as np import numbers import pybamm -from pybamm.util import have_optional_dependency +import sympy +from pybamm.type_definitions import ( + DomainType, + AuxiliaryDomainType, + DomainsType, + Numeric, +) class VariableBase(pybamm.Symbol): @@ -49,14 +55,14 @@ class VariableBase(pybamm.Symbol): def __init__( self, - name, - domain=None, - auxiliary_domains=None, - domains=None, - bounds=None, - print_name=None, - scale=1, - reference=0, + name: str, + domain: DomainType = None, + auxiliary_domains: AuxiliaryDomainType = None, + domains: DomainsType = None, + bounds: tuple[pybamm.Symbol] | None = None, + print_name: str | None = None, + scale: float | pybamm.Symbol | None = 1, + reference: float | pybamm.Symbol | None = 0, ): if isinstance(scale, numbers.Number): scale = pybamm.Scalar(scale) @@ -82,7 +88,7 @@ def bounds(self): return self._bounds @bounds.setter - def bounds(self, values): + def bounds(self, values: tuple[Numeric, Numeric]): if values is None: values = (-np.inf, np.inf) else: @@ -112,7 +118,11 @@ def set_id(self): ) ) - def create_copy(self): + def create_copy( + self, + new_children=None, + perform_simplifications=True, + ): """See :meth:`pybamm.Symbol.new_copy()`.""" return self.__class__( self.name, @@ -129,7 +139,6 @@ def _evaluate_for_shape(self): def to_equation(self): """Convert the node and its subtree into a SymPy equation.""" - sympy = have_optional_dependency("sympy") if self.print_name is not None: return sympy.Symbol(self.print_name) else: @@ -183,7 +192,7 @@ class Variable(VariableBase): Default is 0. """ - def diff(self, variable): + def diff(self, variable: pybamm.Symbol): if variable == self: return pybamm.Scalar(1) elif variable == pybamm.t: @@ -237,7 +246,7 @@ class VariableDot(VariableBase): Default is 0. """ - def get_variable(self): + def get_variable(self) -> pybamm.Variable: """ return a :class:`.Variable` corresponding to this VariableDot @@ -246,7 +255,7 @@ def get_variable(self): """ return Variable(self.name[:-1], domains=self.domains, scale=self.scale) - def diff(self, variable): + def diff(self, variable: pybamm.Symbol) -> pybamm.Scalar: if variable == self: return pybamm.Scalar(1) elif variable == pybamm.t: diff --git a/pybamm/expression_tree/vector.py b/pybamm/expression_tree/vector.py index 641c098f79..6dc358afb0 100644 --- a/pybamm/expression_tree/vector.py +++ b/pybamm/expression_tree/vector.py @@ -1,9 +1,11 @@ # # Vector class # +from __future__ import annotations import numpy as np import pybamm +from pybamm.type_definitions import DomainType, AuxiliaryDomainType, DomainsType class Vector(pybamm.Array): @@ -13,13 +15,13 @@ class Vector(pybamm.Array): def __init__( self, - entries, - name=None, - domain=None, - auxiliary_domains=None, - domains=None, - entries_string=None, - ): + entries: np.ndarray | list[float] | np.matrix, + name: str | None = None, + domain: DomainType = None, + auxiliary_domains: AuxiliaryDomainType = None, + domains: DomainsType = None, + entries_string: str | None = None, + ) -> None: if isinstance(entries, (list, np.matrix)): entries = np.array(entries) # make sure that entries are a vector (can be a column vector) @@ -27,9 +29,9 @@ def __init__( entries = entries[:, np.newaxis] if entries.shape[1] != 1: raise ValueError( + f""" + Entries must have 1 dimension or be column vector, not have shape {entries.shape} """ - Entries must have 1 dimension or be column vector, not have shape {} - """.format(entries.shape) ) if name is None: name = f"Column vector of length {entries.shape[0]!s}" diff --git a/pybamm/geometry/__init__.py b/pybamm/geometry/__init__.py index e69de29bb2..d763174ffb 100644 --- a/pybamm/geometry/__init__.py +++ b/pybamm/geometry/__init__.py @@ -0,0 +1 @@ +__all__ = ['battery_geometry', 'geometry', 'standard_spatial_vars'] diff --git a/pybamm/geometry/battery_geometry.py b/pybamm/geometry/battery_geometry.py index e15c358128..9be08ff619 100644 --- a/pybamm/geometry/battery_geometry.py +++ b/pybamm/geometry/battery_geometry.py @@ -135,8 +135,8 @@ def battery_geometry( } else: raise pybamm.GeometryError( - "Invalid current collector dimension '{}' (should be 0 or 1 for " - "a 'cylindrical' battery geometry)".format(current_collector_dimension) + f"Invalid current collector dimension '{current_collector_dimension}' (should be 0 or 1 for " + "a 'cylindrical' battery geometry)" ) else: raise pybamm.GeometryError( diff --git a/pybamm/input/__init__.py b/pybamm/input/__init__.py index e69de29bb2..916888ce34 100644 --- a/pybamm/input/__init__.py +++ b/pybamm/input/__init__.py @@ -0,0 +1 @@ +__all__ = ['parameters'] diff --git a/pybamm/input/comsol_results/comsol_01C.pickle b/pybamm/input/comsol_results/comsol_01C.pickle deleted file mode 100644 index 4888663b01..0000000000 Binary files a/pybamm/input/comsol_results/comsol_01C.pickle and /dev/null differ diff --git a/pybamm/input/comsol_results/comsol_05C.pickle b/pybamm/input/comsol_results/comsol_05C.pickle deleted file mode 100644 index b498d707ee..0000000000 Binary files a/pybamm/input/comsol_results/comsol_05C.pickle and /dev/null differ diff --git a/pybamm/input/comsol_results/comsol_1C.pickle b/pybamm/input/comsol_results/comsol_1C.pickle deleted file mode 100644 index 69a997d654..0000000000 Binary files a/pybamm/input/comsol_results/comsol_1C.pickle and /dev/null differ diff --git a/pybamm/input/comsol_results/comsol_1plus1D_3C.pickle b/pybamm/input/comsol_results/comsol_1plus1D_3C.pickle deleted file mode 100644 index 85af9b637e..0000000000 Binary files a/pybamm/input/comsol_results/comsol_1plus1D_3C.pickle and /dev/null differ diff --git a/pybamm/input/comsol_results/comsol_2C.pickle b/pybamm/input/comsol_results/comsol_2C.pickle deleted file mode 100644 index fdf55ad156..0000000000 Binary files a/pybamm/input/comsol_results/comsol_2C.pickle and /dev/null differ diff --git a/pybamm/input/comsol_results/comsol_3C.pickle b/pybamm/input/comsol_results/comsol_3C.pickle deleted file mode 100644 index e6f65accf8..0000000000 Binary files a/pybamm/input/comsol_results/comsol_3C.pickle and /dev/null differ diff --git a/pybamm/input/discharge_data/Ecker2015/Ecker_1C.csv b/pybamm/input/discharge_data/Ecker2015/Ecker_1C.csv deleted file mode 100644 index dbd1215881..0000000000 --- a/pybamm/input/discharge_data/Ecker2015/Ecker_1C.csv +++ /dev/null @@ -1,31 +0,0 @@ -20.3084233101775,4.10984760218981 -137.255118370379,4.06170981679401 -247.454888715569,4.02086563524606 -393.638257540821,3.98148017446767 -530.825726746058,3.94063599291972 -670.262170856298,3.91146157752832 -820.943489491555,3.87499355828908 -955.88198379179,3.84581914289769 -1097.56740280703,3.81226856519758 -1243.75077163228,3.79038775365404 -1383.18721574253,3.76121333826264 -1529.37058456778,3.73641508517996 -1666.55805377301,3.7247453190234 -1808.24347278826,3.70578194901899 -1947.6799168985,3.69848834517114 -2087.11636100874,3.68827729978416 -2231.05075492899,3.67514881285803 -2379.48309865925,3.66056160516233 -2521.16851767449,3.63576335207965 -2658.35598687973,3.60075405360997 -2802.29038079997,3.55407498898374 -2939.47785000521,3.51031336589665 -3081.16326902045,3.47384534665741 -3225.0976629407,3.43008372357031 -3344.29333290591,3.38194593817451 -3470.23592758612,3.30755117892646 -3553.44799907126,3.21273432890442 -3616.41929641137,3.11500003734325 -3652.40289489144,3.01872446655165 -3688.3864933715,2.89910936344693 -3715.37419223154,2.76636577341609 diff --git a/pybamm/input/discharge_data/Ecker2015/Ecker_5C.csv b/pybamm/input/discharge_data/Ecker2015/Ecker_5C.csv deleted file mode 100644 index 621281ddff..0000000000 --- a/pybamm/input/discharge_data/Ecker2015/Ecker_5C.csv +++ /dev/null @@ -1,33 +0,0 @@ -0,4.00772806063782 -2.10996257174233,3.9630519063978 -9.53641973294314,3.90466605332464 -22.2674891521443,3.85148732048119 -40.7157517827462,3.80418814691602 -60.9764236014983,3.75781321732131 -82.7105988252504,3.72697310974463 -103.708022346502,3.69851401469471 -126.547325124005,3.67723564231538 -149.386627901507,3.65476333775538 -175.172937489009,3.63351237271449 -204.643005589013,3.60393814158189 -277.21304828527,3.55566182788309 -341.310446402776,3.51924603989306 -391.409562172782,3.4910575923102 -409.091603032784,3.47331305363065 -430.457402405285,3.45679670630482 -454.401832736538,3.43791647603876 -477.60951136529,3.41067186867334 -499.712062440292,3.38938664445941 -530.287258094045,3.35265909799465 -551.653057466548,3.32539736104271 -574.123984392801,3.29098230875862 -594.384656211552,3.25535276879001 -615.013703881554,3.214950926016 -634.169248146556,3.17572931175344 -650.377785601557,3.11737737526159 -664.37606794906,3.06736240853066 -677.63759859406,3.00540126815833 -685.373491470311,2.94219480684576 -693.846136049062,2.87660733300644 -701.213653074063,2.79787632742773 -706.002539140314,2.71195774734383 diff --git a/pybamm/input/discharge_data/Ecker2015/README.md b/pybamm/input/discharge_data/Ecker2015/README.md deleted file mode 100644 index 16555613cb..0000000000 --- a/pybamm/input/discharge_data/Ecker2015/README.md +++ /dev/null @@ -1,11 +0,0 @@ -# Kokam SLPB 75106100 discharge data from Ecker et al (2015) - -Experimental data (Time [s], Voltage [V]) for a 1C and 5C constant current discharge of a Kokam SLPB 75106100 cell at a temperature of 25°C from - -> Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery II. Model validation." Journal of The Electrochemical Society 162.9 (2015): A1849-A1857. - -The data were collected from Figure 8 of - -> Richardson, Giles, et. al. "Generalised single particle models for high-rate operation of graded lithium-ion electrodes: Systematic derivation and validation." Electrochemica Acta 339 (2020): 135862 - -using [WebPlotDigitizer](https://automeris.io/WebPlotDigitizer/). diff --git a/pybamm/input/discharge_data/Enertech_cells/0.1C_discharge_U.txt b/pybamm/input/discharge_data/Enertech_cells/0.1C_discharge_U.txt deleted file mode 100644 index 987192474c..0000000000 --- a/pybamm/input/discharge_data/Enertech_cells/0.1C_discharge_U.txt +++ /dev/null @@ -1,36880 +0,0 @@ -0 4.181482004 -1 4.175568142 -2 4.173469674 -3 4.173088135 -4 4.172897365 -5 4.172515826 -6 4.172325056 -7 4.172325056 -8 4.172325056 -9 4.172134286 -10 4.171943516 -11 4.171943516 -12 4.171561977 -13 4.171371207 -14 4.171180437 -15 4.171180437 -16 4.170989668 -17 4.170989668 -18 4.170989668 -19 4.170989668 -20 4.170798898 -21 4.170608128 -22 4.170608128 -23 4.170417359 -24 4.170226589 -25 4.170035819 -26 4.170035819 -27 4.170035819 -28 4.170035819 -29 4.169845049 -30 4.169845049 -31 4.16965428 -32 4.16965428 -33 4.16927274 -34 4.16927274 -35 4.16927274 -36 4.16908197 -37 4.16927274 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b/pybamm/input/discharge_data/Enertech_cells/README.md deleted file mode 100644 index 3fe724034e..0000000000 --- a/pybamm/input/discharge_data/Enertech_cells/README.md +++ /dev/null @@ -1,9 +0,0 @@ -# Enertech cells - discharge results for beginning of life - -Experimental results of voltage, temperature rise and thickness change for the Enertech cells at 0.5, 1 and 2 C discharge. The default units are "V", "K" and "m" respectively for the second column. For the first column, it is time with the unit of "s". - -> Ai, W., Kraft, L., Sturm, J., Jossen, A., & Wu, B. (2020). Electrochemical Thermal-Mechanical Modelling of Stress Inhomogeneity in Lithium-Ion Pouch Cells. Journal of The Electrochemical Society, 167(1), 013512. DOI: 10.1149/2.0122001JES. - -> Rieger, B., Erhard, S. V., Rumpf, K., & Jossen, A. (2016). A new method to model the thickness change of a commercial pouch cell during discharge. Journal of The Electrochemical Society, 163(8), A1566-A1575. - -and references therein. diff --git a/pybamm/input/discharge_data/Enertech_cells/stn_2C.txt b/pybamm/input/discharge_data/Enertech_cells/stn_2C.txt deleted file mode 100755 index 1517501646..0000000000 --- a/pybamm/input/discharge_data/Enertech_cells/stn_2C.txt +++ /dev/null @@ -1,896 +0,0 @@ -# Negative particle surface tangetial stress close to the separator -# for the Enertech cells Ai2020 JES -# time [s] and normalised stress sigma_t_surf_n/E_n -0,-1.0741e-17 -2,0.00031276 -4,0.00043323 -6,0.00051944 -8,0.00058904 -10,0.00064844 -12,0.00070099 -14,0.00074829 -16,0.0007915 -18,0.00083158 -20,0.00086605 -22,0.00089617 -24,0.00092722 -26,0.00095722 -28,0.00098602 -30,0.0010136 -32,0.0010401 -34,0.0010655 -36,0.0010899 -38,0.0011133 -40,0.0011357 -42,0.0011572 -44,0.0011779 -46,0.0011979 -48,0.001217 -50,0.0012354 -52,0.0012532 -54,0.0012678 -56,0.0012777 -58,0.0012907 -60,0.0013042 -62,0.0013175 -64,0.0013308 -66,0.0013436 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2190ad2fbf..0000000000 --- a/pybamm/input/discharge_data/Enertech_cells/stp_2C.txt +++ /dev/null @@ -1,896 +0,0 @@ -# Positive particle surface tangetial stress close to the separator -# for the Enertech cells Ai2020 JES -# time [s] and normalised stress sigma_t_surf_p/E_p -0,2.207e-18 -2,8.026e-05 -4,0.00011677 -6,0.00014216 -8,0.00016234 -10,0.00017923 -12,0.00019457 -14,0.00020834 -16,0.0002196 -18,0.0002295 -20,0.00023826 -22,0.00024607 -24,0.00025367 -26,0.00026146 -28,0.00026824 -30,0.00027409 -32,0.00027938 -34,0.0002842 -36,0.00028862 -38,0.0002929 -40,0.0002975 -42,0.00030175 -44,0.00030518 -46,0.00030817 -48,0.00031096 -50,0.00031359 -52,0.00031614 -54,0.00031888 -56,0.00032194 -58,0.00032458 -60,0.00032665 -62,0.00032839 -64,0.00033003 -66,0.00033189 -68,0.00033398 -70,0.00033571 -72,0.00033704 -74,0.00033814 -76,0.00033918 -78,0.00034041 -80,0.00034197 -82,0.00034344 -84,0.00034443 -86,0.00034512 -88,0.0003458 -90,0.00034677 -92,0.00034781 -94,0.00034857 -96,0.00034901 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deleted file mode 100644 index 8ddf18dbe1..0000000000 --- a/pybamm/input/drive_cycles/UDDS.csv +++ /dev/null @@ -1,1373 +0,0 @@ -# UDDS profile from SLIDE https://github.com/davidhowey/SLIDE/blob/master/Current%20Profile%20drive%20cycle%20UDDS.csv -# with a maximum current of 8.1A -# Time [s], Current [A] -0,0.030392 -1,0.030392 -2,0.030392 -3,0.030392 -4,0.030392 -5,0.030392 -6,0.030392 -7,0.030392 -8,0.030392 -9,0.030392 -10,0.030392 -11,0.030392 -12,0.030392 -13,0.030392 -14,0.030392 -15,0.030392 -16,0.030392 -17,0.030392 -18,0.030392 -19,0.030392 -20,0.030392 -21,0.3823 -22,1.1017 -23,1.665 -24,2.4526 -25,3.0432 -26,3.4383 -27,0.87647 -28,1.452 -29,4.2781 -30,2.0705 -31,1.647 -32,0.64251 -33,-0.14223 -34,-0.37147 -35,-0.36196 -36,-0.24336 -37,-0.34419 -38,-2.4679 -39,-1.5457 -40,0.30511 -41,0.65534 -42,0.66836 -43,0.92799 -44,1.7388 -45,3.1551 -46,3.5063 -47,3.1505 -48,0.82876 -49,0.12776 -50,0.30059 -51,-1.1905 -52,-2.1659 -53,-1.5575 -54,-0.89464 -55,0.32348 -56,2.7903 -57,3.4132 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-1339,0.88564 -1340,1.7821 -1341,2.6801 -1342,1.9564 -1343,2.3575 -1344,2.4115 -1345,2.3841 -1346,2.1329 -1347,1.6352 -1348,2.0306 -1349,1.4463 -1350,0.79757 -1351,0.97831 -1352,-0.14173 -1353,-0.13972 -1354,-0.25086 -1355,-0.35555 -1356,-0.239 -1357,-0.12877 -1358,-0.83321 -1359,-0.69193 -1360,-0.65352 -1361,-0.61481 -1362,-0.95819 -1363,-1.8067 -1364,-1.3678 -1365,-0.8718 -1366,-0.4758 -1367,-0.12072 -1368,0.030392 -1369,0.030392 diff --git a/pybamm/input/drive_cycles/US06.csv b/pybamm/input/drive_cycles/US06.csv deleted file mode 100644 index e534a5db0f..0000000000 --- a/pybamm/input/drive_cycles/US06.csv +++ /dev/null @@ -1,603 +0,0 @@ -# Based on the US06 drive cycle, -# time [s],current [A] -0,0.012859 -1,0.012859 -2,0.012859 -3,0.012859 -4,0.012859 -5,0.012859 -6,0.013668 -7,0.023524 -8,0.03126 -9,0.050602 -10,0.58049 -11,2.6467 -12,3.8424 -13,4.1121 -14,-0.23677 -15,3.1954 -16,4.134 -17,2.8259 -18,2.5489 -19,2.6931 -20,2.3144 -21,3.5036 -22,2.7033 -23,1.057 -24,-0.44999 -25,-0.4451 -26,-1.8319 -27,-0.6912 -28,-0.41631 -29,0.39479 -30,1.4094 -31,-0.067283 -32,0.40082 -33,-1.6765 -34,-3.0094 -35,-2.9658 -36,-2.8139 -37,-2.1029 -38,-1.7886 -39,-1.0515 -40,-0.33154 -41,-0.00027238 -42,0.012859 -43,0.012859 -44,0.012859 -45,0.012859 -46,0.012859 -47,0.012859 -48,0.012859 -49,0.026246 -50,1.4175 -51,2.3404 -52,1.9203 -53,2.8051 -54,4.2382 -55,4.2743 -56,2.8574 -57,4.2758 -58,4.9662 -59,4.8662 -60,3.8788 -61,2.521 -62,3.5651 -63,3.3848 -64,3.6869 -65,3.1176 -66,2.8487 -67,2.1836 -68,0.18726 -69,0.91501 -70,0.0053402 -71,-0.37207 -72,-0.37225 -73,0.33741 -74,1.0426 -75,0.33741 -76,-0.37219 -77,-0.96565 -78,0.13474 -79,0.46979 -80,0.46799 -81,0.80472 -82,0.80717 -83,1.4932 -84,2.2074 -85,3.3239 -86,4.1708 -87,4.5251 -88,4.3083 -89,4.2568 -90,4.8156 -91,5.2028 -92,4.5034 -93,4.6358 -94,3.3841 -95,1.5935 -96,1.3703 -97,0.22134 -98,-2.5008 -99,-1.8485 -100,-1.6727 -101,-1.0737 -102,-1.065 -103,-1.4701 -104,-1.7157 -105,-1.5508 -106,-1.6473 -107,-1.3644 -108,-1.5766 -109,-1.4219 -110,-1.4997 -111,-1.242 -112,-1.4213 -113,-1.9789 -114,-2.4495 -115,-1.771 -116,-0.67 -117,-0.65407 -118,-0.79789 -119,-3.6676 -120,-3.2481 -121,-2.1471 -122,-1.5972 -123,-1.3075 -124,-0.80837 -125,-0.29408 -126,-0.039143 -127,-0.012101 -128,-0.00027238 -129,0.012859 -130,0.012859 -131,0.012859 -132,0.012859 -133,0.012859 -134,0.012859 -135,0.012859 -136,0.13367 -137,1.3164 -138,2.9506 -139,4.3345 -140,5.7205 -141,5.4552 -142,5.8237 -143,6.0151 -144,3.7866 -145,2.7655 -146,2.3944 -147,2.948 -148,1.8924 -149,2.5999 -150,1.8149 -151,1.3287 -152,1.3425 -153,2.057 -154,0.67879 -155,0.67879 -156,2.0956 -157,3.7768 -158,1.8459 -159,3.0051 -160,1.3509 -161,3.1006 -162,1.4034 -163,1.4141 -164,1.4248 -165,-0.50076 -166,-1.2951 -167,0.40453 -168,0.21185 -169,-0.11201 -170,1.1354 -171,-0.62707 -172,1.4858 -173,0.011324 -174,0.55864 -175,0.18866 -176,1.1018 -177,1.1079 -178,0.37419 -179,0.18726 -180,0.5484 -181,0.54637 -182,-0.86914 -183,-0.24991 -184,1.3988 -185,-2.4102 -186,5.0098 -187,2.5027 -188,3.1156 -189,2.6317 -190,0.96709 -191,3.2753 -192,1.7871 -193,2.998 -194,1.6541 -195,1.8721 -196,2.0978 -197,0.89478 -198,1.3038 -199,-0.078976 -200,1.4977 -201,2.1259 -202,0.089321 -203,1.1053 -204,1.3141 -205,3.1926 -206,0.52137 -207,1.3521 -208,1.3591 -209,1.3661 -210,1.7966 -211,1.8134 -212,2.4758 -213,0.995 -214,1.8602 -215,-2.6906 -216,-0.067612 -217,1.5503 -218,1.3521 -219,0.10446 -220,2.1829 -221,0.73539 -222,0.31463 -223,1.5617 -224,-0.49752 -225,2.1685 -226,0.7278 -227,1.1428 -228,0.51918 -229,1.3486 -230,-0.35495 -231,1.124 -232,1.9596 -233,1.1428 -234,1.5655 -235,1.9998 -236,1.5964 -237,-0.93064 -238,1.3556 -239,-0.92625 -240,2.1543 -241,-0.21363 -242,2.1543 -243,0.51263 -244,-0.07259 -245,1.1146 -246,0.70538 -247,1.5276 -248,1.124 -249,0.7128 -250,-0.49887 -251,0.89478 -252,1.7142 -253,0.70048 -254,-0.21884 -255,1.2937 -256,0.68831 -257,0.88929 -258,1.093 -259,0.28185 -260,1.698 -261,1.5089 -262,-0.078194 -263,0.88929 -264,1.093 -265,-0.081297 -266,2.7118 -267,0.90306 -268,-0.078194 -269,1.9111 -270,0.084907 -271,0.28185 -272,0.47868 -273,0.67631 -274,1.4829 -275,0.88383 -276,0.88383 -277,0.6811 -278,1.694 -279,0.48492 -280,0.88656 -281,1.2937 -282,1.5051 -283,0.69559 -284,3.1671 -285,2.1829 -286,1.9998 -287,1.5964 -288,2.6805 -289,1.4233 -290,2.9549 -291,1.4636 -292,1.6914 -293,2.8152 -294,1.0654 -295,2.1852 -296,1.757 -297,1.7695 -298,6.4018 -299,2.1159 -300,7.9136 -301,-2.9379 -302,0.26072 -303,1.1932 -304,0.72206 -305,2.3667 -306,1.9156 -307,0.97617 -308,-0.62823 -309,0.24381 -310,0.46769 -311,1.156 -312,0.69207 -313,1.3812 -314,1.1527 -315,1.6173 -316,3.2663 -317,2.3718 -318,3.5953 -319,4.151 -320,3.4975 -321,5.3167 -322,4.1756 -323,3.7387 -324,-0.94696 -325,-2.8522 -326,0.33794 -327,3.3004 -328,4.3626 -329,2.4058 -330,4.2276 -331,2.4839 -332,2.505 -333,1.4794 -334,2.5263 -335,1.4955 -336,-0.40618 -337,1.4633 -338,0.42687 -339,0.41621 -340,-0.94842 -341,-0.95058 -342,-2.8125 -343,-1.9414 -344,-2.0775 -345,-4.2071 -346,-1.091 -347,1.6593 -348,-0.19359 -349,0.5667 -350,1.6394 -351,0.78484 -352,3.174 -353,1.6874 -354,3.0325 -355,1.0654 -356,1.9606 -357,-0.023889 -358,0.83956 -359,0.83676 -360,-0.63943 -361,-0.34073 -362,0.35759 -363,0.13671 -364,1.6315 -365,1.8602 -366,0.14178 -367,1.6434 -368,2.5283 -369,2.1238 -370,1.9253 -371,1.4972 -372,1.9517 -373,1.9695 -374,0.41043 -375,2.8783 -376,1.1003 -377,1.1003 -378,2.4667 -379,2.0372 -380,1.5935 -381,1.8329 -382,-0.15832 -383,1.8201 -384,0.44887 -385,-0.16389 -386,1.5661 -387,1.5739 -388,-0.0085663 -389,0.42601 -390,2.4615 -391,0.66037 -392,0.65518 -393,2.0145 -394,1.8031 -395,1.1263 -396,1.1263 -397,2.9716 -398,0.68939 -399,1.3775 -400,0.68671 -401,1.6054 -402,1.8458 -403,1.3922 -404,0.0053429 -405,2.074 -406,2.3266 -407,1.6454 -408,2.8354 -409,0.49679 -410,1.1966 -411,0.72483 -412,0.95438 -413,0.9513 -414,2.8354 -415,1.682 -416,2.1679 -417,-0.95265 -418,-0.14664 -419,-0.15257 -420,0.00062616 -421,1.363 -422,2.0601 -423,-0.31705 -424,2.9716 -425,-0.47505 -426,1.3558 -427,-1.1047 -428,0.41485 -429,-0.17703 -430,1.2852 -431,0.84237 -432,-0.029129 -433,0.16447 -434,1.4748 -435,1.2612 -436,1.0436 -437,0.16268 -438,0.59278 -439,-0.34126 -440,1.6553 -441,0.1452 -442,1.4342 -443,-0.046997 -444,1.4233 -445,-0.19793 -446,-4.0792 -447,0.88656 -448,-0.50003 -449,0.26455 -450,1.4573 -451,0.26455 -452,1.4573 -453,0.86493 -454,1.67 -455,0.87569 -456,0.87569 -457,0.87569 -458,1.686 -459,0.076253 -460,1.686 -461,1.7021 -462,0.081997 -463,1.7021 -464,0.081997 -465,1.7021 -466,0.89753 -467,1.7183 -468,0.087843 -469,0.081997 -470,-0.9152 -471,-1.5834 -472,-0.36787 -473,-2.0567 -474,-0.87672 -475,-1.4815 -476,-2.6241 -477,-2.178 -478,-0.036534 -479,-0.90616 -480,-2.4373 -481,-2.9694 -482,-3.5147 -483,-1.8457 -484,-0.98341 -485,-2.4483 -486,-3.8294 -487,-1.9441 -488,-0.048456 -489,-1.0641 -490,-1.0035 -491,-0.66563 -492,-0.32669 -493,-0.027036 -494,0.012859 -495,0.012859 -496,0.012859 -497,0.012859 -498,0.012859 -499,0.012859 -500,0.012859 -501,0.013668 -502,0.34446 -503,1.4125 -504,2.4117 -505,3.237 -506,3.4134 -507,2.4722 -508,0.71203 -509,-0.68697 -510,-0.9946 -511,-1.1241 -512,-2.2309 -513,-1.7637 -514,-0.84058 -515,0.20754 -516,1.1729 -517,1.9691 -518,2.7371 -519,3.2787 -520,2.6509 -521,1.5214 -522,-0.0058875 -523,-1.3985 -524,-1.9241 -525,-1.808 -526,-1.6276 -527,-0.95015 -528,-0.20711 -529,-0.2798 -530,-0.026526 -531,0.059693 -532,0.71684 -533,1.6183 -534,2.4505 -535,3.2363 -536,3.4634 -537,1.8132 -538,0.52817 -539,-0.37846 -540,-1.398 -541,-1.9658 -542,-1.8804 -543,-1.5482 -544,-0.64195 -545,-0.15352 -546,-0.0033494 -547,1.1079 -548,1.9138 -549,2.325 -550,3.0374 -551,3.3647 -552,3.7312 -553,0.086116 -554,-2.2017 -555,-2.3495 -556,-2.0339 -557,-1.4035 -558,-0.91224 -559,-0.37758 -560,-0.056562 -561,0.012859 -562,0.012859 -563,0.012859 -564,0.012859 -565,0.012859 -566,0.012859 -567,0.012859 -568,0.014561 -569,0.70309 -570,2.0434 -571,3.5113 -572,3.4062 -573,3.9559 -574,7.1727 -575,3.4216 -576,3.9417 -577,6.9749 -578,8.1 -579,3.415 -580,-0.024227 -581,-0.25555 -582,-0.3689 -583,-1.9022 -584,-2.9537 -585,-2.95 -586,-2.7289 -587,-3.271 -588,-3.5216 -589,-2.3332 -590,-2.2084 -591,-2.0047 -592,-1.3769 -593,-0.34366 -594,-0.035978 -595,0.012859 -596,0.012859 -597,0.012859 -598,0.012859 -599,0.012859 -600,0.012859 diff --git a/pybamm/input/drive_cycles/WLTC.csv b/pybamm/input/drive_cycles/WLTC.csv deleted file mode 100644 index e201013fb5..0000000000 --- a/pybamm/input/drive_cycles/WLTC.csv +++ /dev/null @@ -1,18004 +0,0 @@ -# Based on the WLTC drive cycle -# see #1264 for discussion and how to use this drive cycle -# time [s],Power [kW] -0,0.000511 -0.1,0.000511 -0.2,0.000511 -0.3,0.000511 -0.4,0.000511 -0.5,0.000511 -0.6,0.000511 -0.7,0.000511 -0.8,0.000511 -0.9,0.000511 -1,0.000511 -1.1,0.291064 -1.2,0.291064 -1.3,0.291064 -1.4,0.291064 -1.5,0.291064 -1.6,0.291064 -1.7,0.291064 -1.8,0.291064 -1.9,0.291064 -2,0.291064 -2.1,0.291064 -2.2,0.291064 -2.3,0.291064 -2.4,0.291064 -2.5,0.291064 -2.6,0.291064 -2.7,0.291064 -2.8,0.291064 -2.9,0.291064 -3,0.291064 -3.1,0.291064 -3.2,0.291064 -3.3,0.291064 -3.4,0.291064 -3.5,0.291064 -3.6,0.291064 -3.7,0.291064 -3.8,0.291064 -3.9,0.291064 -4,0.291064 -4.1,0.291064 -4.2,0.291064 -4.3,0.291064 -4.4,0.291064 -4.5,0.291064 -4.6,0.291064 -4.7,0.291064 -4.8,0.291064 -4.9,0.291064 -5,0.291064 -5.1,0.291064 -5.2,0.291064 -5.3,0.291064 -5.4,0.291064 -5.5,0.291064 -5.6,0.291064 -5.7,0.291064 -5.8,0.291064 -5.9,0.291064 -6,0.291064 -6.1,0.291064 -6.2,0.291064 -6.3,0.291064 -6.4,0.291064 -6.5,0.291064 -6.6,0.291064 -6.7,0.291064 -6.8,0.291064 -6.9,0.291064 -7,0.291064 -7.1,0.291064 -7.2,0.291064 -7.3,0.291064 -7.4,0.291064 -7.5,0.291064 -7.6,0.291064 -7.7,0.291064 -7.8,0.291064 -7.9,0.291064 -8,0.291064 -8.1,0.291064 -8.2,0.291064 -8.3,0.291064 -8.4,0.291064 -8.5,0.291064 -8.6,0.291064 -8.7,0.291064 -8.8,0.291064 -8.9,0.291064 -9,0.291064 -9.1,0.291064 -9.2,0.291064 -9.3,0.291064 -9.4,0.291064 -9.5,0.291064 -9.6,0.291064 -9.7,0.291064 -9.8,0.291064 -9.9,0.291064 -10,0.291064 -10.1,0.291068 -10.2,0.291083 -10.3,0.291097 -10.4,0.319311 -10.5,0.349797 -10.6,0.378041 -10.7,0.405047 -10.8,0.431971 -10.9,0.413776 -11,0.428219 -11.1,0.457475 -11.2,0.475251 -11.3,0.493025 -11.4,0.740568 -11.5,0.812319 -11.6,0.770088 -11.7,0.816719 -11.8,0.863346 -11.9,0.543614 -12,0.550118 -12.1,1.547174 -12.2,1.727778 -12.3,1.894542 -12.4,2.138306 -12.5,2.311462 -12.6,1.47735 -12.7,1.529 -12.8,1.58064 -12.9,2.054573 -13,2.143176 -13.1,4.506061 -13.2,4.904914 -13.3,5.299318 -13.4,6.635977 -13.5,7.165182 -13.6,7.110738 -13.7,7.562674 -13.8,8.014499 -13.9,6.320979 -14,6.572931 -14.1,6.375914 -14.2,6.594412 -14.3,6.812782 -14.4,5.852581 -14.5,6.00166 -14.6,4.457538 -14.7,4.531535 -14.8,4.605527 -14.9,3.777398 -15,3.822993 -15.1,1.227041 -15.2,1.225398 -15.3,1.22669 -15.4,1.46233 -15.5,1.465168 -15.6,3.182719 -15.7,3.210937 -15.8,3.239152 -15.9,4.434841 -16,4.492472 -16.1,7.00414 -16.2,7.15243 -16.3,7.300708 -16.4,8.571899 -16.5,8.770652 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-1798,0.291072 -1798.1,0.291072 -1798.2,0.291071 -1798.3,0.29107 -1798.4,0.29107 -1798.5,0.291069 -1798.6,0.291068 -1798.7,0.291068 -1798.8,0.291067 -1798.9,0.291066 -1799,0.291066 -1799.1,0.291065 -1799.2,0.291065 -1799.3,0.291064 -1799.4,0.291064 -1799.5,0.291064 -1799.6,0.291064 -1799.7,0.291065 -1799.8,0.291065 -1799.9,0.291065 -1800,0.291065 diff --git a/pybamm/input/drive_cycles/car_current.csv b/pybamm/input/drive_cycles/car_current.csv deleted file mode 100644 index 3d23ca51bc..0000000000 --- a/pybamm/input/drive_cycles/car_current.csv +++ /dev/null @@ -1,16 +0,0 @@ -# This is adapted from the file getCarCurrent.m which is part of the LIONSIMBA toolbox., -# time [s], current [A] -0, 1 -50, 1 -50.001, -0.5 -60, -0.5 -60.001, 0.5 -210, 0.5 -210.001, 1 -410, 1 -410.001, 2 -415, 2 -415.001, 1.25 -615, 1.25 -615.001, -0.5 -3600, -0.5 diff --git a/pybamm/input/parameters/__init__.py b/pybamm/input/parameters/__init__.py index e69de29bb2..9ef23b743d 100644 --- a/pybamm/input/parameters/__init__.py +++ b/pybamm/input/parameters/__init__.py @@ -0,0 +1 @@ +__all__ = ['ecm', 'lead_acid', 'lithium_ion'] diff --git a/pybamm/input/parameters/ecm/__init__.py b/pybamm/input/parameters/ecm/__init__.py index e69de29bb2..65332e5c24 100644 --- a/pybamm/input/parameters/ecm/__init__.py +++ b/pybamm/input/parameters/ecm/__init__.py @@ -0,0 +1 @@ +__all__ = ['example_set'] diff --git a/pybamm/input/parameters/lead_acid/__init__.py b/pybamm/input/parameters/lead_acid/__init__.py index e69de29bb2..608e2b68b7 100644 --- a/pybamm/input/parameters/lead_acid/__init__.py +++ b/pybamm/input/parameters/lead_acid/__init__.py @@ -0,0 +1 @@ +__all__ = ['Sulzer2019'] diff --git a/pybamm/input/parameters/lithium_ion/Ai2020.py b/pybamm/input/parameters/lithium_ion/Ai2020.py index d13f7fb0db..b45c04fa7f 100644 --- a/pybamm/input/parameters/lithium_ion/Ai2020.py +++ b/pybamm/input/parameters/lithium_ion/Ai2020.py @@ -549,6 +549,8 @@ def get_parameter_values(): "Lithium interstitial reference concentration [mol.m-3]": 15.0, "Initial inner SEI thickness [m]": 2.5e-09, "Initial outer SEI thickness [m]": 2.5e-09, + "Initial inner SEI on cracks thickness [m]": 2.5e-13, # avoid division by zero + "Initial outer SEI on cracks thickness [m]": 2.5e-13, # avoid division by zero "EC initial concentration in electrolyte [mol.m-3]": 4541.0, "EC diffusivity [m2.s-1]": 2e-18, "SEI kinetic rate constant [m.s-1]": 1e-12, @@ -582,7 +584,7 @@ def get_parameter_values(): # negative electrode "Negative electrode conductivity [S.m-1]": 100.0, "Maximum concentration in negative electrode [mol.m-3]": 28700.0, - "Negative electrode diffusivity [m2.s-1]": graphite_diffusivity_Dualfoil1998, + "Negative particle diffusivity [m2.s-1]": graphite_diffusivity_Dualfoil1998, "Negative electrode OCP [V]": graphite_ocp_Enertech_Ai2020, "Negative electrode porosity": 0.33, "Negative electrode active material volume fraction": 0.61, @@ -617,7 +619,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 10.0, "Maximum concentration in positive electrode [mol.m-3]": 49943.0, - "Positive electrode diffusivity [m2.s-1]": lico2_diffusivity_Dualfoil1998, + "Positive particle diffusivity [m2.s-1]": lico2_diffusivity_Dualfoil1998, "Positive electrode OCP [V]": lico2_ocp_Ai2020, "Positive electrode porosity": 0.32, "Positive electrode active material volume fraction": 0.62, diff --git a/pybamm/input/parameters/lithium_ion/Chen2020.py b/pybamm/input/parameters/lithium_ion/Chen2020.py index 0b5420baaf..5a7460871b 100644 --- a/pybamm/input/parameters/lithium_ion/Chen2020.py +++ b/pybamm/input/parameters/lithium_ion/Chen2020.py @@ -273,7 +273,7 @@ def get_parameter_values(): # negative electrode "Negative electrode conductivity [S.m-1]": 215.0, "Maximum concentration in negative electrode [mol.m-3]": 33133.0, - "Negative electrode diffusivity [m2.s-1]": 3.3e-14, + "Negative particle diffusivity [m2.s-1]": 3.3e-14, "Negative electrode OCP [V]": graphite_LGM50_ocp_Chen2020, "Negative electrode porosity": 0.25, "Negative electrode active material volume fraction": 0.75, @@ -291,7 +291,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 0.18, "Maximum concentration in positive electrode [mol.m-3]": 63104.0, - "Positive electrode diffusivity [m2.s-1]": 4e-15, + "Positive particle diffusivity [m2.s-1]": 4e-15, "Positive electrode OCP [V]": nmc_LGM50_ocp_Chen2020, "Positive electrode porosity": 0.335, "Positive electrode active material volume fraction": 0.665, diff --git a/pybamm/input/parameters/lithium_ion/Chen2020_composite.py b/pybamm/input/parameters/lithium_ion/Chen2020_composite.py index 69767cbddf..58b6211072 100644 --- a/pybamm/input/parameters/lithium_ion/Chen2020_composite.py +++ b/pybamm/input/parameters/lithium_ion/Chen2020_composite.py @@ -394,7 +394,7 @@ def get_parameter_values(): "Negative electrode conductivity [S.m-1]": 215.0, "Primary: Maximum concentration in negative electrode [mol.m-3]": 28700.0, "Primary: Initial concentration in negative electrode [mol.m-3]": 27700.0, - "Primary: Negative electrode diffusivity [m2.s-1]": 5.5e-14, + "Primary: Negative particle diffusivity [m2.s-1]": 5.5e-14, "Primary: Negative electrode OCP [V]": graphite_ocp_Enertech_Ai2020, "Negative electrode porosity": 0.25, "Primary: Negative electrode active material volume fraction": 0.735, @@ -411,7 +411,7 @@ def get_parameter_values(): "Primary: Negative electrode OCP entropic change [V.K-1]": 0.0, "Secondary: Maximum concentration in negative electrode [mol.m-3]": 278000.0, "Secondary: Initial concentration in negative electrode [mol.m-3]": 276610.0, - "Secondary: Negative electrode diffusivity [m2.s-1]": 1.67e-14, + "Secondary: Negative particle diffusivity [m2.s-1]": 1.67e-14, "Secondary: Negative electrode lithiation OCP [V]" "": silicon_ocp_lithiation_Mark2016, "Secondary: Negative electrode delithiation OCP [V]" @@ -425,7 +425,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 0.18, "Maximum concentration in positive electrode [mol.m-3]": 63104.0, - "Positive electrode diffusivity [m2.s-1]": 4e-15, + "Positive particle diffusivity [m2.s-1]": 4e-15, "Positive electrode OCP [V]": nmc_LGM50_ocp_Chen2020, "Positive electrode porosity": 0.335, "Positive electrode active material volume fraction": 0.665, diff --git a/pybamm/input/parameters/lithium_ion/Ecker2015.py b/pybamm/input/parameters/lithium_ion/Ecker2015.py index 28b2ca21e4..32cc631293 100644 --- a/pybamm/input/parameters/lithium_ion/Ecker2015.py +++ b/pybamm/input/parameters/lithium_ion/Ecker2015.py @@ -293,6 +293,97 @@ def nco_electrolyte_exchange_current_density_Ecker2015(c_e, c_s_surf, c_s_max, T return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 +def plating_exchange_current_density_OKane2020(c_e, c_Li, T): + """ + Exchange-current density for Li plating reaction [A.m-2]. + References + ---------- + .. [1] O’Kane, Simon EJ, Ian D. Campbell, Mohamed WJ Marzook, Gregory J. Offer, and + Monica Marinescu. "Physical origin of the differential voltage minimum associated + with lithium plating in Li-ion batteries." Journal of The Electrochemical Society + 167, no. 9 (2020): 090540. + Parameters + ---------- + c_e : :class:`pybamm.Symbol` + Electrolyte concentration [mol.m-3] + c_Li : :class:`pybamm.Symbol` + Plated lithium concentration [mol.m-3] + T : :class:`pybamm.Symbol` + Temperature [K] + Returns + ------- + :class:`pybamm.Symbol` + Exchange-current density [A.m-2] + """ + + k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]") + + return pybamm.constants.F * k_plating * c_e + + +def stripping_exchange_current_density_OKane2020(c_e, c_Li, T): + """ + Exchange-current density for Li stripping reaction [A.m-2]. + + References + ---------- + + .. [1] O’Kane, Simon EJ, Ian D. Campbell, Mohamed WJ Marzook, Gregory J. Offer, and + Monica Marinescu. "Physical origin of the differential voltage minimum associated + with lithium plating in Li-ion batteries." Journal of The Electrochemical Society + 167, no. 9 (2020): 090540. + + Parameters + ---------- + + c_e : :class:`pybamm.Symbol` + Electrolyte concentration [mol.m-3] + c_Li : :class:`pybamm.Symbol` + Plated lithium concentration [mol.m-3] + T : :class:`pybamm.Symbol` + Temperature [K] + + Returns + ------- + + :class:`pybamm.Symbol` + Exchange-current density [A.m-2] + """ + + k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]") + + return pybamm.constants.F * k_plating * c_Li + + +def SEI_limited_dead_lithium_OKane2022(L_sei): + """ + Decay rate for dead lithium formation [s-1]. + References + ---------- + .. [1] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diega Alonso-Alvarez, + Robert Timms, Valentin Sulzer, Jaqueline Sophie Edge, Billy Wu, Gregory J. Offer + and Monica Marinescu. "Lithium-ion battery degradation: how to model it." + Physical Chemistry: Chemical Physics 24, no. 13 (2022): 7909-7922. + Parameters + ---------- + L_sei : :class:`pybamm.Symbol` + Total SEI thickness [m] + Returns + ------- + :class:`pybamm.Symbol` + Dead lithium decay rate [s-1] + """ + + gamma_0 = pybamm.Parameter("Dead lithium decay constant [s-1]") + L_inner_0 = pybamm.Parameter("Initial inner SEI thickness [m]") + L_outer_0 = pybamm.Parameter("Initial outer SEI thickness [m]") + L_sei_0 = L_inner_0 + L_outer_0 + + gamma = gamma_0 * L_sei_0 / L_sei + + return gamma + + def electrolyte_diffusivity_Ecker2015(c_e, T): """ Diffusivity of LiPF6 in EC:DMC as a function of ion concentration [1, 2, 3]. @@ -409,6 +500,18 @@ def get_parameter_values(): return { "chemistry": "lithium_ion", + # lithium plating + "Lithium metal partial molar volume [m3.mol-1]": 1.3e-05, + "Lithium plating kinetic rate constant [m.s-1]": 1e-10, + "Exchange-current density for plating [A.m-2]" + "": plating_exchange_current_density_OKane2020, + "Exchange-current density for stripping [A.m-2]" + "": stripping_exchange_current_density_OKane2020, + "Initial plated lithium concentration [mol.m-3]": 0.0, + "Typical plated lithium concentration [mol.m-3]": 1000.0, + "Lithium plating transfer coefficient": 0.5, + "Dead lithium decay constant [s-1]": 1e-06, + "Dead lithium decay rate [s-1]": SEI_limited_dead_lithium_OKane2022, # sei "Ratio of lithium moles to SEI moles": 2.0, "Inner SEI reaction proportion": 0.5, @@ -462,7 +565,7 @@ def get_parameter_values(): # negative electrode "Negative electrode conductivity [S.m-1]": 14.0, "Maximum concentration in negative electrode [mol.m-3]": 31920.0, - "Negative electrode diffusivity [m2.s-1]": graphite_diffusivity_Ecker2015, + "Negative particle diffusivity [m2.s-1]": graphite_diffusivity_Ecker2015, "Negative electrode OCP [V]": graphite_ocp_Ecker2015, "Negative electrode porosity": 0.329, "Negative electrode active material volume fraction": 0.372403, @@ -478,7 +581,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 68.1, "Maximum concentration in positive electrode [mol.m-3]": 48580.0, - "Positive electrode diffusivity [m2.s-1]": nco_diffusivity_Ecker2015, + "Positive particle diffusivity [m2.s-1]": nco_diffusivity_Ecker2015, "Positive electrode OCP [V]": nco_ocp_Ecker2015, "Positive electrode porosity": 0.296, "Positive electrode active material volume fraction": 0.40832, @@ -530,5 +633,6 @@ def get_parameter_values(): "Zhao2018", "Hales2019", "Richardson2020", + "OKane2020", ], } diff --git a/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py b/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py index 441ae95b8b..365bb6386c 100644 --- a/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py +++ b/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py @@ -180,6 +180,97 @@ def graphite_electrolyte_exchange_current_density_Ecker2015(c_e, c_s_surf, c_s_m return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 +def plating_exchange_current_density_OKane2020(c_e, c_Li, T): + """ + Exchange-current density for Li plating reaction [A.m-2]. + References + ---------- + .. [1] O’Kane, Simon EJ, Ian D. Campbell, Mohamed WJ Marzook, Gregory J. Offer, and + Monica Marinescu. "Physical origin of the differential voltage minimum associated + with lithium plating in Li-ion batteries." Journal of The Electrochemical Society + 167, no. 9 (2020): 090540. + Parameters + ---------- + c_e : :class:`pybamm.Symbol` + Electrolyte concentration [mol.m-3] + c_Li : :class:`pybamm.Symbol` + Plated lithium concentration [mol.m-3] + T : :class:`pybamm.Symbol` + Temperature [K] + Returns + ------- + :class:`pybamm.Symbol` + Exchange-current density [A.m-2] + """ + + k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]") + + return pybamm.constants.F * k_plating * c_e + + +def stripping_exchange_current_density_OKane2020(c_e, c_Li, T): + """ + Exchange-current density for Li stripping reaction [A.m-2]. + + References + ---------- + + .. [1] O’Kane, Simon EJ, Ian D. Campbell, Mohamed WJ Marzook, Gregory J. Offer, and + Monica Marinescu. "Physical origin of the differential voltage minimum associated + with lithium plating in Li-ion batteries." Journal of The Electrochemical Society + 167, no. 9 (2020): 090540. + + Parameters + ---------- + + c_e : :class:`pybamm.Symbol` + Electrolyte concentration [mol.m-3] + c_Li : :class:`pybamm.Symbol` + Plated lithium concentration [mol.m-3] + T : :class:`pybamm.Symbol` + Temperature [K] + + Returns + ------- + + :class:`pybamm.Symbol` + Exchange-current density [A.m-2] + """ + + k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]") + + return pybamm.constants.F * k_plating * c_Li + + +def SEI_limited_dead_lithium_OKane2022(L_sei): + """ + Decay rate for dead lithium formation [s-1]. + References + ---------- + .. [1] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diega Alonso-Alvarez, + Robert Timms, Valentin Sulzer, Jaqueline Sophie Edge, Billy Wu, Gregory J. Offer + and Monica Marinescu. "Lithium-ion battery degradation: how to model it." + Physical Chemistry: Chemical Physics 24, no. 13 (2022): 7909-7922. + Parameters + ---------- + L_sei : :class:`pybamm.Symbol` + Total SEI thickness [m] + Returns + ------- + :class:`pybamm.Symbol` + Dead lithium decay rate [s-1] + """ + + gamma_0 = pybamm.Parameter("Dead lithium decay constant [s-1]") + L_inner_0 = pybamm.Parameter("Initial inner SEI thickness [m]") + L_outer_0 = pybamm.Parameter("Initial outer SEI thickness [m]") + L_sei_0 = L_inner_0 + L_outer_0 + + gamma = gamma_0 * L_sei_0 / L_sei + + return gamma + + def electrolyte_diffusivity_Ecker2015(c_e, T): """ Diffusivity of LiPF6 in EC:DMC as a function of ion concentration [1, 2, 3]. @@ -332,6 +423,17 @@ def get_parameter_values(): return { "chemistry": "lithium_ion", + # lithium plating + "Lithium plating kinetic rate constant [m.s-1]": 1e-10, + "Exchange-current density for plating [A.m-2]" + "": plating_exchange_current_density_OKane2020, + "Exchange-current density for stripping [A.m-2]" + "": stripping_exchange_current_density_OKane2020, + "Initial plated lithium concentration [mol.m-3]": 0.0, + "Typical plated lithium concentration [mol.m-3]": 1000.0, + "Lithium plating transfer coefficient": 0.5, + "Dead lithium decay constant [s-1]": 1e-06, + "Dead lithium decay rate [s-1]": SEI_limited_dead_lithium_OKane2022, # sei "Ratio of lithium moles to SEI moles": 2.0, "Inner SEI reaction proportion": 0.5, @@ -386,7 +488,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 14.0, "Maximum concentration in positive electrode [mol.m-3]": 31920.0, - "Positive electrode diffusivity [m2.s-1]": graphite_diffusivity_Ecker2015, + "Positive particle diffusivity [m2.s-1]": graphite_diffusivity_Ecker2015, "Positive electrode OCP [V]": graphite_ocp_Ecker2015, "Positive electrode porosity": 0.329, "Positive electrode active material volume fraction": 0.372403, @@ -435,5 +537,6 @@ def get_parameter_values(): "Hales2019", "Xu2019", "Richardson2020", + "OKane2020", ], } diff --git a/pybamm/input/parameters/lithium_ion/MSMR_example_set.py b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py index fce5c7f068..55033431bd 100644 --- a/pybamm/input/parameters/lithium_ion/MSMR_example_set.py +++ b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py @@ -136,7 +136,7 @@ def get_parameter_values(): "j0_ref_n_5": 2.7, "Negative electrode conductivity [S.m-1]": 215.0, "Maximum concentration in negative electrode [mol.m-3]": 33133.0, - "Negative electrode diffusivity [m2.s-1]": 3.3e-14, + "Negative particle diffusivity [m2.s-1]": 3.3e-14, "Negative electrode porosity": 0.25, "Negative electrode active material volume fraction": 0.75, "Negative particle radius [m]": 5.86e-06, @@ -167,7 +167,7 @@ def get_parameter_values(): "j0_ref_p_3": 1e6, "Positive electrode conductivity [S.m-1]": 0.18, "Maximum concentration in positive electrode [mol.m-3]": 63104.0, - "Positive electrode diffusivity [m2.s-1]": 4e-15, + "Positive particle diffusivity [m2.s-1]": 4e-15, "Positive electrode porosity": 0.335, "Positive electrode active material volume fraction": 0.665, "Positive particle radius [m]": 5.22e-06, diff --git a/pybamm/input/parameters/lithium_ion/Marquis2019.py b/pybamm/input/parameters/lithium_ion/Marquis2019.py index 1664e6b1b2..b1f63e6ff7 100644 --- a/pybamm/input/parameters/lithium_ion/Marquis2019.py +++ b/pybamm/input/parameters/lithium_ion/Marquis2019.py @@ -399,7 +399,7 @@ def get_parameter_values(): # negative electrode "Negative electrode conductivity [S.m-1]": 100.0, "Maximum concentration in negative electrode [mol.m-3]": 24983.2619938437, - "Negative electrode diffusivity [m2.s-1]" + "Negative particle diffusivity [m2.s-1]" "": graphite_mcmb2528_diffusivity_Dualfoil1998, "Negative electrode OCP [V]": graphite_mcmb2528_ocp_Dualfoil1998, "Negative electrode porosity": 0.3, @@ -419,7 +419,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 10.0, "Maximum concentration in positive electrode [mol.m-3]": 51217.9257309275, - "Positive electrode diffusivity [m2.s-1]": lico2_diffusivity_Dualfoil1998, + "Positive particle diffusivity [m2.s-1]": lico2_diffusivity_Dualfoil1998, "Positive electrode OCP [V]": lico2_ocp_Dualfoil1998, "Positive electrode porosity": 0.3, "Positive electrode active material volume fraction": 0.5, diff --git a/pybamm/input/parameters/lithium_ion/Mohtat2020.py b/pybamm/input/parameters/lithium_ion/Mohtat2020.py index 86f14e39a2..9923d9d308 100644 --- a/pybamm/input/parameters/lithium_ion/Mohtat2020.py +++ b/pybamm/input/parameters/lithium_ion/Mohtat2020.py @@ -389,7 +389,7 @@ def get_parameter_values(): # negative electrode "Negative electrode conductivity [S.m-1]": 100.0, "Maximum concentration in negative electrode [mol.m-3]": 28746.0, - "Negative electrode diffusivity [m2.s-1]": graphite_diffusivity_PeymanMPM, + "Negative particle diffusivity [m2.s-1]": graphite_diffusivity_PeymanMPM, "Negative electrode OCP [V]": graphite_ocp_PeymanMPM, "Negative electrode porosity": 0.3, "Negative electrode active material volume fraction": 0.61, @@ -411,7 +411,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 100.0, "Maximum concentration in positive electrode [mol.m-3]": 35380.0, - "Positive electrode diffusivity [m2.s-1]": NMC_diffusivity_PeymanMPM, + "Positive particle diffusivity [m2.s-1]": NMC_diffusivity_PeymanMPM, "Positive electrode OCP [V]": NMC_ocp_PeymanMPM, "Positive electrode porosity": 0.3, "Positive electrode active material volume fraction": 0.445, diff --git a/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py b/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py index 123714a9da..7d0478b6d0 100644 --- a/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py +++ b/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py @@ -362,7 +362,7 @@ def get_parameter_values(): # negative electrode "Negative electrode conductivity [S.m-1]": 100.0, "Maximum concentration in negative electrode [mol.m-3]": 28700.0, - "Negative electrode diffusivity [m2.s-1]": graphite_diffusivity_Kim2011, + "Negative particle diffusivity [m2.s-1]": graphite_diffusivity_Kim2011, "Negative electrode OCP [V]": graphite_ocp_Kim2011, "Negative electrode porosity": 0.4, "Negative electrode active material volume fraction": 0.51, @@ -380,7 +380,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 10.0, "Maximum concentration in positive electrode [mol.m-3]": 49000.0, - "Positive electrode diffusivity [m2.s-1]": nca_diffusivity_Kim2011, + "Positive particle diffusivity [m2.s-1]": nca_diffusivity_Kim2011, "Positive electrode OCP [V]": nca_ocp_Kim2011, "Positive electrode porosity": 0.4, "Positive electrode active material volume fraction": 0.41, diff --git a/pybamm/input/parameters/lithium_ion/OKane2022.py b/pybamm/input/parameters/lithium_ion/OKane2022.py index 930848268f..b1e852dbdf 100644 --- a/pybamm/input/parameters/lithium_ion/OKane2022.py +++ b/pybamm/input/parameters/lithium_ion/OKane2022.py @@ -536,6 +536,8 @@ def get_parameter_values(): "Lithium interstitial reference concentration [mol.m-3]": 15.0, "Initial inner SEI thickness [m]": 0.0, "Initial outer SEI thickness [m]": 5e-09, + "Initial inner SEI on cracks thickness [m]": 0, + "Initial outer SEI on cracks thickness [m]": 5e-13, # avoid division by zero "EC initial concentration in electrolyte [mol.m-3]": 4541.0, "EC diffusivity [m2.s-1]": 2e-18, "SEI kinetic rate constant [m.s-1]": 1e-12, @@ -568,7 +570,7 @@ def get_parameter_values(): # negative electrode "Negative electrode conductivity [S.m-1]": 215.0, "Maximum concentration in negative electrode [mol.m-3]": 33133.0, - "Negative electrode diffusivity [m2.s-1]": graphite_LGM50_diffusivity_Chen2020, + "Negative particle diffusivity [m2.s-1]": graphite_LGM50_diffusivity_Chen2020, "Negative electrode OCP [V]": graphite_LGM50_ocp_Chen2020, "Negative electrode porosity": 0.25, "Negative electrode active material volume fraction": 0.75, @@ -601,7 +603,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 0.18, "Maximum concentration in positive electrode [mol.m-3]": 63104.0, - "Positive electrode diffusivity [m2.s-1]": nmc_LGM50_diffusivity_Chen2020, + "Positive particle diffusivity [m2.s-1]": nmc_LGM50_diffusivity_Chen2020, "Positive electrode OCP [V]": nmc_LGM50_ocp_Chen2020, "Positive electrode porosity": 0.335, "Positive electrode active material volume fraction": 0.665, diff --git a/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py b/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py index 31081af14a..35533ba80e 100644 --- a/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py +++ b/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py @@ -422,6 +422,8 @@ def get_parameter_values(): "Lithium interstitial reference concentration [mol.m-3]": 15.0, "Initial inner SEI thickness [m]": 0.0, "Initial outer SEI thickness [m]": 5e-09, + "Initial inner SEI on cracks thickness [m]": 0, + "Initial outer SEI on cracks thickness [m]": 5e-13, # avoid division by zero "EC initial concentration in electrolyte [mol.m-3]": 4541.0, "EC diffusivity [m2.s-1]": 2e-18, "SEI kinetic rate constant [m.s-1]": 1e-12, @@ -458,7 +460,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 215.0, "Maximum concentration in positive electrode [mol.m-3]": 33133.0, - "Positive electrode diffusivity [m2.s-1]": graphite_LGM50_diffusivity_Chen2020, + "Positive particle diffusivity [m2.s-1]": graphite_LGM50_diffusivity_Chen2020, "Positive electrode OCP [V]": graphite_LGM50_ocp_Chen2020, "Positive electrode porosity": 0.25, "Positive electrode active material volume fraction": 0.75, diff --git a/pybamm/input/parameters/lithium_ion/ORegan2022.py b/pybamm/input/parameters/lithium_ion/ORegan2022.py index 3ea7ab06ce..3ca5f6824c 100644 --- a/pybamm/input/parameters/lithium_ion/ORegan2022.py +++ b/pybamm/input/parameters/lithium_ion/ORegan2022.py @@ -953,7 +953,7 @@ def get_parameter_values(): # negative electrode "Negative electrode conductivity [S.m-1]": 215.0, "Maximum concentration in negative electrode [mol.m-3]": 29583.0, - "Negative electrode diffusivity [m2.s-1]" + "Negative particle diffusivity [m2.s-1]" "": graphite_LGM50_diffusivity_ORegan2022, "Negative electrode OCP [V]": graphite_LGM50_ocp_Chen2020, "Negative electrode porosity": 0.25, @@ -976,7 +976,7 @@ def get_parameter_values(): "Positive electrode conductivity [S.m-1]" "": nmc_LGM50_electronic_conductivity_ORegan2022, "Maximum concentration in positive electrode [mol.m-3]": 51765.0, - "Positive electrode diffusivity [m2.s-1]": nmc_LGM50_diffusivity_ORegan2022, + "Positive particle diffusivity [m2.s-1]": nmc_LGM50_diffusivity_ORegan2022, "Positive electrode OCP [V]": nmc_LGM50_ocp_Chen2020, "Positive electrode porosity": 0.335, "Positive electrode active material volume fraction": 0.665, diff --git a/pybamm/input/parameters/lithium_ion/Prada2013.py b/pybamm/input/parameters/lithium_ion/Prada2013.py index 421256af2a..0ba56516ab 100644 --- a/pybamm/input/parameters/lithium_ion/Prada2013.py +++ b/pybamm/input/parameters/lithium_ion/Prada2013.py @@ -183,7 +183,7 @@ def get_parameter_values(): # negative electrode "Negative electrode conductivity [S.m-1]": 215.0, "Maximum concentration in negative electrode [mol.m-3]": 30555, - "Negative electrode diffusivity [m2.s-1]": 3e-15, + "Negative particle diffusivity [m2.s-1]": 3e-15, "Negative electrode OCP [V]": graphite_LGM50_ocp_Chen2020, "Negative electrode porosity": 0.36, "Negative electrode active material volume fraction": 0.58, @@ -198,7 +198,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 0.33795074, "Maximum concentration in positive electrode [mol.m-3]": 22806.0, - "Positive electrode diffusivity [m2.s-1]": 5.9e-18, + "Positive particle diffusivity [m2.s-1]": 5.9e-18, "Positive electrode OCP [V]": LFP_ocp_Afshar2017, "Positive electrode porosity": 0.426, "Positive electrode active material volume fraction": 0.374, diff --git a/pybamm/input/parameters/lithium_ion/Ramadass2004.py b/pybamm/input/parameters/lithium_ion/Ramadass2004.py index 4269acf1e9..879a5f55c6 100644 --- a/pybamm/input/parameters/lithium_ion/Ramadass2004.py +++ b/pybamm/input/parameters/lithium_ion/Ramadass2004.py @@ -408,7 +408,7 @@ def get_parameter_values(): # negative electrode "Negative electrode conductivity [S.m-1]": 100.0, "Maximum concentration in negative electrode [mol.m-3]": 30555.0, - "Negative electrode diffusivity [m2.s-1]" + "Negative particle diffusivity [m2.s-1]" "": graphite_mcmb2528_diffusivity_Dualfoil1998, "Negative electrode OCP [V]": graphite_ocp_Ramadass2004, "Negative electrode porosity": 0.485, @@ -428,7 +428,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 100.0, "Maximum concentration in positive electrode [mol.m-3]": 51555.0, - "Positive electrode diffusivity [m2.s-1]": lico2_diffusivity_Ramadass2004, + "Positive particle diffusivity [m2.s-1]": lico2_diffusivity_Ramadass2004, "Positive electrode OCP [V]": lico2_ocp_Ramadass2004, "Positive electrode porosity": 0.385, "Positive electrode active material volume fraction": 0.59, diff --git a/pybamm/input/parameters/lithium_ion/Xu2019.py b/pybamm/input/parameters/lithium_ion/Xu2019.py index d96afc3f04..edf3bd40b0 100644 --- a/pybamm/input/parameters/lithium_ion/Xu2019.py +++ b/pybamm/input/parameters/lithium_ion/Xu2019.py @@ -257,7 +257,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 100.0, "Maximum concentration in positive electrode [mol.m-3]": 48230.0, - "Positive electrode diffusivity [m2.s-1]": 1e-14, + "Positive particle diffusivity [m2.s-1]": 1e-14, "Positive electrode OCP [V]": nmc_ocp_Xu2019, "Positive electrode porosity": 0.331, "Positive electrode active material volume fraction": 0.518, diff --git a/pybamm/input/parameters/lithium_ion/__init__.py b/pybamm/input/parameters/lithium_ion/__init__.py index e69de29bb2..8f8e250f9d 100644 --- a/pybamm/input/parameters/lithium_ion/__init__.py +++ b/pybamm/input/parameters/lithium_ion/__init__.py @@ -0,0 +1,5 @@ +__all__ = ['Ai2020', 'Chen2020', 'Chen2020_composite', 'Ecker2015', + 'Ecker2015_graphite_halfcell', 'MSMR_example_set', 'Marquis2019', + 'Mohtat2020', 'NCA_Kim2011', 'OKane2022', + 'OKane2022_graphite_SiOx_halfcell', 'ORegan2022', 'Prada2013', + 'Ramadass2004', 'Xu2019'] diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py deleted file mode 100644 index 3809d763f2..0000000000 --- a/pybamm/install_odes.py +++ /dev/null @@ -1,202 +0,0 @@ -import os -import tarfile -from os.path import join, isfile -import argparse -import sys -import logging -import subprocess -from multiprocessing import cpu_count - -from pybamm.util import root_dir - -if sys.platform == "win32": - raise Exception("pybamm_install_odes is not supported on Windows.") - -SUNDIALS_VERSION = "6.5.0" - -# Build in parallel wherever possible -os.environ["CMAKE_BUILD_PARALLEL_LEVEL"] = str(cpu_count()) - -try: - # wget module is required to download SUNDIALS or SuiteSparse. - import wget - - NO_WGET = False -except ModuleNotFoundError: - NO_WGET = True - -# Build in parallel wherever possible -os.environ["CMAKE_BUILD_PARALLEL_LEVEL"] = str(cpu_count()) - - -def download_extract_library(url, directory): - # Download and extract archive at url - if NO_WGET: - error_msg = ( - "Could not find wget module." - " Please install wget module (pip install wget)." - ) - raise ModuleNotFoundError(error_msg) - archive = wget.download(url, out=directory) - tar = tarfile.open(archive) - tar.extractall(directory) - - -def install_sundials(download_dir, install_dir): - # Download the SUNDIALS library and compile it. - logger = logging.getLogger("scikits.odes setup") - - try: - subprocess.run(["cmake", "--version"]) - except OSError: - raise RuntimeError("CMake must be installed to build SUNDIALS.") - - url = f"https://github.com/LLNL/sundials/releases/download/v{SUNDIALS_VERSION}/sundials-{SUNDIALS_VERSION}.tar.gz" - logger.info("Downloading sundials") - download_extract_library(url, download_dir) - - cmake_args = [ - "-DLAPACK_ENABLE=ON", - "-DSUNDIALS_INDEX_SIZE=32", - "-DBUILD_ARKODE:BOOL=OFF", - "-DEXAMPLES_ENABLE:BOOL=OFF", - f"-DCMAKE_INSTALL_PREFIX={install_dir}", - ] - - # SUNDIALS are built within directory 'build_sundials' in the PyBaMM root - # directory - build_directory = os.path.abspath(join(download_dir, "build_sundials")) - if not os.path.exists(build_directory): - print("\n-" * 10, "Creating build dir", "-" * 40) - os.makedirs(build_directory) - - print("-" * 10, "Running CMake prepare", "-" * 40) - subprocess.run( - ["cmake", f"../sundials-{SUNDIALS_VERSION}", *cmake_args], - cwd=build_directory, - check=True, - ) - - print("-" * 10, "Building the sundials", "-" * 40) - make_cmd = ["make", "install"] - subprocess.run(make_cmd, cwd=build_directory, check=True) - - -def update_LD_LIBRARY_PATH(install_dir): - # Look for the current python virtual env and add an export statement - # for LD_LIBRARY_PATH in the activate script. If no virtual env is found, - # the current user's .bashrc file is modified instead. - - export_statement = f"export LD_LIBRARY_PATH={install_dir}/lib:$LD_LIBRARY_PATH" - - home_dir = os.environ.get("HOME") - bashrc_path = os.path.join(home_dir, ".bashrc") - zshrc_path = os.path.join(home_dir, ".zshrc") - venv_path = os.environ.get("VIRTUAL_ENV") - - if venv_path: - script_path = os.path.join(venv_path, "bin/activate") - else: - if os.path.exists(bashrc_path): - script_path = os.path.join(os.environ.get("HOME"), ".bashrc") - elif os.path.exists(zshrc_path): - script_path = os.path.join(os.environ.get("HOME"), ".zshrc") - elif os.path.exists(bashrc_path) and os.path.exists(zshrc_path): - print( - "Both .bashrc and .zshrc found in the home directory. Setting .bashrc as path" - ) - script_path = os.path.join(os.environ.get("HOME"), ".bashrc") - else: - print("Neither .bashrc nor .zshrc found in the home directory.") - - if os.getenv("LD_LIBRARY_PATH") and f"{install_dir}/lib" in os.getenv( - "LD_LIBRARY_PATH" - ): - print(f"{install_dir}/lib was found in LD_LIBRARY_PATH.") - if os.path.exists(bashrc_path): - print("--> Not updating venv activate or .bashrc scripts") - if os.path.exists(zshrc_path): - print("--> Not updating venv activate or .zshrc scripts") - else: - with open(script_path, "a+") as fh: - # Just check that export statement is not already there. - if export_statement not in fh.read(): - fh.write(export_statement) - print( - f"Adding {install_dir}/lib to LD_LIBRARY_PATH" f" in {script_path}" - ) - - -def main(arguments=None): - log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s" - logger = logging.getLogger("scikits.odes setup") - - # To override the default severity of logging - logger.setLevel("INFO") - - # Use FileHandler() to log to a file - logfile = join(os.path.dirname(os.path.abspath(__file__)), "scikits_odes_setup.log") - print(logfile) - file_handler = logging.FileHandler(logfile) - formatter = logging.Formatter(log_format) - file_handler.setFormatter(formatter) - - # Add the file handler - logger.addHandler(file_handler) - logger.info("Starting scikits.odes setup") - - desc = "Install scikits.odes." - parser = argparse.ArgumentParser(description=desc) - parser.add_argument("--sundials-libs", type=str, help="path to sundials libraries.") - default_install_dir = os.path.join(os.getenv("HOME"), ".local") - parser.add_argument("--install-dir", type=str, default=default_install_dir) - args = parser.parse_args(arguments) - - pybamm_dir = root_dir() - install_dir = ( - args.install_dir - if os.path.isabs(args.install_dir) - else os.path.join(pybamm_dir, args.install_dir) - ) - - # Check if sundials is already installed - SUNDIALS_LIB_DIRS = [join(os.getenv("HOME"), ".local"), "/usr/local", "/usr"] - - if args.sundials_libs: - SUNDIALS_LIB_DIRS.insert(0, args.sundials_libs) - for DIR in SUNDIALS_LIB_DIRS: - logger.info(f"Looking for sundials at {DIR}") - SUNDIALS_FOUND = isfile(join(DIR, "lib", "libsundials_ida.so")) or isfile( - join(DIR, "lib", "libsundials_ida.dylib") - ) - if SUNDIALS_FOUND: - SUNDIALS_LIB_DIR = DIR - logger.info(f"Found sundials at {SUNDIALS_LIB_DIR}") - break - - if not SUNDIALS_FOUND: - logger.info("Could not find sundials libraries.") - logger.info(f"Installing sundials in {install_dir}") - download_dir = os.path.join(pybamm_dir, "sundials") - if not os.path.exists(download_dir): - os.makedirs(download_dir) - install_sundials(download_dir, install_dir) - SUNDIALS_LIB_DIR = install_dir - - update_LD_LIBRARY_PATH(SUNDIALS_LIB_DIR) - - # At the time scikits.odes is pip installed, the path to the sundials - # library must be contained in an env variable SUNDIALS_INST - # see https://scikits-odes.readthedocs.io/en/latest/installation.html#id1 - os.environ["SUNDIALS_INST"] = SUNDIALS_LIB_DIR - env = os.environ.copy() - logger.info("Installing scikits.odes via pip") - subprocess.run( - [f"{sys.executable}", "-m", "pip", "install", "scikits.odes", "--verbose"], - env=env, - check=True, - ) - - -if __name__ == "__main__": - main(sys.argv[1:]) diff --git a/pybamm/logger.py b/pybamm/logger.py index 5e96d5a010..7dcacb5237 100644 --- a/pybamm/logger.py +++ b/pybamm/logger.py @@ -1,11 +1,3 @@ -# -# Logging class for PyBaMM -# Includes additional logging levels inspired by verboselogs -# https://pypi.org/project/verboselogs/#overview-of-logging-levels -# -# Implementation from stackoverflow -# https://stackoverflow.com/questions/2183233/how-to-add-a-custom-loglevel-to-pythons-logging-facility -# import logging diff --git a/pybamm/meshes/__init__.py b/pybamm/meshes/__init__.py index e69de29bb2..0bd3f8d78b 100644 --- a/pybamm/meshes/__init__.py +++ b/pybamm/meshes/__init__.py @@ -0,0 +1,2 @@ +__all__ = ['meshes', 'one_dimensional_submeshes', 'scikit_fem_submeshes', + 'zero_dimensional_submesh'] diff --git a/pybamm/meshes/meshes.py b/pybamm/meshes/meshes.py index 7fdcd0eede..3ec291b1b8 100644 --- a/pybamm/meshes/meshes.py +++ b/pybamm/meshes/meshes.py @@ -85,8 +85,8 @@ def __init__(self, geometry, submesh_types, var_pts): for spatial_variable, spatial_limits in geometry[domain].items(): # process tab information if using 1 or 2D current collectors if spatial_variable == "tabs": - for tab, position_size in spatial_limits.items(): - for position_size, sym in position_size.items(): + for tab, position_info in spatial_limits.items(): + for position_size, sym in position_info.items(): if isinstance(sym, pybamm.Symbol): sym_eval = sym.evaluate() geometry[domain]["tabs"][tab][position_size] = sym_eval @@ -102,7 +102,7 @@ def __init__(self, geometry, submesh_types, var_pts): "geometry. Make sure that something like " "`param.process_geometry(geometry)` has been " "run." - ) + ) from error else: raise error elif isinstance(sym, numbers.Number): diff --git a/pybamm/meshes/one_dimensional_submeshes.py b/pybamm/meshes/one_dimensional_submeshes.py index d6c3c7f78e..8f27049411 100644 --- a/pybamm/meshes/one_dimensional_submeshes.py +++ b/pybamm/meshes/one_dimensional_submeshes.py @@ -132,7 +132,7 @@ class Exponential1DSubMesh(SubMesh1D): .. math:: x_{k} = (b-a) + - \\frac{\mathrm{e}^{\\alpha k / N} - 1}{\mathrm{e}^{\\alpha} - 1} + a, + \\frac{\\mathrm{e}^{\\alpha k / N} - 1}{\\mathrm{e}^{\\alpha} - 1} + a, for k = 1, ..., N, where N is the number of nodes. @@ -140,7 +140,7 @@ class Exponential1DSubMesh(SubMesh1D): .. math:: x_{k} = (b-a) + - \\frac{\mathrm{e}^{-\\alpha k / N} - 1}{\mathrm{e}^{-\\alpha} - 1} + a, + \\frac{\\mathrm{e}^{-\\alpha k / N} - 1}{\\mathrm{e}^{-\\alpha} - 1} + a, for k = 1, ..., N. @@ -149,7 +149,7 @@ class Exponential1DSubMesh(SubMesh1D): .. math:: x_{k} = (b/2-a) + - \\frac{\mathrm{e}^{\\alpha k / N} - 1}{\mathrm{e}^{\\alpha} - 1} + a, + \\frac{\\mathrm{e}^{\\alpha k / N} - 1}{\\mathrm{e}^{\\alpha} - 1} + a, for k = 1, ..., N. The grid spacing is then reflected to contruct the grid on the full interval [a,b]. @@ -289,20 +289,18 @@ class UserSupplied1DSubMesh(SubMesh1D): """ def __init__(self, lims, npts, edges=None): - # raise error if no edges passed if edges is None: raise pybamm.GeometryError("User mesh requires parameter 'edges'") spatial_var, spatial_lims, tabs = self.read_lims(lims) npts = npts[spatial_var.name] - # check that npts + 1 equals number of user-supplied edges if (npts + 1) != len(edges): raise pybamm.GeometryError( - """User-suppled edges has should have length (npts + 1) but has length - {}.Number of points (npts) for domain {} is {}.""".format( - len(edges), spatial_var.domain, npts - ).replace("\n ", " ") + f"""User-suppled edges has should have length (npts + 1) but has length + {len(edges)}.Number of points (npts) for domain {spatial_var.domain} is {npts}.""".replace( + "\n ", " " + ) ) # check end points of edges agree with spatial_lims @@ -360,9 +358,7 @@ def __init__(self, lims, npts, edges=None, order=2): elif (npts + 1) != len(edges): raise pybamm.GeometryError( "User-suppled edges should have length (npts + 1) but has len" - "gth {}. Number of points (npts) for domain {} is {}.".format( - len(edges), spatial_var.domain, npts - ) + f"gth {len(edges)}. Number of points (npts) for domain {spatial_var.domain} is {npts}." ) # check end points of edges agree with spatial_lims diff --git a/pybamm/meshes/scikit_fem_submeshes.py b/pybamm/meshes/scikit_fem_submeshes.py index 82a7bd72f1..ba624c7f48 100644 --- a/pybamm/meshes/scikit_fem_submeshes.py +++ b/pybamm/meshes/scikit_fem_submeshes.py @@ -5,7 +5,7 @@ from .meshes import SubMesh import numpy as np -from pybamm.util import have_optional_dependency +from pybamm.util import import_optional_dependency class ScikitSubMesh2D(SubMesh): @@ -27,7 +27,7 @@ class ScikitSubMesh2D(SubMesh): """ def __init__(self, edges, coord_sys, tabs): - skfem = have_optional_dependency("skfem") + skfem = import_optional_dependency("skfem") self.edges = edges self.nodes = dict.fromkeys(["y", "z"]) for var in self.nodes.keys(): @@ -92,10 +92,8 @@ def read_lims(self, lims): # check coordinate system agrees if spatial_vars[0].coord_sys != spatial_vars[1].coord_sys: raise pybamm.DomainError( - """spatial variables should have the same coordinate system, - but have coordinate systems {} and {}""".format( - spatial_vars[0].coord_sys, spatial_vars[1].coord_sys - ) + f"""spatial variables should have the same coordinate system, + but have coordinate systems {spatial_vars[0].coord_sys} and {spatial_vars[1].coord_sys}""" ) return spatial_vars, tabs @@ -362,11 +360,9 @@ def __init__(self, lims, npts, y_edges=None, z_edges=None): # check that npts equals number of user-supplied edges if npts[var.name] != len(edges[var.name]): raise pybamm.GeometryError( - """User-suppled edges has should have length npts but has length {}. - Number of points (npts) for variable {} in - domain {} is {}.""".format( - len(edges[var.name]), var.name, var.domain, npts[var.name] - ) + f"""User-suppled edges has should have length npts but has length {len(edges[var.name])}. + Number of points (npts) for variable {var.name} in + domain {var.domain} is {npts[var.name]}.""" ) # check end points of edges agree with spatial_lims diff --git a/pybamm/models/__init__.py b/pybamm/models/__init__.py index e69de29bb2..3796f1264d 100644 --- a/pybamm/models/__init__.py +++ b/pybamm/models/__init__.py @@ -0,0 +1 @@ +__all__ = ['base_model', 'event', 'full_battery_models', 'submodels'] diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py index 6b45aeb083..e90f974676 100644 --- a/pybamm/models/base_model.py +++ b/pybamm/models/base_model.py @@ -1,6 +1,5 @@ -# -# Base model class -# +from __future__ import annotations + import numbers import warnings from collections import OrderedDict @@ -11,7 +10,6 @@ import pybamm from pybamm.expression_tree.operations.serialise import Serialise -from pybamm.util import have_optional_dependency class BaseModel: @@ -22,59 +20,14 @@ class BaseModel: ---------- name: str A string giving the name of the model. - options: dict - A dictionary of options to be passed to the model. submodels: dict A dictionary of submodels that the model is composed of. - rhs: dict - A dictionary that maps expressions (variables) to expressions that represent - the rhs. - algebraic: dict - A dictionary that maps expressions (variables) to expressions that represent - the algebraic equations. The algebraic expressions are assumed to equate - to zero. Note that all the variables in the model must exist in the keys of - `rhs` or `algebraic`. - initial_conditions: dict - A dictionary that maps expressions (variables) to expressions that represent - the initial conditions for the state variables y. The initial conditions for - algebraic variables are provided as initial guesses to a root finding algorithm - that calculates consistent initial conditions. boundary_conditions: dict A dictionary that maps expressions (variables) to expressions that represent the boundary conditions. variables: dict A dictionary that maps strings to expressions that represent the useful variables. - events: list of :class:`pybamm.Event` - A list of events. Each event can either cause the solver to terminate - (e.g. concentration goes negative), or be used to inform the solver of the - existance of a discontinuity (e.g. discontinuity in the input current). - concatenated_rhs : :class:`pybamm.Concatenation` - After discretisation, contains the expressions representing the rhs equations - concatenated into a single expression. - concatenated_algebraic : :class:`pybamm.Concatenation` - After discretisation, contains the expressions representing the algebraic - equations concatenated into a single expression. - concatenated_initial_conditions : :class:`numpy.array` - After discretisation, contains the vector of initial conditions. - mass_matrix : :class:`pybamm.Matrix` - After discretisation, contains the mass matrix for the model. This is computed - automatically. - mass_matrix_inv : :class:`pybamm.Matrix` - After discretisation, contains the inverse mass matrix for the differential - (rhs) part of model. This is computed automatically. - jacobian : :class:`pybamm.Concatenation` - Contains the Jacobian for the model. If model.use_jacobian is True, the - Jacobian is computed automatically during solver set up. - jacobian_rhs : :class:`pybamm.Concatenation` - Contains the Jacobian for the part of the model which contains time derivatives. - If model.use_jacobian is True, the Jacobian is computed automatically during - solver set up. - jacobian_algebraic : :class:`pybamm.Concatenation` - Contains the Jacobian for the algebraic part of the model. This may be used - by the solver when calculating consistent initial conditions. If - model.use_jacobian is True, the Jacobian is computed automatically during - solver set up. use_jacobian : bool Whether to use the Jacobian when solving the model (default is True). convert_to_format : str @@ -102,7 +55,9 @@ def __init__(self, name="Unnamed model"): self._algebraic = {} self._initial_conditions = {} self._boundary_conditions = {} + self._variables_by_submodel = {} self._variables = pybamm.FuzzyDict({}) + self._summary_variables = [] self._events = [] self._concatenated_rhs = None self._concatenated_algebraic = None @@ -130,29 +85,29 @@ def deserialise(cls, properties: dict): """ Create a model instance from a serialised object. """ - instance = cls.__new__(cls) # append the model name with _saved to differentiate - instance.__init__(name=properties["name"] + "_saved") + instance = cls(name=properties["name"] + "_saved") instance.options = properties["options"] + return cls.generic_deserialise(instance, properties) + + @classmethod + def generic_deserialise(cls, instance, properties): # Initialise model with stored variables that have already been discretised instance._concatenated_rhs = properties["concatenated_rhs"] instance._concatenated_algebraic = properties["concatenated_algebraic"] instance._concatenated_initial_conditions = properties[ "concatenated_initial_conditions" ] - instance.len_rhs = instance.concatenated_rhs.size instance.len_alg = instance.concatenated_algebraic.size instance.len_rhs_and_alg = instance.len_rhs + instance.len_alg - instance.bounds = properties["bounds"] instance.events = properties["events"] instance.mass_matrix = properties["mass_matrix"] instance.mass_matrix_inv = properties["mass_matrix_inv"] - # add optional properties not required for model to solve if properties["variables"]: instance._variables = pybamm.FuzzyDict(properties["variables"]) @@ -174,10 +129,8 @@ def deserialise(cls, properties: dict): else: # Delete the default variables which have not been discretised instance._variables = pybamm.FuzzyDict({}) - # Model has already been discretised instance.is_discretised = True - return instance @property @@ -421,83 +374,232 @@ def input_parameters(self): self._input_parameters = self._find_symbols(pybamm.InputParameter) return self._input_parameters - def get_parameter_info(self): + def get_parameter_info(self, by_submodel=False): """ Extracts the parameter information and returns it as a dictionary. To get a list of all parameter-like objects without extra information, use :py:attr:`model.parameters`. + + Parameters + ---------- + by_submodel : bool, optional + Whether to return the parameter info sub-model wise or not (default False) """ parameter_info = {} - parameters = self._find_symbols(pybamm.Parameter) - for param in parameters: - parameter_info[param.name] = (param, "Parameter") - - input_parameters = self._find_symbols(pybamm.InputParameter) - for input_param in input_parameters: - if not input_param.domain: - parameter_info[input_param.name] = (input_param, "InputParameter") - else: - parameter_info[input_param.name] = ( - input_param, - f"InputParameter in {input_param.domain}", + + if by_submodel: + for submodel_name, submodel_vars in self._variables_by_submodel.items(): + submodel_info = {} + for var_name, var_symbol in submodel_vars.items(): + if isinstance(var_symbol, pybamm.Parameter): + submodel_info[var_name] = (var_symbol, "Parameter") + elif isinstance(var_symbol, pybamm.InputParameter): + if not var_symbol.domain: + submodel_info[var_name] = (var_symbol, "InputParameter") + else: + submodel_info[var_name] = ( + var_symbol, + f"InputParameter in {var_symbol.domain}", + ) + elif isinstance(var_symbol, pybamm.FunctionParameter): + input_names = "', '".join(var_symbol.input_names) + submodel_info[var_name] = ( + var_symbol, + f"FunctionParameter with inputs(s) '{input_names}'", + ) + else: + submodel_info[var_name] = (var_symbol, "Unknown Type") + + parameters = self._find_symbols_by_submodel( + pybamm.Parameter, submodel_name + ) + for param in parameters: + submodel_info[param.name] = (param, "Parameter") + + input_parameters = self._find_symbols_by_submodel( + pybamm.InputParameter, submodel_name ) + for input_param in input_parameters: + if not input_param.domain: + submodel_info[input_param.name] = ( + input_param, + "InputParameter", + ) + else: + submodel_info[input_param.name] = ( + input_param, + f"InputParameter in {input_param.domain}", + ) - function_parameters = self._find_symbols(pybamm.FunctionParameter) - for func_param in function_parameters: - if func_param.name not in parameter_info: - input_names = "', '".join(func_param.input_names) - parameter_info[func_param.name] = ( - func_param, - f"FunctionParameter with inputs(s) '{input_names}'", + function_parameters = self._find_symbols_by_submodel( + pybamm.FunctionParameter, submodel_name ) + for func_param in function_parameters: + if func_param.name not in parameter_info: + input_names = "', '".join(func_param.input_names) + submodel_info[func_param.name] = ( + func_param, + f"FunctionParameter with inputs(s) '{input_names}'", + ) - return parameter_info + parameter_info[submodel_name] = submodel_info + + else: + parameters = self._find_symbols(pybamm.Parameter) + for param in parameters: + parameter_info[param.name] = (param, "Parameter") + + input_parameters = self._find_symbols(pybamm.InputParameter) + for input_param in input_parameters: + if not input_param.domain: + parameter_info[input_param.name] = (input_param, "InputParameter") + else: + parameter_info[input_param.name] = ( + input_param, + f"InputParameter in {input_param.domain}", + ) - def print_parameter_info(self): - """Print parameter information in a formatted table from a dictionary of parameters""" - info = self.get_parameter_info() - max_param_name_length = 0 - max_param_type_length = 0 + function_parameters = self._find_symbols(pybamm.FunctionParameter) + for func_param in function_parameters: + if func_param.name not in parameter_info: + input_names = "', '".join(func_param.input_names) + parameter_info[func_param.name] = ( + func_param, + f"FunctionParameter with inputs(s) '{input_names}'", + ) - for param, param_type in info.values(): - param_name_length = len(getattr(param, "name", str(param))) - param_type_length = len(param_type) - max_param_name_length = max(max_param_name_length, param_name_length) - max_param_type_length = max(max_param_type_length, param_type_length) + return parameter_info - header_format = ( - f"| {{:<{max_param_name_length}}} | {{:<{max_param_type_length}}} |" + def _calculate_max_lengths(self, parameter_dict): + """ + Calculate the maximum length of parameters and parameter type in a dictionary + + Parameters + ---------- + parameter_dict : dict + The dict from which maximum lengths are calculated + """ + max_name_length = max( + len(getattr(parameter, "name", str(parameter))) + for parameter, _ in parameter_dict.values() ) - row_format = ( - f"| {{:<{max_param_name_length}}} | {{:<{max_param_type_length}}} |" + max_type_length = max( + len(parameter_type) for _, parameter_type in parameter_dict.values() ) - table = [ - header_format.format("Parameter", "Type of parameter"), - header_format.format( - "=" * max_param_name_length, "=" * max_param_type_length - ), + return max_name_length, max_type_length + + def _format_table_row( + self, param_name, param_type, max_name_length, max_type_length + ): + """ + Format the parameter information in a formatted table + + Parameters + ---------- + param_name : str + The name of the parameter + param_type : str + The type of the parameter + max_name_length : int + The maximum length of the parameter in the dictionary + max_type_length : int + The maximum length of the parameter type in the dictionary + """ + param_name_lines = [ + param_name[i : i + max_name_length] + for i in range(0, len(param_name), max_name_length) + ] + param_type_lines = [ + param_type[i : i + max_type_length] + for i in range(0, len(param_type), max_type_length) + ] + max_lines = max(len(param_name_lines), len(param_type_lines)) + + return [ + f"│ {param_name_lines[i]:<{max_name_length}} │ {param_type_lines[i]:<{max_type_length}} │" + for i in range(max_lines) ] - for param, param_type in info.values(): - param_name = getattr(param, "name", str(param)) - param_name_lines = [ - param_name[i : i + max_param_name_length] - for i in range(0, len(param_name), max_param_name_length) - ] - param_type_lines = [ - param_type[i : i + max_param_type_length] - for i in range(0, len(param_type), max_param_type_length) + def print_parameter_info(self, by_submodel=False): + """ + Print parameter information in a formatted table from a dictionary of parameters + + Parameters + ---------- + by_submodel : bool, optional + Whether to print the parameter info sub-model wise or not (default False) + """ + + if by_submodel: + parameter_info = self.get_parameter_info(by_submodel=True) + for submodel_name, submodel_vars in parameter_info.items(): + if not submodel_vars: + print(f"'{submodel_name}' submodel parameters: \nNo parameters\n") + else: + print(f"'{submodel_name}' submodel parameters:") + ( + max_param_name_length, + max_param_type_length, + ) = self._calculate_max_lengths(submodel_vars) + + table = [ + f"┌─{'─' * max_param_name_length}─┬─{'─' * max_param_type_length}─┐", + f"│ {'Parameter':<{max_param_name_length}} │ {'Type of parameter':<{max_param_type_length}} │", + f"├─{'─' * max_param_name_length}─┼─{'─' * max_param_type_length}─┤", + ] + + for param, param_type in submodel_vars.values(): + param_name = getattr(param, "name", str(param)) + table.extend( + self._format_table_row( + param_name, + param_type, + max_param_name_length, + max_param_type_length, + ) + ) + table.extend( + [ + f"└─{'─' * max_param_name_length}─┴─{'─' * max_param_type_length}─┘", + ] + ) + table = "\n".join(table) + "\n" + table.encode("utf-8") + print(table) + + else: + info = self.get_parameter_info() + max_param_name_length, max_param_type_length = self._calculate_max_lengths( + info + ) + + table = [ + f"┌─{'─' * max_param_name_length}─┬─{'─' * max_param_type_length}─┐", + f"│ {'Parameter':<{max_param_name_length}} │ {'Type of parameter':<{max_param_type_length}} │", + f"├─{'─' * max_param_name_length}─┼─{'─' * max_param_type_length}─┤", ] - max_lines = max(len(param_name_lines), len(param_type_lines)) - for i in range(max_lines): - param_line = param_name_lines[i] if i < len(param_name_lines) else "" - type_line = param_type_lines[i] if i < len(param_type_lines) else "" - table.append(row_format.format(param_line, type_line)) + for param, param_type in info.values(): + param_name = getattr(param, "name", str(param)) + table.extend( + self._format_table_row( + param_name, + param_type, + max_param_name_length, + max_param_type_length, + ) + ) + + table.extend( + [ + f"└─{'─' * max_param_name_length}─┴─{'─' * max_param_type_length}─┘", + ] + ) - for line in table: - print(line) + table = "\n".join(table) + "\n" + table.encode("utf-8") + print(table) def _find_symbols(self, typ): """Find all the instances of `typ` in the model""" @@ -516,6 +618,23 @@ def _find_symbols(self, typ): ) return list(all_input_parameters) + def _find_symbols_by_submodel(self, typ, submodel): + """Find all the instances of `typ` in the submodel""" + unpacker = pybamm.SymbolUnpacker(typ) + all_input_parameters = unpacker.unpack_list_of_symbols( + list(self.submodels[submodel].rhs.values()) + + list(self.submodels[submodel].algebraic.values()) + + list(self.submodels[submodel].initial_conditions.values()) + + [ + x[side][0] + for x in self.submodels[submodel].boundary_conditions.values() + for side in x.keys() + ] + + list(self._variables_by_submodel[submodel].values()) + + [event.expression for event in self.submodels[submodel].events] + ) + return list(all_input_parameters) + def new_copy(self): """ Creates a copy of the model, explicitly copying all the mutable attributes @@ -555,13 +674,16 @@ def update(self, *submodels): def build_fundamental(self): # Get the fundamental variables + self._variables_by_submodel = {submodel: {} for submodel in self.submodels} for submodel_name, submodel in self.submodels.items(): pybamm.logger.debug( - "Getting fundamental variables for {} submodel ({})".format( - submodel_name, self.name - ) + f"Getting fundamental variables for {submodel_name} submodel ({self.name})" + ) + submodel_fundamental_variables = submodel.get_fundamental_variables() + self._variables_by_submodel[submodel_name].update( + submodel_fundamental_variables ) - self.variables.update(submodel.get_fundamental_variables()) + self.variables.update(submodel_fundamental_variables) self._built_fundamental = True @@ -580,25 +702,30 @@ def build_coupled_variables(self): for submodel_name, submodel in self.submodels.items(): if submodel_name in submodels: pybamm.logger.debug( - "Getting coupled variables for {} submodel ({})".format( - submodel_name, self.name - ) + f"Getting coupled variables for {submodel_name} submodel ({self.name})" ) try: - self.variables.update( - submodel.get_coupled_variables(self.variables) + model_var_copy = self.variables.copy() + updated_variables = submodel.get_coupled_variables( + self.variables ) + self._variables_by_submodel[submodel_name].update( + { + key: updated_variables[key] + for key in updated_variables + if key not in model_var_copy + } + ) + self.variables.update(updated_variables) submodels.remove(submodel_name) except KeyError as key: if len(submodels) == 1 or count == 100: # no more submodels to try raise pybamm.ModelError( - "Missing variable for submodel '{}': {}.\n".format( - submodel_name, key - ) + f"Missing variable for submodel '{submodel_name}': {key}.\n" + "Check the selected " "submodels provide all of the required variables." - ) + ) from key else: # try setting coupled variables on next loop through pybamm.logger.debug( @@ -621,9 +748,7 @@ def build_model_equations(self): submodel.set_algebraic(self.variables) pybamm.logger.verbose( - "Setting boundary conditions for {} submodel ({})".format( - submodel_name, self.name - ) + f"Setting boundary conditions for {submodel_name} submodel ({self.name})" ) submodel.set_boundary_conditions(self.variables) @@ -687,7 +812,7 @@ def set_initial_conditions_from(self, solution, inplace=True, return_type="model "model.initial_conditions must appear in the solution with " "the same key as the variable name. In the solution provided, " f"'{e.args[0]}' was not found." - ) + ) from e if isinstance(solution, pybamm.Solution): final_state = final_state.data if final_state.ndim == 0: @@ -711,7 +836,7 @@ def set_initial_conditions_from(self, solution, inplace=True, return_type="model "model.initial_conditions must appear in the solution with " "the same key as the variable name. In the solution " f"provided, {e.args[0]}" - ) + ) from e if isinstance(solution, pybamm.Solution): final_state = final_state.data if final_state.ndim == 2: @@ -883,8 +1008,8 @@ def check_well_determined(self, post_discretisation): ] ) all_vars_in_eqns.update(vars_in_eqns) - for var, side_eqn in self.boundary_conditions.items(): - for side, (eqn, typ) in side_eqn.items(): + for _, side_eqn in self.boundary_conditions.items(): + for _, (eqn, _) in side_eqn.items(): vars_in_eqns = unpacker.unpack_symbol(eqn) all_vars_in_eqns.update(vars_in_eqns) @@ -967,9 +1092,7 @@ def check_no_repeated_keys(self): if not rhs_keys.isdisjoint(alg_keys): raise pybamm.ModelError( - "Multiple equations specified for variables {}".format( - rhs_keys.intersection(alg_keys) - ) + f"Multiple equations specified for variables {rhs_keys.intersection(alg_keys)}" ) def info(self, symbol_name): @@ -1013,7 +1136,7 @@ def check_discretised_or_discretise_inplace_if_0D(self): raise pybamm.DiscretisationError( "Cannot automatically discretise model, model should be " f"discretised before exporting casadi functions ({e})" - ) + ) from e def export_casadi_objects(self, variable_names, input_parameter_order=None): """ @@ -1173,20 +1296,18 @@ def latexify(self, filename=None, newline=True, output_variables=None): >>> model = pybamm.lithium_ion.SPM() This will returns all model equations in png - >>> model.latexify("equations.png") + >>> model.latexify("equations.png") # doctest: +SKIP This will return all the model equations in latex - >>> model.latexify() + >>> model.latexify() # doctest: +SKIP This will return the list of all the model equations - >>> model.latexify(newline=False) + >>> model.latexify(newline=False) # doctest: +SKIP This will return first five model equations - >>> model.latexify(newline=False)[1:5] + >>> model.latexify(newline=False)[1:5] # doctest: +SKIP """ - sympy = have_optional_dependency("sympy") - if sympy: - from pybamm.expression_tree.operations.latexify import Latexify + from pybamm.expression_tree.operations.latexify import Latexify return Latexify(self, filename, newline).latexify( output_variables=output_variables @@ -1256,12 +1377,13 @@ def save_model(self, filename=None, mesh=None, variables=None): Plotting may not be available. """, pybamm.ModelWarning, + stacklevel=2, ) Serialise().save_model(self, filename=filename, mesh=mesh, variables=variables) -def load_model(filename, battery_model: BaseModel = None): +def load_model(filename, battery_model: BaseModel | None = None): """ Load in a saved model from a JSON file @@ -1343,9 +1465,7 @@ def check_and_convert_equations(self, equations): variable_in_equation = next(iter(unpacker.unpack_symbol(eqn))) raise TypeError( "Initial conditions cannot contain 'Variable' objects, " - "but '{!r}' found in initial conditions for '{}'".format( - variable_in_equation, var - ) + f"but '{variable_in_equation!r}' found in initial conditions for '{var}'" ) return equations @@ -1376,9 +1496,9 @@ def check_and_convert_bcs(self, boundary_conditions): # Check types if bc[1] not in ["Dirichlet", "Neumann"]: raise pybamm.ModelError( + f""" + boundary condition types must be Dirichlet or Neumann, not '{bc[1]}' """ - boundary condition types must be Dirichlet or Neumann, not '{}' - """.format(bc[1]) ) return boundary_conditions diff --git a/pybamm/models/event.py b/pybamm/models/event.py index 5bba4cd14b..8d2695160e 100644 --- a/pybamm/models/event.py +++ b/pybamm/models/event.py @@ -1,4 +1,9 @@ +from __future__ import annotations + from enum import Enum +import numpy as np + +from typing import TypeVar class EventType(Enum): @@ -24,6 +29,9 @@ class EventType(Enum): SWITCH = 3 +E = TypeVar("E", bound="Event") + + class Event: """ @@ -47,7 +55,7 @@ def __init__(self, name, expression, event_type=EventType.TERMINATION): self._event_type = event_type @classmethod - def _from_json(cls, snippet: dict): + def _from_json(cls: type[E], snippet: dict) -> E: """ Reconstructs an Event instance during deserialisation of a JSON file. @@ -58,17 +66,19 @@ def _from_json(cls, snippet: dict): Should contain "name", "expression" and "event_type". """ - instance = cls.__new__(cls) - - instance.__init__( + return cls( snippet["name"], snippet["expression"], event_type=EventType(snippet["event_type"][1]), ) - return instance - - def evaluate(self, t=None, y=None, y_dot=None, inputs=None): + def evaluate( + self, + t: float | None = None, + y: np.ndarray | None = None, + y_dot: np.ndarray | None = None, + inputs: dict | None = None, + ): """ Acts as a drop-in replacement for :func:`pybamm.Symbol.evaluate` """ diff --git a/pybamm/models/full_battery_models/__init__.py b/pybamm/models/full_battery_models/__init__.py index e69de29bb2..135f678289 100644 --- a/pybamm/models/full_battery_models/__init__.py +++ b/pybamm/models/full_battery_models/__init__.py @@ -0,0 +1,2 @@ +__all__ = ['base_battery_model', 'equivalent_circuit', 'lead_acid', + 'lithium_ion'] diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index cbc270653b..fb8d001f93 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -102,7 +102,7 @@ class BatteryModelOptions(pybamm.FuzzyDict): reactions. * "open-circuit potential" : str Sets the model for the open circuit potential. Can be "single" - (default), "current sigmoid", or "MSMR". If "MSMR" then the "particle" + (default), "current sigmoid", "Wycisk", or "MSMR". If "MSMR" then the "particle" option must also be "MSMR". A 2-tuple can be provided for different behaviour in negative and positive electrodes. * "operating mode" : str @@ -238,6 +238,7 @@ def __init__(self, extra_options): "integrated", ], "exchange-current density": ["single", "current sigmoid"], + "heat of mixing": ["false", "true"], "hydrolysis": ["false", "true"], "intercalation kinetics": [ "symmetric Butler-Volmer", @@ -263,7 +264,7 @@ def __init__(self, extra_options): "stress and reaction-driven", ], "number of MSMR reactions": ["none"], - "open-circuit potential": ["single", "current sigmoid", "MSMR"], + "open-circuit potential": ["single", "current sigmoid", "MSMR", "Wycisk"], "operating mode": [ "current", "voltage", @@ -305,6 +306,16 @@ def __init__(self, extra_options): "surface form": ["false", "differential", "algebraic"], "thermal": ["isothermal", "lumped", "x-lumped", "x-full"], "total interfacial current density as a state": ["false", "true"], + "transport efficiency": [ + "Bruggeman", + "ordered packing", + "hyperbola of revolution", + "overlapping spheres", + "tortuosity factor", + "random overlapping cylinders", + "heterogeneous catalyst", + "cation-exchange membrane", + ], "working electrode": ["both", "positive"], "x-average side reactions": ["false", "true"], } @@ -435,9 +446,7 @@ def __init__(self, extra_options): ) else: raise pybamm.OptionError( - "Option '{}' not recognised. Best matches are {}".format( - name, options.get_best_matches(name) - ) + f"Option '{name}' not recognised. Best matches are {options.get_best_matches(name)}" ) # If any of "open-circuit potential", "particle" or "intercalation kinetics" is @@ -504,6 +513,11 @@ def __init__(self, extra_options): # Options not yet compatible with particle-size distributions if options["particle size"] == "distribution": + if options["heat of mixing"] != "false": + raise NotImplementedError( + "Heat of mixing submodels do not yet support particle-size " + "distributions." + ) if options["lithium plating"] != "none": raise NotImplementedError( "Lithium plating submodels do not yet support particle-size " @@ -611,14 +625,12 @@ def __init__(self, extra_options): and options["particle"] == "Fickian diffusion" and options["particle mechanics"] == "none" and options["loss of active material"] == "none" - and options["lithium plating"] == "none" ): raise pybamm.OptionError( "If there are multiple particle phases: 'surface form' cannot be " "'false', 'particle size' must be 'single', 'particle' must be " "'Fickian diffusion'. Also the following must " - "be 'none': 'particle mechanics', " - "'loss of active material', 'lithium plating'" + "be 'none': 'particle mechanics', 'loss of active material'" ) # Check options are valid @@ -809,54 +821,13 @@ def deserialise(cls, properties: dict): """ Create a model instance from a serialised object. """ - instance = cls.__new__(cls) # append the model name with _saved to differentiate - instance.__init__( + instance = cls( options=properties["options"], name=properties["name"] + "_saved" ) - # Initialise model with stored variables that have already been discretised - instance._concatenated_rhs = properties["concatenated_rhs"] - instance._concatenated_algebraic = properties["concatenated_algebraic"] - instance._concatenated_initial_conditions = properties[ - "concatenated_initial_conditions" - ] - - instance.len_rhs = instance.concatenated_rhs.size - instance.len_alg = instance.concatenated_algebraic.size - instance.len_rhs_and_alg = instance.len_rhs + instance.len_alg - - instance.bounds = properties["bounds"] - instance.events = properties["events"] - instance.mass_matrix = properties["mass_matrix"] - instance.mass_matrix_inv = properties["mass_matrix_inv"] - - # add optional properties not required for model to solve - if properties["variables"]: - instance._variables = pybamm.FuzzyDict(properties["variables"]) - - # assign meshes to each variable - for var in instance._variables.values(): - if var.domain != []: - var.mesh = properties["mesh"][var.domain] - else: - var.mesh = None - - if var.domains["secondary"] != []: - var.secondary_mesh = properties["mesh"][var.domains["secondary"]] - else: - var.secondary_mesh = None - - instance._geometry = pybamm.Geometry(properties["geometry"]) - else: - # Delete the default variables which have not been discretised - instance._variables = pybamm.FuzzyDict({}) - - # Model has already been discretised - instance.is_discretised = True - - return instance + return cls.generic_deserialise(instance, properties) @property def default_geometry(self): @@ -923,9 +894,9 @@ def default_spatial_methods(self): } if self.options["dimensionality"] == 0: # 0D submesh - use base spatial method - base_spatial_methods[ - "current collector" - ] = pybamm.ZeroDimensionalSpatialMethod() + base_spatial_methods["current collector"] = ( + pybamm.ZeroDimensionalSpatialMethod() + ) elif self.options["dimensionality"] == 1: base_spatial_methods["current collector"] = pybamm.FiniteVolume() elif self.options["dimensionality"] == 2: @@ -1039,9 +1010,7 @@ def build_model_equations(self): submodel.set_algebraic(self.variables) pybamm.logger.verbose( - "Setting boundary conditions for {} submodel ({})".format( - submodel_name, self.name - ) + f"Setting boundary conditions for {submodel_name} submodel ({self.name})" ) submodel.set_boundary_conditions(self.variables) @@ -1140,7 +1109,7 @@ def set_external_circuit_submodel(self): ) elif self.options["operating mode"] == "differential power": model = pybamm.external_circuit.PowerFunctionControl( - self.param, self.options, "differential without max" + self.param, self.options, "differential" ) elif self.options["operating mode"] == "explicit power": model = pybamm.external_circuit.ExplicitPowerControl( @@ -1152,7 +1121,7 @@ def set_external_circuit_submodel(self): ) elif self.options["operating mode"] == "differential resistance": model = pybamm.external_circuit.ResistanceFunctionControl( - self.param, self.options, "differential without max" + self.param, self.options, "differential" ) elif self.options["operating mode"] == "explicit resistance": model = pybamm.external_circuit.ExplicitResistanceControl( @@ -1167,16 +1136,99 @@ def set_external_circuit_submodel(self): self.param, self.options["operating mode"], self.options ) self.submodels["external circuit"] = model + self.submodels["discharge and throughput variables"] = ( + pybamm.external_circuit.DischargeThroughput(self.param, self.options) + ) def set_transport_efficiency_submodels(self): - self.submodels[ - "electrolyte transport efficiency" - ] = pybamm.transport_efficiency.Bruggeman( - self.param, "Electrolyte", self.options - ) - self.submodels[ - "electrode transport efficiency" - ] = pybamm.transport_efficiency.Bruggeman(self.param, "Electrode", self.options) + if self.options["transport efficiency"] == "Bruggeman": + self.submodels["electrolyte transport efficiency"] = ( + pybamm.transport_efficiency.Bruggeman( + self.param, "Electrolyte", self.options + ) + ) + self.submodels["electrode transport efficiency"] = ( + pybamm.transport_efficiency.Bruggeman( + self.param, "Electrode", self.options + ) + ) + elif self.options["transport efficiency"] == "tortuosity factor": + self.submodels["electrolyte transport efficiency"] = ( + pybamm.transport_efficiency.TortuosityFactor( + self.param, "Electrolyte", self.options + ) + ) + self.submodels["electrode transport efficiency"] = ( + pybamm.transport_efficiency.TortuosityFactor( + self.param, "Electrode", self.options + ) + ) + elif self.options["transport efficiency"] == "ordered packing": + self.submodels["electrolyte transport efficiency"] = ( + pybamm.transport_efficiency.OrderedPacking( + self.param, "Electrolyte", self.options + ) + ) + self.submodels["electrode transport efficiency"] = ( + pybamm.transport_efficiency.OrderedPacking( + self.param, "Electrode", self.options + ) + ) + elif self.options["transport efficiency"] == "hyperbola of revolution": + self.submodels["electrolyte transport efficiency"] = ( + pybamm.transport_efficiency.HyperbolaOfRevolution( + self.param, "Electrolyte", self.options + ) + ) + self.submodels["electrode transport efficiency"] = ( + pybamm.transport_efficiency.HyperbolaOfRevolution( + self.param, "Electrode", self.options + ) + ) + elif self.options["transport efficiency"] == "overlapping spheres": + self.submodels["electrolyte transport efficiency"] = ( + pybamm.transport_efficiency.OverlappingSpheres( + self.param, "Electrolyte", self.options + ) + ) + self.submodels["electrode transport efficiency"] = ( + pybamm.transport_efficiency.OverlappingSpheres( + self.param, "Electrode", self.options + ) + ) + elif self.options["transport efficiency"] == "random overlapping cylinders": + self.submodels["electrolyte transport efficiency"] = ( + pybamm.transport_efficiency.RandomOverlappingCylinders( + self.param, "Electrolyte", self.options + ) + ) + self.submodels["electrode transport efficiency"] = ( + pybamm.transport_efficiency.RandomOverlappingCylinders( + self.param, "Electrode", self.options + ) + ) + elif self.options["transport efficiency"] == "heterogeneous catalyst": + self.submodels["electrolyte transport efficiency"] = ( + pybamm.transport_efficiency.HeterogeneousCatalyst( + self.param, "Electrolyte", self.options + ) + ) + self.submodels["electrode transport efficiency"] = ( + pybamm.transport_efficiency.HeterogeneousCatalyst( + self.param, "Electrode", self.options + ) + ) + elif self.options["transport efficiency"] == "cation-exchange membrane": + self.submodels["electrolyte transport efficiency"] = ( + pybamm.transport_efficiency.CationExchangeMembrane( + self.param, "Electrolyte", self.options + ) + ) + self.submodels["electrode transport efficiency"] = ( + pybamm.transport_efficiency.CationExchangeMembrane( + self.param, "Electrode", self.options + ) + ) def set_thermal_submodel(self): if self.options["thermal"] == "isothermal": @@ -1194,7 +1246,10 @@ def set_thermal_submodel(self): if self.options["dimensionality"] == 0: thermal_submodel = pybamm.thermal.pouch_cell.OneDimensionalX - self.submodels["thermal"] = thermal_submodel(self.param, self.options) + x_average = getattr(self, "x_average", False) + self.submodels["thermal"] = thermal_submodel( + self.param, self.options, x_average + ) def set_current_collector_submodel(self): if self.options["current collector"] in ["uniform"]: diff --git a/pybamm/models/full_battery_models/equivalent_circuit/__init__.py b/pybamm/models/full_battery_models/equivalent_circuit/__init__.py index c4bf7d5a56..8c87ce3179 100644 --- a/pybamm/models/full_battery_models/equivalent_circuit/__init__.py +++ b/pybamm/models/full_battery_models/equivalent_circuit/__init__.py @@ -1 +1,3 @@ from .thevenin import * + +__all__ = ['ecm_model_options', 'thevenin'] diff --git a/pybamm/models/full_battery_models/equivalent_circuit/thevenin.py b/pybamm/models/full_battery_models/equivalent_circuit/thevenin.py index 9d01f89ffd..9aaf747a4c 100644 --- a/pybamm/models/full_battery_models/equivalent_circuit/thevenin.py +++ b/pybamm/models/full_battery_models/equivalent_circuit/thevenin.py @@ -89,17 +89,13 @@ def set_options(self, extra_options=None): options[name] = opt else: raise pybamm.OptionError( - "Option '{}' not recognised. Best matches are {}".format( - name, options.get_best_matches(name) - ) + f"Option '{name}' not recognised. Best matches are {options.get_best_matches(name)}" ) for opt, value in options.items(): if value not in possible_options[opt]: raise pybamm.OptionError( - "Option '{}' must be one of {}. Got '{}' instead.".format( - opt, possible_options[opt], value - ) + f"Option '{opt}' must be one of {possible_options[opt]}. Got '{value}' instead." ) self.options = options @@ -124,7 +120,7 @@ def set_external_circuit_submodel(self): ) elif self.options["operating mode"] == "differential power": model = pybamm.external_circuit.PowerFunctionControl( - self.param, self.options, "differential without max" + self.param, self.options, "differential" ) elif self.options["operating mode"] == "resistance": model = pybamm.external_circuit.ResistanceFunctionControl( @@ -132,7 +128,7 @@ def set_external_circuit_submodel(self): ) elif self.options["operating mode"] == "differential resistance": model = pybamm.external_circuit.ResistanceFunctionControl( - self.param, self.options, "differential without max" + self.param, self.options, "differential" ) elif self.options["operating mode"] == "CCCV": model = pybamm.external_circuit.CCCVFunctionControl( @@ -143,14 +139,14 @@ def set_external_circuit_submodel(self): self.param, self.options["operating mode"], self.options, - control="differential without max", + control="differential", ) self.submodels["external circuit"] = model def set_ocv_submodel(self): - self.submodels[ - "Open-circuit voltage" - ] = pybamm.equivalent_circuit_elements.OCVElement(self.param, self.options) + self.submodels["Open-circuit voltage"] = ( + pybamm.equivalent_circuit_elements.OCVElement(self.param, self.options) + ) def set_resistor_submodel(self): name = "Element-0 (Resistor)" diff --git a/pybamm/models/full_battery_models/lead_acid/__init__.py b/pybamm/models/full_battery_models/lead_acid/__init__.py index 5895bddeb4..b707e8922c 100644 --- a/pybamm/models/full_battery_models/lead_acid/__init__.py +++ b/pybamm/models/full_battery_models/lead_acid/__init__.py @@ -5,3 +5,5 @@ from .loqs import LOQS from .full import Full from .basic_full import BasicFull + +__all__ = ['base_lead_acid_model', 'basic_full', 'full', 'loqs'] diff --git a/pybamm/models/full_battery_models/lead_acid/base_lead_acid_model.py b/pybamm/models/full_battery_models/lead_acid/base_lead_acid_model.py index c0b5d1935c..1e6105be0f 100644 --- a/pybamm/models/full_battery_models/lead_acid/base_lead_acid_model.py +++ b/pybamm/models/full_battery_models/lead_acid/base_lead_acid_model.py @@ -77,23 +77,23 @@ def set_soc_variables(self): def set_open_circuit_potential_submodel(self): for domain in ["negative", "positive"]: - self.submodels[ - f"{domain} open-circuit potential" - ] = pybamm.open_circuit_potential.SingleOpenCircuitPotential( - self.param, domain, "lead-acid main", self.options, "primary" + self.submodels[f"{domain} open-circuit potential"] = ( + pybamm.open_circuit_potential.SingleOpenCircuitPotential( + self.param, domain, "lead-acid main", self.options, "primary" + ) ) - self.submodels[ - f"{domain} oxygen open-circuit potential" - ] = pybamm.open_circuit_potential.SingleOpenCircuitPotential( - self.param, domain, "lead-acid oxygen", self.options, "primary" + self.submodels[f"{domain} oxygen open-circuit potential"] = ( + pybamm.open_circuit_potential.SingleOpenCircuitPotential( + self.param, domain, "lead-acid oxygen", self.options, "primary" + ) ) def set_active_material_submodel(self): for domain in ["negative", "positive"]: - self.submodels[ - f"{domain} active material" - ] = pybamm.active_material.Constant( - self.param, domain, self.options, "primary" + self.submodels[f"{domain} active material"] = ( + pybamm.active_material.Constant( + self.param, domain, self.options, "primary" + ) ) def set_sei_submodel(self): @@ -104,9 +104,9 @@ def set_sei_submodel(self): def set_lithium_plating_submodel(self): for domain in ["negative", "positive"]: - self.submodels[ - f"{domain} lithium plating" - ] = pybamm.lithium_plating.NoPlating(self.param, domain) + self.submodels[f"{domain} lithium plating"] = ( + pybamm.lithium_plating.NoPlating(self.param, domain) + ) def set_total_interface_submodel(self): self.submodels["total interface"] = pybamm.interface.TotalInterfacialCurrent( diff --git a/pybamm/models/full_battery_models/lead_acid/full.py b/pybamm/models/full_battery_models/lead_acid/full.py index 927f9a2028..b5b561e6dd 100644 --- a/pybamm/models/full_battery_models/lead_acid/full.py +++ b/pybamm/models/full_battery_models/lead_acid/full.py @@ -45,24 +45,24 @@ def set_porosity_submodel(self): def set_convection_submodel(self): if self.options["convection"] == "none": - self.submodels[ - "transverse convection" - ] = pybamm.convection.transverse.NoConvection(self.param) - self.submodels[ - "through-cell convection" - ] = pybamm.convection.through_cell.NoConvection(self.param) + self.submodels["transverse convection"] = ( + pybamm.convection.transverse.NoConvection(self.param) + ) + self.submodels["through-cell convection"] = ( + pybamm.convection.through_cell.NoConvection(self.param) + ) else: if self.options["convection"] == "uniform transverse": - self.submodels[ - "transverse convection" - ] = pybamm.convection.transverse.Uniform(self.param) + self.submodels["transverse convection"] = ( + pybamm.convection.transverse.Uniform(self.param) + ) elif self.options["convection"] == "full transverse": - self.submodels[ - "transverse convection" - ] = pybamm.convection.transverse.Full(self.param) - self.submodels[ - "through-cell convection" - ] = pybamm.convection.through_cell.Full(self.param) + self.submodels["transverse convection"] = ( + pybamm.convection.transverse.Full(self.param) + ) + self.submodels["through-cell convection"] = ( + pybamm.convection.through_cell.Full(self.param) + ) def set_intercalation_kinetics_submodel(self): for domain in ["negative", "positive"]: @@ -90,9 +90,9 @@ def set_electrolyte_submodel(self): ) if self.options["surface form"] == "false": - self.submodels[ - "electrolyte conductivity" - ] = pybamm.electrolyte_conductivity.Full(self.param) + self.submodels["electrolyte conductivity"] = ( + pybamm.electrolyte_conductivity.Full(self.param) + ) surf_model = surf_form.Explicit elif self.options["surface form"] == "differential": surf_model = surf_form.FullDifferential @@ -112,10 +112,14 @@ def set_side_reaction_submodels(self): self.submodels["positive oxygen interface"] = pybamm.kinetics.ForwardTafel( self.param, "positive", "lead-acid oxygen", self.options, "primary" ) - self.submodels[ - "negative oxygen interface" - ] = pybamm.kinetics.DiffusionLimited( - self.param, "negative", "lead-acid oxygen", self.options, order="full" + self.submodels["negative oxygen interface"] = ( + pybamm.kinetics.DiffusionLimited( + self.param, + "negative", + "lead-acid oxygen", + self.options, + order="full", + ) ) else: self.submodels["oxygen diffusion"] = pybamm.oxygen_diffusion.NoOxygen( diff --git a/pybamm/models/full_battery_models/lead_acid/loqs.py b/pybamm/models/full_battery_models/lead_acid/loqs.py index 953be55c9c..c63c9cd11b 100644 --- a/pybamm/models/full_battery_models/lead_acid/loqs.py +++ b/pybamm/models/full_battery_models/lead_acid/loqs.py @@ -47,22 +47,22 @@ def set_external_circuit_submodel(self): e.g. (not necessarily constant-) current, voltage, etc """ if self.options["operating mode"] == "current": - self.submodels[ - "external circuit" - ] = pybamm.external_circuit.ExplicitCurrentControl(self.param, self.options) + self.submodels["external circuit"] = ( + pybamm.external_circuit.ExplicitCurrentControl(self.param, self.options) + ) elif self.options["operating mode"] == "voltage": - self.submodels[ - "external circuit" - ] = pybamm.external_circuit.VoltageFunctionControl(self.param, self.options) + self.submodels["external circuit"] = ( + pybamm.external_circuit.VoltageFunctionControl(self.param, self.options) + ) elif self.options["operating mode"] == "power": - self.submodels[ - "external circuit" - ] = pybamm.external_circuit.PowerFunctionControl(self.param, self.options) + self.submodels["external circuit"] = ( + pybamm.external_circuit.PowerFunctionControl(self.param, self.options) + ) elif callable(self.options["operating mode"]): - self.submodels[ - "external circuit" - ] = pybamm.external_circuit.FunctionControl( - self.param, self.options["operating mode"], self.options + self.submodels["external circuit"] = ( + pybamm.external_circuit.FunctionControl( + self.param, self.options["operating mode"], self.options + ) ) def set_current_collector_submodel(self): @@ -85,58 +85,58 @@ def set_porosity_submodel(self): def set_convection_submodel(self): if self.options["convection"] == "none": - self.submodels[ - "leading-order transverse convection" - ] = pybamm.convection.transverse.NoConvection(self.param) - self.submodels[ - "leading-order through-cell convection" - ] = pybamm.convection.through_cell.NoConvection(self.param) + self.submodels["leading-order transverse convection"] = ( + pybamm.convection.transverse.NoConvection(self.param) + ) + self.submodels["leading-order through-cell convection"] = ( + pybamm.convection.through_cell.NoConvection(self.param) + ) else: if self.options["convection"] == "uniform transverse": - self.submodels[ - "leading-order transverse convection" - ] = pybamm.convection.transverse.Uniform(self.param) + self.submodels["leading-order transverse convection"] = ( + pybamm.convection.transverse.Uniform(self.param) + ) elif self.options["convection"] == "full transverse": - self.submodels[ - "leading-order transverse convection" - ] = pybamm.convection.transverse.Full(self.param) - self.submodels[ - "leading-order through-cell convection" - ] = pybamm.convection.through_cell.Explicit(self.param) + self.submodels["leading-order transverse convection"] = ( + pybamm.convection.transverse.Full(self.param) + ) + self.submodels["leading-order through-cell convection"] = ( + pybamm.convection.through_cell.Explicit(self.param) + ) def set_intercalation_kinetics_submodel(self): if self.options["surface form"] == "false": - self.submodels[ - "leading-order negative interface" - ] = pybamm.kinetics.InverseButlerVolmer( - self.param, "negative", "lead-acid main", self.options - ) - self.submodels[ - "leading-order positive interface" - ] = pybamm.kinetics.InverseButlerVolmer( - self.param, "positive", "lead-acid main", self.options - ) - self.submodels[ - "negative interface current" - ] = pybamm.kinetics.CurrentForInverseButlerVolmer( - self.param, "negative", "lead-acid main" - ) - self.submodels[ - "positive interface current" - ] = pybamm.kinetics.CurrentForInverseButlerVolmer( - self.param, "positive", "lead-acid main" + self.submodels["leading-order negative interface"] = ( + pybamm.kinetics.InverseButlerVolmer( + self.param, "negative", "lead-acid main", self.options + ) + ) + self.submodels["leading-order positive interface"] = ( + pybamm.kinetics.InverseButlerVolmer( + self.param, "positive", "lead-acid main", self.options + ) + ) + self.submodels["negative interface current"] = ( + pybamm.kinetics.CurrentForInverseButlerVolmer( + self.param, "negative", "lead-acid main" + ) + ) + self.submodels["positive interface current"] = ( + pybamm.kinetics.CurrentForInverseButlerVolmer( + self.param, "positive", "lead-acid main" + ) ) else: - self.submodels[ - "leading-order negative interface" - ] = pybamm.kinetics.SymmetricButlerVolmer( - self.param, "negative", "lead-acid main", self.options, "primary" + self.submodels["leading-order negative interface"] = ( + pybamm.kinetics.SymmetricButlerVolmer( + self.param, "negative", "lead-acid main", self.options, "primary" + ) ) - self.submodels[ - "leading-order positive interface" - ] = pybamm.kinetics.SymmetricButlerVolmer( - self.param, "positive", "lead-acid main", self.options, "primary" + self.submodels["leading-order positive interface"] = ( + pybamm.kinetics.SymmetricButlerVolmer( + self.param, "positive", "lead-acid main", self.options, "primary" + ) ) # always use forward Butler-Volmer for the reaction submodel to be passed to the # higher order model @@ -154,20 +154,20 @@ def set_intercalation_kinetics_submodel(self): } def set_electrode_submodels(self): - self.submodels[ - "leading-order negative electrode potential" - ] = pybamm.electrode.ohm.LeadingOrder(self.param, "negative") - self.submodels[ - "leading-order positive electrode potential" - ] = pybamm.electrode.ohm.LeadingOrder(self.param, "positive") + self.submodels["leading-order negative electrode potential"] = ( + pybamm.electrode.ohm.LeadingOrder(self.param, "negative") + ) + self.submodels["leading-order positive electrode potential"] = ( + pybamm.electrode.ohm.LeadingOrder(self.param, "positive") + ) def set_electrolyte_submodel(self): surf_form = pybamm.electrolyte_conductivity.surface_potential_form if self.options["surface form"] == "false": - self.submodels[ - "leading-order electrolyte conductivity" - ] = pybamm.electrolyte_conductivity.LeadingOrder(self.param) + self.submodels["leading-order electrolyte conductivity"] = ( + pybamm.electrolyte_conductivity.LeadingOrder(self.param) + ) surf_model = surf_form.Explicit elif self.options["surface form"] == "differential": surf_model = surf_form.LeadingOrderDifferential @@ -179,42 +179,42 @@ def set_electrolyte_submodel(self): self.param, domain, self.options ) - self.submodels[ - "electrolyte diffusion" - ] = pybamm.electrolyte_diffusion.LeadingOrder(self.param) + self.submodels["electrolyte diffusion"] = ( + pybamm.electrolyte_diffusion.LeadingOrder(self.param) + ) def set_side_reaction_submodels(self): if self.options["hydrolysis"] == "true": - self.submodels[ - "leading-order oxygen diffusion" - ] = pybamm.oxygen_diffusion.LeadingOrder(self.param) - self.submodels[ - "leading-order positive oxygen interface" - ] = pybamm.kinetics.ForwardTafel( - self.param, "positive", "lead-acid oxygen", self.options, "primary" - ) - self.submodels[ - "leading-order negative oxygen interface" - ] = pybamm.kinetics.DiffusionLimited( - self.param, - "negative", - "lead-acid oxygen", - self.options, - order="leading", + self.submodels["leading-order oxygen diffusion"] = ( + pybamm.oxygen_diffusion.LeadingOrder(self.param) + ) + self.submodels["leading-order positive oxygen interface"] = ( + pybamm.kinetics.ForwardTafel( + self.param, "positive", "lead-acid oxygen", self.options, "primary" + ) + ) + self.submodels["leading-order negative oxygen interface"] = ( + pybamm.kinetics.DiffusionLimited( + self.param, + "negative", + "lead-acid oxygen", + self.options, + order="leading", + ) ) else: - self.submodels[ - "leading-order oxygen diffusion" - ] = pybamm.oxygen_diffusion.NoOxygen(self.param) - self.submodels[ - "leading-order negative oxygen interface" - ] = pybamm.kinetics.NoReaction( - self.param, "negative", "lead-acid oxygen", "primary" - ) - self.submodels[ - "leading-order positive oxygen interface" - ] = pybamm.kinetics.NoReaction( - self.param, "positive", "lead-acid oxygen", "primary" + self.submodels["leading-order oxygen diffusion"] = ( + pybamm.oxygen_diffusion.NoOxygen(self.param) + ) + self.submodels["leading-order negative oxygen interface"] = ( + pybamm.kinetics.NoReaction( + self.param, "negative", "lead-acid oxygen", "primary" + ) + ) + self.submodels["leading-order positive oxygen interface"] = ( + pybamm.kinetics.NoReaction( + self.param, "positive", "lead-acid oxygen", "primary" + ) ) self.reaction_submodels["negative"].append( self.submodels["leading-order negative oxygen interface"] diff --git a/pybamm/models/full_battery_models/lithium_ion/__init__.py b/pybamm/models/full_battery_models/lithium_ion/__init__.py index 4afb23f493..b02868dbe9 100644 --- a/pybamm/models/full_battery_models/lithium_ion/__init__.py +++ b/pybamm/models/full_battery_models/lithium_ion/__init__.py @@ -11,7 +11,7 @@ ) from .electrode_soh_half_cell import ( ElectrodeSOHHalfCell, - get_initial_stoichiometry_half_cell + get_initial_stoichiometry_half_cell, ) from .spm import SPM from .spme import SPMe @@ -24,3 +24,8 @@ from .Yang2017 import Yang2017 from .mpm import MPM from .msmr import MSMR + +__all__ = ['Yang2017', 'base_lithium_ion_model', 'basic_dfn', + 'basic_dfn_composite', 'basic_dfn_half_cell', 'basic_spm', 'dfn', + 'electrode_soh', 'electrode_soh_half_cell', 'mpm', 'msmr', + 'newman_tobias', 'spm', 'spme'] diff --git a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py index fbe19b0d42..479e8203ed 100644 --- a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py +++ b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py @@ -181,7 +181,7 @@ def set_degradation_variables(self): } ) - def set_summary_variables(self): + def set_default_summary_variables(self): """ Sets the default summary variables. """ @@ -248,6 +248,9 @@ def set_open_circuit_potential_submodel(self): ocp_model = ocp_submodels.SingleOpenCircuitPotential elif ocp_option == "current sigmoid": ocp_model = ocp_submodels.CurrentSigmoidOpenCircuitPotential + elif ocp_option == "Wycisk": + pybamm.citations.register("Wycisk2022") + ocp_model = ocp_submodels.WyciskOpenCircuitPotential elif ocp_option == "MSMR": ocp_model = ocp_submodels.MSMROpenCircuitPotential self.submodels[f"{domain} {phase} open-circuit potential"] = ocp_model( @@ -318,10 +321,10 @@ def set_sei_on_cracks_submodel(self): ) self.submodels[f"{domain} {phase} sei on cracks"] = submodel if len(phases) > 1: - self.submodels[ - f"{domain} total sei on cracks" - ] = pybamm.sei.TotalSEI( - self.param, domain, self.options, cracks=True + self.submodels[f"{domain} total sei on cracks"] = ( + pybamm.sei.TotalSEI( + self.param, domain, self.options, cracks=True + ) ) def set_lithium_plating_submodel(self): @@ -331,18 +334,23 @@ def set_lithium_plating_submodel(self): if domain != "separator": domain = domain.split()[0].lower() lithium_plating_opt = getattr(self.options, domain)["lithium plating"] - if lithium_plating_opt == "none": - self.submodels[ - f"{domain} lithium plating" - ] = pybamm.lithium_plating.NoPlating( - self.param, domain, self.options - ) - else: - x_average = self.options["x-average side reactions"] == "true" - self.submodels[ - f"{domain} lithium plating" - ] = pybamm.lithium_plating.Plating( - self.param, domain, x_average, self.options + phases = self.options.phases[domain] + for phase in phases: + if lithium_plating_opt == "none": + submodel = pybamm.lithium_plating.NoPlating( + self.param, domain, self.options, phase + ) + else: + x_average = self.options["x-average side reactions"] == "true" + submodel = pybamm.lithium_plating.Plating( + self.param, domain, x_average, self.options, phase + ) + self.submodels[f"{domain} {phase} lithium plating"] = submodel + if len(phases) > 1: + self.submodels[f"{domain} total lithium plating"] = ( + pybamm.lithium_plating.TotalLithiumPlating( + self.param, domain, self.options + ) ) def set_total_interface_submodel(self): @@ -356,26 +364,26 @@ def set_crack_submodel(self): domain = domain.split()[0].lower() crack = getattr(self.options, domain)["particle mechanics"] if crack == "none": - self.submodels[ - f"{domain} particle mechanics" - ] = pybamm.particle_mechanics.NoMechanics( - self.param, domain, options=self.options, phase="primary" + self.submodels[f"{domain} particle mechanics"] = ( + pybamm.particle_mechanics.NoMechanics( + self.param, domain, options=self.options, phase="primary" + ) ) elif crack == "swelling only": - self.submodels[ - f"{domain} particle mechanics" - ] = pybamm.particle_mechanics.SwellingOnly( - self.param, domain, options=self.options, phase="primary" + self.submodels[f"{domain} particle mechanics"] = ( + pybamm.particle_mechanics.SwellingOnly( + self.param, domain, options=self.options, phase="primary" + ) ) elif crack == "swelling and cracking": - self.submodels[ - f"{domain} particle mechanics" - ] = pybamm.particle_mechanics.CrackPropagation( - self.param, - domain, - self.x_average, - options=self.options, - phase="primary", + self.submodels[f"{domain} particle mechanics"] = ( + pybamm.particle_mechanics.CrackPropagation( + self.param, + domain, + self.x_average, + options=self.options, + phase="primary", + ) ) def set_active_material_submodel(self): @@ -396,9 +404,9 @@ def set_active_material_submodel(self): # Submodel for the total active material, summing up each phase if len(phases) > 1: - self.submodels[ - f"{domain} total active material" - ] = pybamm.active_material.Total(self.param, domain, self.options) + self.submodels[f"{domain} total active material"] = ( + pybamm.active_material.Total(self.param, domain, self.options) + ) def set_porosity_submodel(self): if ( @@ -427,41 +435,45 @@ def set_li_metal_counter_electrode_submodels(self): and self.options["surface form"] == "false" ): # only symmetric Butler-Volmer can be inverted - self.submodels[ - f"{domain} electrode potential" - ] = pybamm.electrode.ohm.LithiumMetalExplicit( - self.param, domain, self.options + self.submodels[f"{domain} electrode potential"] = ( + pybamm.electrode.ohm.LithiumMetalExplicit( + self.param, domain, self.options + ) ) - self.submodels[ - f"{domain} electrode interface" - ] = pybamm.kinetics.InverseButlerVolmer( - self.param, domain, "lithium metal plating", self.options + self.submodels[f"{domain} electrode interface"] = ( + pybamm.kinetics.InverseButlerVolmer( + self.param, domain, "lithium metal plating", self.options + ) ) # assuming symmetric reaction for now so we can take the inverse - self.submodels[ - f"{domain} electrode interface current" - ] = pybamm.kinetics.CurrentForInverseButlerVolmerLithiumMetal( - self.param, domain, "lithium metal plating", self.options + self.submodels[f"{domain} electrode interface current"] = ( + pybamm.kinetics.CurrentForInverseButlerVolmerLithiumMetal( + self.param, domain, "lithium metal plating", self.options + ) ) else: - self.submodels[ - f"{domain} electrode potential" - ] = pybamm.electrode.ohm.LithiumMetalSurfaceForm( - self.param, domain, self.options + self.submodels[f"{domain} electrode potential"] = ( + pybamm.electrode.ohm.LithiumMetalSurfaceForm( + self.param, domain, self.options + ) ) neg_intercalation_kinetics = self.get_intercalation_kinetics(domain) - self.submodels[ - f"{domain} electrode interface" - ] = neg_intercalation_kinetics( - self.param, domain, "lithium metal plating", self.options, "primary" + self.submodels[f"{domain} electrode interface"] = ( + neg_intercalation_kinetics( + self.param, + domain, + "lithium metal plating", + self.options, + "primary", + ) ) def set_convection_submodel(self): - self.submodels[ - "transverse convection" - ] = pybamm.convection.transverse.NoConvection(self.param, self.options) - self.submodels[ - "through-cell convection" - ] = pybamm.convection.through_cell.NoConvection(self.param, self.options) + self.submodels["transverse convection"] = ( + pybamm.convection.transverse.NoConvection(self.param, self.options) + ) + self.submodels["through-cell convection"] = ( + pybamm.convection.through_cell.NoConvection(self.param, self.options) + ) def insert_reference_electrode(self, position=None): """ diff --git a/pybamm/models/full_battery_models/lithium_ion/basic_dfn.py b/pybamm/models/full_battery_models/lithium_ion/basic_dfn.py index 5b38926699..08809b645f 100644 --- a/pybamm/models/full_battery_models/lithium_ion/basic_dfn.py +++ b/pybamm/models/full_battery_models/lithium_ion/basic_dfn.py @@ -34,6 +34,7 @@ def __init__(self, name="Doyle-Fuller-Newman model"): ###################### # Variables that depend on time only are created without a domain Q = pybamm.Variable("Discharge capacity [A.h]") + # Variables that vary spatially are created with a domain c_e_n = pybamm.Variable( "Negative electrolyte concentration [mol.m-3]", @@ -240,18 +241,32 @@ def __init__(self, name="Doyle-Fuller-Newman model"): # (Some) variables ###################### voltage = pybamm.boundary_value(phi_s_p, "right") + num_cells = pybamm.Parameter( + "Number of cells connected in series to make a battery" + ) # The `variables` dictionary contains all variables that might be useful for # visualising the solution of the model self.variables = { + "Negative particle concentration [mol.m-3]": c_s_n, "Negative particle surface concentration [mol.m-3]": c_s_surf_n, "Electrolyte concentration [mol.m-3]": c_e, + "Negative electrolyte concentration [mol.m-3]": c_e_n, + "Separator electrolyte concentration [mol.m-3]": c_e_s, + "Positive electrolyte concentration [mol.m-3]": c_e_p, + "Positive particle concentration [mol.m-3]": c_s_p, "Positive particle surface concentration [mol.m-3]": c_s_surf_p, "Current [A]": I, + "Current variable [A]": I, # for compatibility with pybamm.Experiment "Negative electrode potential [V]": phi_s_n, "Electrolyte potential [V]": phi_e, + "Negative electrolyte potential [V]": phi_e_n, + "Separator electrolyte potential [V]": phi_e_s, + "Positive electrolyte potential [V]": phi_e_p, "Positive electrode potential [V]": phi_s_p, "Voltage [V]": voltage, + "Battery voltage [V]": voltage * num_cells, "Time [s]": pybamm.t, + "Discharge capacity [A.h]": Q, } # Events specify points at which a solution should terminate self.events += [ diff --git a/pybamm/models/full_battery_models/lithium_ion/basic_dfn_composite.py b/pybamm/models/full_battery_models/lithium_ion/basic_dfn_composite.py index 76889d28ea..95f65f4d50 100644 --- a/pybamm/models/full_battery_models/lithium_ion/basic_dfn_composite.py +++ b/pybamm/models/full_battery_models/lithium_ion/basic_dfn_composite.py @@ -341,18 +341,40 @@ def __init__(self, name="Composite graphite/silicon Doyle-Fuller-Newman model"): ocp_av_p = pybamm.x_average(ocp_p) a_j_n_p1_av = pybamm.x_average(a_j_n_p1) a_j_n_p2_av = pybamm.x_average(a_j_n_p2) + num_cells = pybamm.Parameter( + "Number of cells connected in series to make a battery" + ) # The `variables` dictionary contains all variables that might be useful for # visualising the solution of the model self.variables = { "Negative primary particle concentration [mol.m-3]": c_s_n_p1, "Negative secondary particle concentration [mol.m-3]": c_s_n_p2, + "R-averaged negative primary particle concentration " + "[mol.m-3]": c_s_rav_n_p1, + "R-averaged negative secondary particle concentration " + "[mol.m-3]": c_s_rav_n_p2, + "Average negative primary particle concentration " + "[mol.m-3]": c_s_xrav_n_p1, + "Average negative secondary particle concentration " + "[mol.m-3]": c_s_xrav_n_p2, + "Positive particle concentration [mol.m-3]": c_s_p, + "Average positive particle concentration [mol.m-3]": c_s_xrav_p, + "Electrolyte concentration [mol.m-3]": c_e, + "Negative electrolyte concentration [mol.m-3]": c_e_n, + "Separator electrolyte concentration [mol.m-3]": c_e_s, + "Positive electrolyte concentration [mol.m-3]": c_e_p, "Negative electrode potential [V]": phi_s_n, - "Electrolyte potential [V]": phi_e, "Positive electrode potential [V]": phi_s_p, + "Electrolyte potential [V]": phi_e, + "Negative electrolyte potential [V]": phi_e_n, + "Separator electrolyte potential [V]": phi_e_s, + "Positive electrolyte potential [V]": phi_e_p, "Current [A]": I, + "Current variable [A]": I, # for compatibility with pybamm.Experiment "Discharge capacity [A.h]": Q, "Time [s]": pybamm.t, "Voltage [V]": voltage, + "Battery voltage [V]": voltage * num_cells, "Negative electrode primary open-circuit potential [V]": ocp_n_p1, "Negative electrode secondary open-circuit potential [V]": ocp_n_p2, "X-averaged negative electrode primary open-circuit potential " @@ -361,15 +383,6 @@ def __init__(self, name="Composite graphite/silicon Doyle-Fuller-Newman model"): "[V]": ocp_av_n_p2, "Positive electrode open-circuit potential [V]": ocp_p, "X-averaged positive electrode open-circuit potential [V]": ocp_av_p, - "R-averaged negative primary particle concentration " - "[mol.m-3]": c_s_rav_n_p1, - "R-averaged negative secondary particle concentration " - "[mol.m-3]": c_s_rav_n_p2, - "Average negative primary particle concentration " - "[mol.m-3]": c_s_xrav_n_p1, - "Average negative secondary particle concentration " - "[mol.m-3]": c_s_xrav_n_p2, - "Average positive particle concentration [mol.m-3]": c_s_xrav_p, "Negative electrode primary interfacial current density [A.m-2]": j_n_p1, "Negative electrode secondary interfacial current density [A.m-2]": j_n_p2, "X-averaged negative electrode primary interfacial current density " @@ -385,7 +398,12 @@ def __init__(self, name="Composite graphite/silicon Doyle-Fuller-Newman model"): "X-averaged negative electrode secondary volumetric " "interfacial current density [A.m-3]": a_j_n_p2_av, } + # Events specify points at which a solution should terminate self.events += [ pybamm.Event("Minimum voltage [V]", voltage - param.voltage_low_cut), pybamm.Event("Maximum voltage [V]", param.voltage_high_cut - voltage), ] + + @property + def default_parameter_values(self): + return pybamm.ParameterValues("Chen2020_composite") diff --git a/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py b/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py index f8379fffec..bc1eba3a83 100644 --- a/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py +++ b/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py @@ -30,6 +30,7 @@ class BasicDFNHalfCell(BaseModel): """ def __init__(self, options=None, name="Doyle-Fuller-Newman half cell model"): + options = {"working electrode": "positive"} super().__init__(options, name) pybamm.citations.register("Marquis2019") # `param` is a class containing all the relevant parameters and functions for @@ -221,8 +222,8 @@ def __init__(self, options=None, name="Doyle-Fuller-Newman half cell model"): self.algebraic[phi_e] = param.L_x**2 * (pybamm.div(i_e) - a_j) # reference potential - L_Li = param.p.L - sigma_Li = param.p.sigma + L_Li = param.n.L + sigma_Li = param.n.sigma j_Li = param.j0_Li_metal(pybamm.boundary_value(c_e, "left"), c_w_max, T) eta_Li = 2 * RT_F * pybamm.arcsinh(i_cell / (2 * j_Li)) @@ -244,6 +245,10 @@ def __init__(self, options=None, name="Doyle-Fuller-Newman half cell model"): vdrop_cell = pybamm.boundary_value(phi_s_w, "right") - ref_potential vdrop_Li = -eta_Li - delta_phis_Li voltage = vdrop_cell + vdrop_Li + num_cells = pybamm.Parameter( + "Number of cells connected in series to make a battery" + ) + c_e_total = pybamm.x_average(eps * c_e) c_s_surf_w_av = pybamm.x_average(c_s_surf_w) @@ -280,22 +285,29 @@ def __init__(self, options=None, name="Doyle-Fuller-Newman half cell model"): # visualising the solution of the model self.variables = { "Time [s]": pybamm.t, + "Discharge capacity [A.h]": Q, "Positive particle surface concentration [mol.m-3]": c_s_surf_w, "X-averaged positive particle surface concentration " "[mol.m-3]": c_s_surf_w_av, "Positive particle concentration [mol.m-3]": c_s_w, "Total lithium in positive electrode [mol]": c_s_vol_av * L_w * param.A_cc, "Electrolyte concentration [mol.m-3]": c_e, + "Separator electrolyte concentration [mol.m-3]": c_e_s, + "Positive electrolyte concentration [mol.m-3]": c_e_w, "Total lithium in electrolyte [mol]": c_e_total * param.L_x * param.A_cc, "Current [A]": I, + "Current variable [A]": I, # for compatibility with pybamm.Experiment "Current density [A.m-2]": i_cell, "Positive electrode potential [V]": phi_s_w, "Positive electrode open-circuit potential [V]": U_w(sto_surf_w, T), "Electrolyte potential [V]": phi_e, + "Separator electrolyte potential [V]": phi_e_s, + "Positive electrolyte potential [V]": phi_e_w, "Voltage drop in the cell [V]": vdrop_cell, "Negative electrode exchange current density [A.m-2]": j_Li, "Negative electrode reaction overpotential [V]": eta_Li, "Negative electrode potential drop [V]": delta_phis_Li, "Voltage [V]": voltage, + "Battery voltage [V]": voltage * num_cells, "Instantaneous power [W.m-2]": i_cell * voltage, } diff --git a/pybamm/models/full_battery_models/lithium_ion/basic_spm.py b/pybamm/models/full_battery_models/lithium_ion/basic_spm.py index 802f8037e3..6bd93f3b27 100644 --- a/pybamm/models/full_battery_models/lithium_ion/basic_spm.py +++ b/pybamm/models/full_battery_models/lithium_ion/basic_spm.py @@ -139,6 +139,9 @@ def __init__(self, name="Single Particle Model"): phi_e = -eta_n - param.n.prim.U(sto_surf_n, T) phi_s_p = eta_p + phi_e + param.p.prim.U(sto_surf_p, T) V = phi_s_p + num_cells = pybamm.Parameter( + "Number of cells connected in series to make a battery" + ) whole_cell = ["negative electrode", "separator", "positive electrode"] # The `variables` dictionary contains all variables that might be useful for @@ -146,7 +149,9 @@ def __init__(self, name="Single Particle Model"): # Primary broadcasts are used to broadcast scalar quantities across a domain # into a vector of the right shape, for multiplying with other vectors self.variables = { + "Time [s]": pybamm.t, "Discharge capacity [A.h]": Q, + "X-averaged negative particle concentration [mol.m-3]": c_s_n, "Negative particle surface " "concentration [mol.m-3]": pybamm.PrimaryBroadcast( c_s_surf_n, "negative electrode" @@ -154,11 +159,13 @@ def __init__(self, name="Single Particle Model"): "Electrolyte concentration [mol.m-3]": pybamm.PrimaryBroadcast( param.c_e_init_av, whole_cell ), + "X-averaged positive particle concentration [mol.m-3]": c_s_p, "Positive particle surface " "concentration [mol.m-3]": pybamm.PrimaryBroadcast( c_s_surf_p, "positive electrode" ), "Current [A]": I, + "Current variable [A]": I, # for compatibility with pybamm.Experiment "Negative electrode potential [V]": pybamm.PrimaryBroadcast( phi_s_n, "negative electrode" ), @@ -167,7 +174,9 @@ def __init__(self, name="Single Particle Model"): phi_s_p, "positive electrode" ), "Voltage [V]": V, + "Battery voltage [V]": V * num_cells, } + # Events specify points at which a solution should terminate self.events += [ pybamm.Event("Minimum voltage [V]", V - param.voltage_low_cut), pybamm.Event("Maximum voltage [V]", param.voltage_high_cut - V), diff --git a/pybamm/models/full_battery_models/lithium_ion/dfn.py b/pybamm/models/full_battery_models/lithium_ion/dfn.py index db4e0282d8..c22d74ef68 100644 --- a/pybamm/models/full_battery_models/lithium_ion/dfn.py +++ b/pybamm/models/full_battery_models/lithium_ion/dfn.py @@ -41,10 +41,10 @@ def set_intercalation_kinetics_submodel(self): self.submodels[f"{domain} {phase} interface"] = submod if len(phases) > 1: - self.submodels[ - f"total {domain} interface" - ] = pybamm.kinetics.TotalMainKinetics( - self.param, domain, "lithium-ion main", self.options + self.submodels[f"total {domain} interface"] = ( + pybamm.kinetics.TotalMainKinetics( + self.param, domain, "lithium-ion main", self.options + ) ) def set_particle_submodel(self): @@ -69,11 +69,22 @@ def set_particle_submodel(self): submod = pybamm.particle.MSMRDiffusion( self.param, domain, self.options, phase=phase, x_average=False ) + # also set the submodel for calculating stoichiometry from + # potential + self.submodels[f"{domain} {phase} stoichiometry"] = ( + pybamm.particle.MSMRStoichiometryVariables( + self.param, + domain, + self.options, + phase=phase, + x_average=False, + ) + ) self.submodels[f"{domain} {phase} particle"] = submod - self.submodels[ - f"{domain} {phase} total particle concentration" - ] = pybamm.particle.TotalConcentration( - self.param, domain, self.options, phase + self.submodels[f"{domain} {phase} total particle concentration"] = ( + pybamm.particle.TotalConcentration( + self.param, domain, self.options, phase + ) ) def set_solid_submodel(self): @@ -104,9 +115,9 @@ def set_electrolyte_potential_submodel(self): ) if self.options["surface form"] == "false": - self.submodels[ - "electrolyte conductivity" - ] = pybamm.electrolyte_conductivity.Full(self.param, self.options) + self.submodels["electrolyte conductivity"] = ( + pybamm.electrolyte_conductivity.Full(self.param, self.options) + ) if self.options["surface form"] == "false": surf_model = surf_form.Explicit @@ -121,3 +132,6 @@ def set_electrolyte_potential_submodel(self): self.submodels[f"{domain} surface potential difference"] = surf_model( self.param, domain, self.options ) + + def set_summary_variables(self): + self.set_default_summary_variables() diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py index d975de859c..a5710dc986 100644 --- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py +++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py @@ -4,7 +4,6 @@ import pybamm import numpy as np from functools import lru_cache -import warnings class _BaseElectrodeSOH(pybamm.BaseModel): @@ -113,8 +112,8 @@ def __init__( Up = param.p.prim.U T_ref = param.T_ref - V_max = param.ocp_soc_100_dimensional - V_min = param.ocp_soc_0_dimensional + V_max = param.ocp_soc_100 + V_min = param.ocp_soc_0 Q_n = pybamm.InputParameter("Q_n") Q_p = pybamm.InputParameter("Q_p") @@ -122,6 +121,10 @@ def __init__( Q_Li = pybamm.InputParameter("Q_Li") elif known_value == "cell capacity": Q = pybamm.InputParameter("Q") + else: + raise ValueError( + "Known value must be cell capacity or cyclable lithium capacity" + ) # Define variables for 100% state of charge if "x_100" in solve_for: @@ -199,6 +202,7 @@ def __init__( x_n = param.n.prim.x x_p = param.p.prim.x + T = param.T_ref V_max = param.voltage_high_cut V_min = param.voltage_low_cut Q_n = pybamm.InputParameter("Q_n") @@ -208,27 +212,31 @@ def __init__( Q_Li = pybamm.InputParameter("Q_Li") elif known_value == "cell capacity": Q = pybamm.InputParameter("Q") + else: + raise ValueError( + "Known value must be cell capacity or cyclable lithium capacity" + ) # Define variables for 0% state of charge # TODO: thermal effects (include dU/dT) if "Un_0" in solve_for: Un_0 = pybamm.Variable("Un(x_0)") Up_0 = V_min + Un_0 - x_0 = x_n(Un_0) - y_0 = x_p(Up_0) + x_0 = x_n(Un_0, T) + y_0 = x_p(Up_0, T) # Define variables for 100% state of charge # TODO: thermal effects (include dU/dT) if "Un_100" in solve_for: Un_100 = pybamm.Variable("Un(x_100)") Up_100 = V_max + Un_100 - x_100 = x_n(Un_100) - y_100 = x_p(Up_100) + x_100 = x_n(Un_100, T) + y_100 = x_p(Up_100, T) else: Un_100 = pybamm.InputParameter("Un(x_100)") Up_100 = pybamm.InputParameter("Up(y_100)") - x_100 = x_n(Un_100) - y_100 = x_p(Up_100) + x_100 = x_n(Un_100, T) + y_100 = x_p(Up_100, T) # Define equations for 100% state of charge if "Un_100" in solve_for: @@ -288,6 +296,10 @@ def __init__( ): self.parameter_values = parameter_values self.param = param or pybamm.LithiumIonParameters(options) + if known_value not in ["cell capacity", "cyclable lithium capacity"]: + raise ValueError( + "Known value must be cell capacity or cyclable lithium capacity" + ) self.known_value = known_value self.options = options or pybamm.BatteryModelOptions({}) @@ -363,35 +375,6 @@ def __get_electrode_soh_sims_split(self): return [x100_sim, x0_sim] def solve(self, inputs): - if "n_Li" in inputs: - warnings.warn( - "Input 'n_Li' has been replaced by 'Q_Li', which is 'n_Li * F / 3600'. " - "This will be automatically calculated for now. " - "Q_Li can be read from parameters as 'param.Q_Li_particles_init'", - DeprecationWarning, - ) - n_Li = inputs.pop("n_Li") - inputs["Q_Li"] = n_Li * pybamm.constants.F.value / 3600 - if "C_n" in inputs: - warnings.warn("Input 'C_n' has been renamed to 'Q_n'", DeprecationWarning) - inputs["Q_n"] = inputs.pop("C_n") - if "C_p" in inputs: - warnings.warn("Input 'C_p' has been renamed to 'Q_p'", DeprecationWarning) - inputs["Q_p"] = inputs.pop("C_p") - if inputs.pop("V_min", None) is not None: - warnings.warn( - "V_min has been removed from the inputs. " - "The 'Open-circuit voltage at 0% SOC [V]' " - "parameter is now used automatically.", - DeprecationWarning, - ) - if inputs.pop("V_max", None) is not None: - warnings.warn( - "V_max has been removed from the inputs. " - "The 'Open-circuit voltage at 100% SOC [V]' " - "parameter is now used automatically.", - DeprecationWarning, - ) ics = self._set_up_solve(inputs) try: sol = self._solve_full(inputs, ics) @@ -410,7 +393,8 @@ def solve(self, inputs): # Calculate theoretical energy # TODO: energy calc for MSMR if self.options["open-circuit potential"] != "MSMR": - energy = self.theoretical_energy_integral(sol_dict) + energy_inputs = {**sol_dict, **inputs} + energy = self.theoretical_energy_integral(energy_inputs) sol_dict.update({"Maximum theoretical energy [W.h]": energy}) return sol_dict @@ -556,8 +540,6 @@ def _get_lims(self, inputs): f"Q_Li={Q_Li:.4f} Ah is outside the range of possible values " f"[{Q_Li_min:.4f}, {Q_Li_max:.4f}]." ) - if Q_Li > Q_p: - warnings.warn(f"Q_Li={Q_Li:.4f} Ah is greater than Q_p={Q_p:.4f} Ah.") # Update (tighten) stoich limits based on total lithium content and # electrode capacities @@ -598,12 +580,8 @@ def _check_esoh_feasible(self, inputs): # Parameterize the OCP functions if self.OCV_function is None: - self.V_max = self.parameter_values.evaluate( - self.param.ocp_soc_100_dimensional - ) - self.V_min = self.parameter_values.evaluate( - self.param.ocp_soc_0_dimensional - ) + self.V_max = self.parameter_values.evaluate(self.param.ocp_soc_100) + self.V_min = self.parameter_values.evaluate(self.param.ocp_soc_0) if self.options["open-circuit potential"] == "MSMR": # will solve for potentials at the sto limits, so no need # to store a function @@ -636,12 +614,10 @@ def _check_esoh_feasible(self, inputs): else: # address numpy 1.25 deprecation warning: array should have ndim=0 # before conversion - V_lower_bound = float( - self.OCV_function.evaluate(inputs={"x": x0_min, "y": y0_max}).item() - ) - V_upper_bound = float( - self.OCV_function.evaluate(inputs={"x": x100_max, "y": y100_min}).item() - ) + all_inputs = {**inputs, "x": x0_min, "y": y0_max} + V_lower_bound = float(self.OCV_function.evaluate(inputs=all_inputs).item()) + all_inputs.update({"x": x100_max, "y": y100_min}) + V_upper_bound = float(self.OCV_function.evaluate(inputs=all_inputs).item()) # Check that the min and max achievable voltages span wider than the desired # voltage range @@ -664,7 +640,7 @@ def _check_esoh_feasible(self, inputs): ) ) - def get_initial_stoichiometries(self, initial_value): + def get_initial_stoichiometries(self, initial_value, tol=1e-6, inputs=None): """ Calculate initial stoichiometries to start off the simulation at a particular state of charge, given voltage limits, open-circuit potentials, etc defined by @@ -677,6 +653,10 @@ def get_initial_stoichiometries(self, initial_value): If integer, interpreted as SOC, must be between 0 and 1. If string e.g. "4 V", interpreted as voltage, must be between V_min and V_max. + tol : float, optional + The tolerance for the solver used to compute the initial stoichiometries. + A lower value results in higher precision but may increase computation time. + Default is 1e-6. Returns ------- @@ -685,14 +665,14 @@ def get_initial_stoichiometries(self, initial_value): """ parameter_values = self.parameter_values param = self.param - x_0, x_100, y_100, y_0 = self.get_min_max_stoichiometries() + x_0, x_100, y_100, y_0 = self.get_min_max_stoichiometries(inputs=inputs) if isinstance(initial_value, str) and initial_value.endswith("V"): V_init = float(initial_value[:-1]) - V_min = parameter_values.evaluate(param.ocp_soc_0_dimensional) - V_max = parameter_values.evaluate(param.ocp_soc_100_dimensional) + V_min = parameter_values.evaluate(param.ocp_soc_0) + V_max = parameter_values.evaluate(param.ocp_soc_100) - if not V_min < V_init < V_max: + if not V_min <= V_init <= V_max: raise ValueError( f"Initial voltage {V_init}V is outside the voltage limits " f"({V_min}, {V_max})" @@ -703,27 +683,29 @@ def get_initial_stoichiometries(self, initial_value): soc = pybamm.Variable("soc") x = x_0 + soc * (x_100 - x_0) y = y_0 - soc * (y_0 - y_100) + T_ref = parameter_values["Reference temperature [K]"] if self.options["open-circuit potential"] == "MSMR": xn = param.n.prim.x xp = param.p.prim.x Up = pybamm.Variable("Up") Un = pybamm.Variable("Un") - soc_model.algebraic[Up] = x - xn(Un) - soc_model.algebraic[Un] = y - xp(Up) + soc_model.algebraic[Up] = x - xn(Un, T_ref) + soc_model.algebraic[Un] = y - xp(Up, T_ref) soc_model.initial_conditions[Un] = 0 soc_model.initial_conditions[Up] = V_max soc_model.algebraic[soc] = Up - Un - V_init else: Up = param.p.prim.U Un = param.n.prim.U - T_ref = parameter_values["Reference temperature [K]"] soc_model.algebraic[soc] = Up(y, T_ref) - Un(x, T_ref) - V_init # initial guess for soc linearly interpolates between 0 and 1 # based on V linearly interpolating between V_max and V_min soc_model.initial_conditions[soc] = (V_init - V_min) / (V_max - V_min) soc_model.variables["soc"] = soc parameter_values.process_model(soc_model) - initial_soc = pybamm.AlgebraicSolver().solve(soc_model, [0])["soc"].data[0] + initial_soc = ( + pybamm.AlgebraicSolver(tol=tol).solve(soc_model, [0])["soc"].data[0] + ) elif isinstance(initial_value, (int, float)): initial_soc = initial_value if not 0 <= initial_soc <= 1: @@ -740,7 +722,7 @@ def get_initial_stoichiometries(self, initial_value): return x, y - def get_min_max_stoichiometries(self): + def get_min_max_stoichiometries(self, inputs=None): """ Calculate min/max stoichiometries given voltage limits, open-circuit potentials, etc defined by parameter_values @@ -750,23 +732,26 @@ def get_min_max_stoichiometries(self): x_0, x_100, y_100, y_0 The min/max stoichiometries """ + inputs = inputs or {} parameter_values = self.parameter_values param = self.param - Q_n = parameter_values.evaluate(param.n.Q_init) - Q_p = parameter_values.evaluate(param.p.Q_init) + Q_n = parameter_values.evaluate(param.n.Q_init, inputs=inputs) + Q_p = parameter_values.evaluate(param.p.Q_init, inputs=inputs) if self.known_value == "cyclable lithium capacity": - Q_Li = parameter_values.evaluate(param.Q_Li_particles_init) - inputs = {"Q_n": Q_n, "Q_p": Q_p, "Q_Li": Q_Li} + Q_Li = parameter_values.evaluate(param.Q_Li_particles_init, inputs=inputs) + all_inputs = {**inputs, "Q_n": Q_n, "Q_p": Q_p, "Q_Li": Q_Li} elif self.known_value == "cell capacity": - Q = parameter_values.evaluate(param.Q / param.n_electrodes_parallel) - inputs = {"Q_n": Q_n, "Q_p": Q_p, "Q": Q} + Q = parameter_values.evaluate( + param.Q / param.n_electrodes_parallel, inputs=inputs + ) + all_inputs = {**inputs, "Q_n": Q_n, "Q_p": Q_p, "Q": Q} # Solve the model and check outputs - sol = self.solve(inputs) + sol = self.solve(all_inputs) return [sol["x_0"], sol["x_100"], sol["y_100"], sol["y_0"]] - def get_initial_ocps(self, initial_value): + def get_initial_ocps(self, initial_value, tol=1e-6): """ Calculate initial open-circuit potentials to start off the simulation at a particular state of charge, given voltage limits, open-circuit potentials, etc @@ -776,6 +761,8 @@ def get_initial_ocps(self, initial_value): ---------- initial_value : float Target SOC, must be between 0 and 1. + tol: float, optional + Tolerance for the solver used in calculating initial stoichiometries. Returns ------- @@ -784,7 +771,7 @@ def get_initial_ocps(self, initial_value): """ parameter_values = self.parameter_values param = self.param - x, y = self.get_initial_stoichiometries(initial_value) + x, y = self.get_initial_stoichiometries(initial_value, tol) if self.options["open-circuit potential"] == "MSMR": msmr_pot_model = _get_msmr_potential_model( self.parameter_values, self.param @@ -838,7 +825,7 @@ def theoretical_energy_integral(self, inputs, points=1000): param = self.param T = param.T_amb_av(0) Vs = self.parameter_values.evaluate( - param.p.prim.U(y_vals, T) - param.n.prim.U(x_vals, T) + param.p.prim.U(y_vals, T) - param.n.prim.U(x_vals, T), inputs=inputs ).flatten() # Calculate dQ Q = Q_p * (y_0 - y_100) @@ -854,6 +841,8 @@ def get_initial_stoichiometries( param=None, known_value="cyclable lithium capacity", options=None, + tol=1e-6, + inputs=None, ): """ Calculate initial stoichiometries to start off the simulation at a particular @@ -878,6 +867,10 @@ def get_initial_stoichiometries( options : dict-like, optional A dictionary of options to be passed to the model, see :class:`pybamm.BatteryModelOptions`. + tol : float, optional + The tolerance for the solver used to compute the initial stoichiometries. + A lower value results in higher precision but may increase computation time. + Default is 1e-6. Returns ------- @@ -885,7 +878,7 @@ def get_initial_stoichiometries( The initial stoichiometries that give the desired initial state of charge """ esoh_solver = ElectrodeSOHSolver(parameter_values, param, known_value, options) - return esoh_solver.get_initial_stoichiometries(initial_value) + return esoh_solver.get_initial_stoichiometries(initial_value, tol, inputs=inputs) def get_min_max_stoichiometries( @@ -1014,7 +1007,7 @@ def theoretical_energy_integral(parameter_values, param, inputs, points=100): def calculate_theoretical_energy( - parameter_values, initial_soc=1.0, final_soc=0.0, points=100 + parameter_values, initial_soc=1.0, final_soc=0.0, points=100, tol=1e-6 ): """ Calculate maximum energy possible from a cell given OCV, initial soc, and final soc @@ -1030,14 +1023,16 @@ def calculate_theoretical_energy( The soc at end of discharge, default 0.0 points : int The number of points at which to calculate voltage. + tol: float + Tolerance for the solver used in calculating initial and final stoichiometries. Returns ------- E The total energy of the cell in Wh """ # Get initial and final stoichiometric values. - x_100, y_100 = get_initial_stoichiometries(initial_soc, parameter_values) - x_0, y_0 = get_initial_stoichiometries(final_soc, parameter_values) + x_100, y_100 = get_initial_stoichiometries(initial_soc, parameter_values, tol=tol) + x_0, y_0 = get_initial_stoichiometries(final_soc, parameter_values, tol=tol) Q_p = parameter_values.evaluate(pybamm.LithiumIonParameters().p.prim.Q_init) E = theoretical_energy_integral( parameter_values, @@ -1057,14 +1052,15 @@ def _get_msmr_potential_model(parameter_values, param): V_min = param.voltage_low_cut x_n = param.n.prim.x x_p = param.p.prim.x + T = param.T_ref model = pybamm.BaseModel() Un = pybamm.Variable("Un") Up = pybamm.Variable("Up") x = pybamm.InputParameter("x") y = pybamm.InputParameter("y") model.algebraic = { - Un: x_n(Un) - x, - Up: x_p(Up) - y, + Un: x_n(Un, T) - x, + Up: x_p(Up, T) - y, } model.initial_conditions = { Un: 1 - x, diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py index 8c22cf2ada..be7ced642e 100644 --- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py +++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py @@ -33,8 +33,8 @@ def __init__(self, name="ElectrodeSOH model"): U_w = param.p.prim.U Q = Q_w * (x_100 - x_0) - V_max = param.ocp_soc_100_dimensional - V_min = param.ocp_soc_0_dimensional + V_max = param.ocp_soc_100 + V_min = param.ocp_soc_0 self.algebraic = { x_100: U_w(x_100, T_ref) - V_max, @@ -62,8 +62,9 @@ def get_initial_stoichiometry_half_cell( initial_value, parameter_values, param=None, - known_value="cyclable lithium capacity", options=None, + inputs=None, + **kwargs, ): """ Calculate initial stoichiometry to start off the simulation at a particular @@ -86,14 +87,14 @@ def get_initial_stoichiometry_half_cell( The initial stoichiometry that give the desired initial state of charge """ param = pybamm.LithiumIonParameters(options) - x_0, x_100 = get_min_max_stoichiometries(parameter_values) + x_0, x_100 = get_min_max_stoichiometries(parameter_values, inputs=inputs) if isinstance(initial_value, str) and initial_value.endswith("V"): V_init = float(initial_value[:-1]) V_min = parameter_values.evaluate(param.voltage_low_cut) V_max = parameter_values.evaluate(param.voltage_high_cut) - if not V_min < V_init < V_max: + if not V_min <= V_init <= V_max: raise ValueError( f"Initial voltage {V_init}V is outside the voltage limits " f"({V_min}, {V_max})" @@ -129,9 +130,7 @@ def get_initial_stoichiometry_half_cell( return x -def get_min_max_stoichiometries( - parameter_values, options={"working electrode": "positive"} -): +def get_min_max_stoichiometries(parameter_values, options=None, inputs=None): """ Get the minimum and maximum stoichiometries from the parameter values @@ -139,11 +138,20 @@ def get_min_max_stoichiometries( ---------- parameter_values : pybamm.ParameterValues The parameter values to use in the calculation + options : dict, optional + A dictionary of options to be passed to the parameters, see + :class:`pybamm.BatteryModelOptions`. + If None, the default is used: {"working electrode": "positive"} """ + inputs = inputs or {} + if options is None: + options = {"working electrode": "positive"} esoh_model = pybamm.lithium_ion.ElectrodeSOHHalfCell("ElectrodeSOH") param = pybamm.LithiumIonParameters(options) + Q_w = parameter_values.evaluate(param.p.Q_init, inputs=inputs) + # Add Q_w to input parameters + all_inputs = {**inputs, "Q_w": Q_w} esoh_sim = pybamm.Simulation(esoh_model, parameter_values=parameter_values) - Q_w = parameter_values.evaluate(param.p.Q_init) - esoh_sol = esoh_sim.solve([0], inputs={"Q_w": Q_w}) + esoh_sol = esoh_sim.solve([0], inputs=all_inputs) x_0, x_100 = esoh_sol["x_0"].data[0], esoh_sol["x_100"].data[0] return x_0, x_100 diff --git a/pybamm/models/full_battery_models/lithium_ion/newman_tobias.py b/pybamm/models/full_battery_models/lithium_ion/newman_tobias.py index a704bd0b33..9ecd32708a 100644 --- a/pybamm/models/full_battery_models/lithium_ion/newman_tobias.py +++ b/pybamm/models/full_battery_models/lithium_ion/newman_tobias.py @@ -52,14 +52,25 @@ def set_particle_submodel(self): submod = pybamm.particle.MSMRDiffusion( self.param, domain, self.options, phase=phase, x_average=True ) + # also set the submodel for calculating stoichiometry from + # potential + self.submodels[f"{domain} {phase} stoichiometry"] = ( + pybamm.particle.MSMRStoichiometryVariables( + self.param, + domain, + self.options, + phase=phase, + x_average=True, + ) + ) self.submodels[f"{domain} {phase} particle"] = submod - self.submodels[ - f"{domain} {phase} total particle concentration" - ] = pybamm.particle.TotalConcentration( - self.param, domain, self.options, phase + self.submodels[f"{domain} {phase} total particle concentration"] = ( + pybamm.particle.TotalConcentration( + self.param, domain, self.options, phase + ) ) def set_electrolyte_concentration_submodel(self): - self.submodels[ - "electrolyte diffusion" - ] = pybamm.electrolyte_diffusion.ConstantConcentration(self.param) + self.submodels["electrolyte diffusion"] = ( + pybamm.electrolyte_diffusion.ConstantConcentration(self.param) + ) diff --git a/pybamm/models/full_battery_models/lithium_ion/spm.py b/pybamm/models/full_battery_models/lithium_ion/spm.py index 386c55ded9..a983db5577 100644 --- a/pybamm/models/full_battery_models/lithium_ion/spm.py +++ b/pybamm/models/full_battery_models/lithium_ion/spm.py @@ -65,10 +65,10 @@ def set_intercalation_kinetics_submodel(self): self.submodels[f"{domain} interface"] = inverse_intercalation_kinetics( self.param, domain, "lithium-ion main", self.options ) - self.submodels[ - f"{domain} interface current" - ] = pybamm.kinetics.CurrentForInverseButlerVolmer( - self.param, domain, "lithium-ion main", self.options + self.submodels[f"{domain} interface current"] = ( + pybamm.kinetics.CurrentForInverseButlerVolmer( + self.param, domain, "lithium-ion main", self.options + ) ) else: intercalation_kinetics = self.get_intercalation_kinetics(domain) @@ -79,10 +79,10 @@ def set_intercalation_kinetics_submodel(self): ) self.submodels[f"{domain} {phase} interface"] = submod if len(phases) > 1: - self.submodels[ - f"total {domain} interface" - ] = pybamm.kinetics.TotalMainKinetics( - self.param, domain, "lithium-ion main", self.options + self.submodels[f"total {domain} interface"] = ( + pybamm.kinetics.TotalMainKinetics( + self.param, domain, "lithium-ion main", self.options + ) ) def set_particle_submodel(self): @@ -108,25 +108,36 @@ def set_particle_submodel(self): submod = pybamm.particle.MSMRDiffusion( self.param, domain, self.options, phase=phase, x_average=True ) + # also set the submodel for calculating stoichiometry from + # potential + self.submodels[f"{domain} {phase} stoichiometry"] = ( + pybamm.particle.MSMRStoichiometryVariables( + self.param, + domain, + self.options, + phase=phase, + x_average=True, + ) + ) self.submodels[f"{domain} {phase} particle"] = submod - self.submodels[ - f"{domain} {phase} total particle concentration" - ] = pybamm.particle.TotalConcentration( - self.param, domain, self.options, phase + self.submodels[f"{domain} {phase} total particle concentration"] = ( + pybamm.particle.TotalConcentration( + self.param, domain, self.options, phase + ) ) def set_solid_submodel(self): for domain in ["negative", "positive"]: if self.options.electrode_types[domain] == "planar": continue - self.submodels[ - f"{domain} electrode potential" - ] = pybamm.electrode.ohm.LeadingOrder(self.param, domain, self.options) + self.submodels[f"{domain} electrode potential"] = ( + pybamm.electrode.ohm.LeadingOrder(self.param, domain, self.options) + ) def set_electrolyte_concentration_submodel(self): - self.submodels[ - "electrolyte diffusion" - ] = pybamm.electrolyte_diffusion.ConstantConcentration(self.param, self.options) + self.submodels["electrolyte diffusion"] = ( + pybamm.electrolyte_diffusion.ConstantConcentration(self.param, self.options) + ) def set_electrolyte_potential_submodel(self): surf_form = pybamm.electrolyte_conductivity.surface_potential_form @@ -142,10 +153,10 @@ def set_electrolyte_potential_submodel(self): self.options["surface form"] == "false" or self.options.electrode_types["negative"] == "planar" ): - self.submodels[ - "leading-order electrolyte conductivity" - ] = pybamm.electrolyte_conductivity.LeadingOrder( - self.param, options=self.options + self.submodels["leading-order electrolyte conductivity"] = ( + pybamm.electrolyte_conductivity.LeadingOrder( + self.param, options=self.options + ) ) if self.options["surface form"] == "false": surf_model = surf_form.Explicit @@ -160,3 +171,6 @@ def set_electrolyte_potential_submodel(self): self.submodels[f"{domain} surface potential difference"] = surf_model( self.param, domain, options=self.options ) + + def set_summary_variables(self): + self.set_default_summary_variables() diff --git a/pybamm/models/full_battery_models/lithium_ion/spme.py b/pybamm/models/full_battery_models/lithium_ion/spme.py index 103f13415a..6e0784ff60 100644 --- a/pybamm/models/full_battery_models/lithium_ion/spme.py +++ b/pybamm/models/full_battery_models/lithium_ion/spme.py @@ -56,16 +56,16 @@ def set_electrolyte_potential_submodel(self): or self.options.electrode_types["negative"] == "planar" ): if self.options["electrolyte conductivity"] in ["default", "composite"]: - self.submodels[ - "electrolyte conductivity" - ] = pybamm.electrolyte_conductivity.Composite( - self.param, options=self.options + self.submodels["electrolyte conductivity"] = ( + pybamm.electrolyte_conductivity.Composite( + self.param, options=self.options + ) ) elif self.options["electrolyte conductivity"] == "integrated": - self.submodels[ - "electrolyte conductivity" - ] = pybamm.electrolyte_conductivity.Integrated( - self.param, options=self.options + self.submodels["electrolyte conductivity"] = ( + pybamm.electrolyte_conductivity.Integrated( + self.param, options=self.options + ) ) if self.options["surface form"] == "false": surf_model = surf_form.Explicit @@ -76,6 +76,6 @@ def set_electrolyte_potential_submodel(self): for domain in ["negative", "positive"]: if self.options.electrode_types[domain] == "porous": - self.submodels[ - f"{domain} surface potential difference [V]" - ] = surf_model(self.param, domain, self.options) + self.submodels[f"{domain} surface potential difference [V]"] = ( + surf_model(self.param, domain, self.options) + ) diff --git a/pybamm/models/full_battery_models/lithium_metal/dfn.py b/pybamm/models/full_battery_models/lithium_metal/dfn.py index a7ab8b99e2..48ec3cddf5 100644 --- a/pybamm/models/full_battery_models/lithium_metal/dfn.py +++ b/pybamm/models/full_battery_models/lithium_metal/dfn.py @@ -2,7 +2,7 @@ # Doyle-Fuller-Newman (DFN) Model # import pybamm -from ..lithium_ion.dfn import DFN as LithiumIonDFN +from pybamm.models.full_battery_models.lithium_ion.dfn import DFN as LithiumIonDFN class DFN(LithiumIonDFN): diff --git a/pybamm/models/submodels/__init__.py b/pybamm/models/submodels/__init__.py index e69de29bb2..68950d1744 100644 --- a/pybamm/models/submodels/__init__.py +++ b/pybamm/models/submodels/__init__.py @@ -0,0 +1,5 @@ +__all__ = ['active_material', 'base_submodel', 'convection', + 'current_collector', 'electrode', 'electrolyte_conductivity', + 'electrolyte_diffusion', 'equivalent_circuit_elements', + 'external_circuit', 'interface', 'oxygen_diffusion', 'particle', + 'particle_mechanics', 'porosity', 'thermal', 'transport_efficiency'] diff --git a/pybamm/models/submodels/active_material/__init__.py b/pybamm/models/submodels/active_material/__init__.py index cb263eb4ff..ce03867c30 100644 --- a/pybamm/models/submodels/active_material/__init__.py +++ b/pybamm/models/submodels/active_material/__init__.py @@ -2,3 +2,6 @@ from .constant_active_material import Constant from .loss_active_material import LossActiveMaterial from .total_active_material import Total + +__all__ = ['base_active_material', 'constant_active_material', + 'loss_active_material', 'total_active_material'] diff --git a/pybamm/models/submodels/base_submodel.py b/pybamm/models/submodels/base_submodel.py index 225ae83705..e120691edd 100644 --- a/pybamm/models/submodels/base_submodel.py +++ b/pybamm/models/submodels/base_submodel.py @@ -1,6 +1,3 @@ -# -# Base submodel class -# import pybamm @@ -33,29 +30,12 @@ class BaseSubModel(pybamm.BaseModel): ---------- param: parameter class The model parameter symbols - rhs: dict - A dictionary that maps expressions (variables) to expressions that represent - the rhs - algebraic: dict - A dictionary that maps expressions (variables) to expressions that represent - the algebraic equations. The algebraic expressions are assumed to equate - to zero. Note that all the variables in the model must exist in the keys of - `rhs` or `algebraic`. - initial_conditions: dict - A dictionary that maps expressions (variables) to expressions that represent - the initial conditions for the state variables y. The initial conditions for - algebraic variables are provided as initial guesses to a root finding algorithm - that calculates consistent initial conditions. boundary_conditions: dict A dictionary that maps expressions (variables) to expressions that represent the boundary conditions variables: dict A dictionary that maps strings to expressions that represent the useful variables - events: list - A list of events. Each event can either cause the solver to terminate - (e.g. concentration goes negative), or be used to inform the solver of the - existance of a discontinuity (e.g. discontinuity in the input current) """ def __init__( @@ -70,7 +50,6 @@ def __init__( super().__init__(name) self.domain = domain self.name = name - self.external = external if options is None or type(options) == dict: # noqa: E721 @@ -135,6 +114,17 @@ def domain(self, domain): def domain_Domain(self): return self._domain, self._Domain + def get_parameter_info(self, by_submodel=False): + """ + Extracts the parameter information and returns it as a dictionary. + To get a list of all parameter-like objects without extra information, + use :py:attr:`model.parameters`. + """ + raise NotImplementedError( + "Cannot use get_parameter_info OR print_parameter_info directly on a submodel. " + "Please use it on the full model." + ) + def get_fundamental_variables(self): """ A public method that creates and returns the variables in a submodel which can diff --git a/pybamm/models/submodels/convection/__init__.py b/pybamm/models/submodels/convection/__init__.py index cc0a0d086d..45f1b13a03 100644 --- a/pybamm/models/submodels/convection/__init__.py +++ b/pybamm/models/submodels/convection/__init__.py @@ -1,2 +1,4 @@ from .base_convection import BaseModel from . import through_cell, transverse + +__all__ = ['base_convection', 'through_cell', 'transverse'] diff --git a/pybamm/models/submodels/convection/through_cell/__init__.py b/pybamm/models/submodels/convection/through_cell/__init__.py index ddcd5b9e9b..7b31a94264 100644 --- a/pybamm/models/submodels/convection/through_cell/__init__.py +++ b/pybamm/models/submodels/convection/through_cell/__init__.py @@ -2,3 +2,6 @@ from .no_convection import NoConvection from .explicit_convection import Explicit from .full_convection import Full + +__all__ = ['base_through_cell_convection', 'explicit_convection', + 'full_convection', 'no_convection'] diff --git a/pybamm/models/submodels/convection/through_cell/base_through_cell_convection.py b/pybamm/models/submodels/convection/through_cell/base_through_cell_convection.py index 2a8eaa18ed..311b9dd7bf 100644 --- a/pybamm/models/submodels/convection/through_cell/base_through_cell_convection.py +++ b/pybamm/models/submodels/convection/through_cell/base_through_cell_convection.py @@ -2,7 +2,7 @@ # Base class for convection submodels in the through-cell direction # import pybamm -from ..base_convection import BaseModel +from pybamm.models.submodels.convection.base_convection import BaseModel class BaseThroughCellModel(BaseModel): diff --git a/pybamm/models/submodels/convection/transverse/__init__.py b/pybamm/models/submodels/convection/transverse/__init__.py index c2b2292168..9683fa0eb0 100644 --- a/pybamm/models/submodels/convection/transverse/__init__.py +++ b/pybamm/models/submodels/convection/transverse/__init__.py @@ -2,3 +2,6 @@ from .no_convection import NoConvection from .uniform_convection import Uniform from .full_convection import Full + +__all__ = ['base_transverse_convection', 'full_convection', 'no_convection', + 'uniform_convection'] diff --git a/pybamm/models/submodels/convection/transverse/base_transverse_convection.py b/pybamm/models/submodels/convection/transverse/base_transverse_convection.py index e73b586eae..0d5974bc90 100644 --- a/pybamm/models/submodels/convection/transverse/base_transverse_convection.py +++ b/pybamm/models/submodels/convection/transverse/base_transverse_convection.py @@ -2,7 +2,7 @@ # Base class for convection submodels in transverse directions # import pybamm -from ..base_convection import BaseModel +from pybamm.models.submodels.convection.base_convection import BaseModel class BaseTransverseModel(BaseModel): diff --git a/pybamm/models/submodels/current_collector/__init__.py b/pybamm/models/submodels/current_collector/__init__.py index b721f14394..75eae41a34 100644 --- a/pybamm/models/submodels/current_collector/__init__.py +++ b/pybamm/models/submodels/current_collector/__init__.py @@ -10,3 +10,6 @@ PotentialPair1plus1D, PotentialPair2plus1D, ) + +__all__ = ['base_current_collector', 'effective_resistance_current_collector', + 'homogeneous_current_collector', 'potential_pair'] diff --git a/pybamm/models/submodels/current_collector/effective_resistance_current_collector.py b/pybamm/models/submodels/current_collector/effective_resistance_current_collector.py index 1d50e1be7c..23001b9d02 100644 --- a/pybamm/models/submodels/current_collector/effective_resistance_current_collector.py +++ b/pybamm/models/submodels/current_collector/effective_resistance_current_collector.py @@ -76,9 +76,7 @@ def options(self, extra_options): options[name] = opt else: raise pybamm.OptionError( - "Option '{}' not recognised. Best matches are {}".format( - name, options.get_best_matches(name) - ) + f"Option '{name}' not recognised. Best matches are {options.get_best_matches(name)}" ) if options["dimensionality"] not in [1, 2]: diff --git a/pybamm/models/submodels/current_collector/potential_pair.py b/pybamm/models/submodels/current_collector/potential_pair.py index f2fd3aee83..68a9066da3 100644 --- a/pybamm/models/submodels/current_collector/potential_pair.py +++ b/pybamm/models/submodels/current_collector/potential_pair.py @@ -58,13 +58,12 @@ def set_algebraic(self, variables): } def set_initial_conditions(self, variables): - applied_current = self.param.current_with_time phi_s_cn = variables["Negative current collector potential [V]"] i_boundary_cc = variables["Current collector current density [A.m-2]"] self.initial_conditions = { phi_s_cn: pybamm.Scalar(0), - i_boundary_cc: applied_current, + i_boundary_cc: pybamm.Scalar(0), } diff --git a/pybamm/models/submodels/electrode/__init__.py b/pybamm/models/submodels/electrode/__init__.py index ce60b7e1e7..c9abc0aeab 100644 --- a/pybamm/models/submodels/electrode/__init__.py +++ b/pybamm/models/submodels/electrode/__init__.py @@ -1,2 +1,4 @@ from .base_electrode import BaseElectrode from . import ohm + +__all__ = ['base_electrode', 'ohm'] diff --git a/pybamm/models/submodels/electrode/ohm/__init__.py b/pybamm/models/submodels/electrode/ohm/__init__.py index c25ece8717..0a120ab44a 100644 --- a/pybamm/models/submodels/electrode/ohm/__init__.py +++ b/pybamm/models/submodels/electrode/ohm/__init__.py @@ -4,3 +4,6 @@ from .leading_ohm import LeadingOrder from .surface_form_ohm import SurfaceForm from .li_metal import LithiumMetalExplicit, LithiumMetalSurfaceForm + +__all__ = ['base_ohm', 'composite_ohm', 'full_ohm', 'leading_ohm', 'li_metal', + 'surface_form_ohm'] diff --git a/pybamm/models/submodels/electrode/ohm/base_ohm.py b/pybamm/models/submodels/electrode/ohm/base_ohm.py index ad38aa6b73..ab38674559 100644 --- a/pybamm/models/submodels/electrode/ohm/base_ohm.py +++ b/pybamm/models/submodels/electrode/ohm/base_ohm.py @@ -2,7 +2,7 @@ # Base class for Ohm's law submodels # import pybamm -from ..base_electrode import BaseElectrode +from pybamm.models.submodels.electrode.base_electrode import BaseElectrode class BaseModel(BaseElectrode): diff --git a/pybamm/models/submodels/electrolyte_conductivity/__init__.py b/pybamm/models/submodels/electrolyte_conductivity/__init__.py index 04925ea890..48b3232d44 100644 --- a/pybamm/models/submodels/electrolyte_conductivity/__init__.py +++ b/pybamm/models/submodels/electrolyte_conductivity/__init__.py @@ -5,3 +5,7 @@ from .integrated_conductivity import Integrated from . import surface_potential_form + +__all__ = ['base_electrolyte_conductivity', 'composite_conductivity', + 'full_conductivity', 'integrated_conductivity', + 'leading_order_conductivity', 'surface_potential_form'] diff --git a/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/__init__.py b/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/__init__.py index 2af47138c8..ec443ccf51 100644 --- a/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/__init__.py +++ b/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/__init__.py @@ -15,3 +15,8 @@ # Explicit model from .explicit_surface_form_conductivity import Explicit + +__all__ = ['composite_surface_form_conductivity', + 'explicit_surface_form_conductivity', + 'full_surface_form_conductivity', + 'leading_surface_form_conductivity'] diff --git a/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/composite_surface_form_conductivity.py b/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/composite_surface_form_conductivity.py index 4b40e28e7d..3b701ab2ac 100644 --- a/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/composite_surface_form_conductivity.py +++ b/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/composite_surface_form_conductivity.py @@ -3,7 +3,9 @@ # import pybamm -from ..composite_conductivity import Composite +from pybamm.models.submodels.electrolyte_conductivity.composite_conductivity import ( + Composite, +) class BaseModel(Composite): @@ -97,6 +99,10 @@ def __init__(self, param, domain, options=None): def set_rhs(self, variables): domain = self.domain + a = variables[ + f"X-averaged {domain} electrode surface area to volume ratio [m-1]" + ] + sum_a_j = variables[ f"Sum of x-averaged {domain} electrode volumetric " "interfacial current densities [A.m-3]" @@ -114,7 +120,7 @@ def set_rhs(self, variables): C_dl = self.domain_param.C_dl(T) - self.rhs[delta_phi] = 1 / C_dl * (sum_a_j_av - sum_a_j) + self.rhs[delta_phi] = 1 / (a * C_dl) * (sum_a_j_av - sum_a_j) class CompositeAlgebraic(BaseModel): diff --git a/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/explicit_surface_form_conductivity.py b/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/explicit_surface_form_conductivity.py index c2391b277b..a17fb47af1 100644 --- a/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/explicit_surface_form_conductivity.py +++ b/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/explicit_surface_form_conductivity.py @@ -2,7 +2,9 @@ # Class for explicit surface form potentials # import pybamm -from ..base_electrolyte_conductivity import BaseElectrolyteConductivity +from pybamm.models.submodels.electrolyte_conductivity.base_electrolyte_conductivity import ( + BaseElectrolyteConductivity, +) class Explicit(BaseElectrolyteConductivity): diff --git a/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/full_surface_form_conductivity.py b/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/full_surface_form_conductivity.py index 83bcfb8027..cceb88f83e 100644 --- a/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/full_surface_form_conductivity.py +++ b/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/full_surface_form_conductivity.py @@ -2,7 +2,9 @@ # Class for full surface form electrolyte conductivity employing stefan-maxwell # import pybamm -from ..base_electrolyte_conductivity import BaseElectrolyteConductivity +from pybamm.models.submodels.electrolyte_conductivity.base_electrolyte_conductivity import ( + BaseElectrolyteConductivity, +) class BaseModel(BaseElectrolyteConductivity): @@ -78,7 +80,7 @@ def get_coupled_variables(self, variables): else: phi_e_n = variables["Negative electrolyte potential [V]"] phi_e_n_s = pybamm.boundary_value(phi_e_n, "right") - tor_s = variables["Separator porosity"] + tor_s = variables["Separator electrolyte transport efficiency"] T = variables["Separator temperature [K]"] chiRT_over_Fc_e_s = param.chiRT_over_Fc(c_e_s, T) @@ -270,13 +272,11 @@ def set_rhs(self, variables): domain, Domain = self.domain_Domain T = variables[f"{Domain} electrode temperature [K]"] - C_dl = self.domain_param.C_dl(T) delta_phi = variables[f"{Domain} electrode surface potential difference [V]"] i_e = variables[f"{Domain} electrolyte current density [A.m-2]"] - # Variable summing all of the interfacial current densities sum_a_j = variables[ f"Sum of {domain} electrode volumetric " "interfacial current densities [A.m-3]" diff --git a/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/leading_surface_form_conductivity.py b/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/leading_surface_form_conductivity.py index 042d143f36..c7b47cdd37 100644 --- a/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/leading_surface_form_conductivity.py +++ b/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/leading_surface_form_conductivity.py @@ -3,7 +3,9 @@ # import pybamm -from ..leading_order_conductivity import LeadingOrder +from pybamm.models.submodels.electrolyte_conductivity.leading_order_conductivity import ( + LeadingOrder, +) class BaseLeadingOrderSurfaceForm(LeadingOrder): @@ -84,24 +86,26 @@ def __init__(self, param, domain, options=None): def set_rhs(self, variables): domain = self.domain + T = variables[f"X-averaged {domain} electrode temperature [K]"] + C_dl = self.domain_param.C_dl(T) + + delta_phi = variables[ + f"X-averaged {domain} electrode surface potential difference [V]" + ] + sum_a_j = variables[ f"Sum of x-averaged {domain} electrode volumetric " "interfacial current densities [A.m-3]" ] - sum_a_j_av = variables[ f"X-averaged {domain} electrode total volumetric " "interfacial current density [A.m-3]" ] - delta_phi = variables[ - f"X-averaged {domain} electrode surface potential difference [V]" + a = variables[ + f"X-averaged {domain} electrode surface area to volume ratio [m-1]" ] - T = variables[f"X-averaged {domain} electrode temperature [K]"] - - C_dl = self.domain_param.C_dl(T) - - self.rhs[delta_phi] = 1 / C_dl * (sum_a_j_av - sum_a_j) + self.rhs[delta_phi] = 1 / (a * C_dl) * (sum_a_j_av - sum_a_j) class LeadingOrderAlgebraic(BaseLeadingOrderSurfaceForm): diff --git a/pybamm/models/submodels/electrolyte_diffusion/__init__.py b/pybamm/models/submodels/electrolyte_diffusion/__init__.py index 5636b77fea..3d03f53394 100644 --- a/pybamm/models/submodels/electrolyte_diffusion/__init__.py +++ b/pybamm/models/submodels/electrolyte_diffusion/__init__.py @@ -2,3 +2,6 @@ from .leading_order_diffusion import LeadingOrder from .full_diffusion import Full from .constant_concentration import ConstantConcentration + +__all__ = ['base_electrolyte_diffusion', 'constant_concentration', + 'full_diffusion', 'leading_order_diffusion'] diff --git a/pybamm/models/submodels/electrolyte_diffusion/constant_concentration.py b/pybamm/models/submodels/electrolyte_diffusion/constant_concentration.py index ca8b11e796..eee441446f 100644 --- a/pybamm/models/submodels/electrolyte_diffusion/constant_concentration.py +++ b/pybamm/models/submodels/electrolyte_diffusion/constant_concentration.py @@ -48,9 +48,9 @@ def get_coupled_variables(self, variables): c_e_k = eps_c_e_k / eps_k c_e_dict[domain] = c_e_k - variables[ - "Electrolyte concentration concatenation [mol.m-3]" - ] = pybamm.concatenation(*c_e_dict.values()) + variables["Electrolyte concentration concatenation [mol.m-3]"] = ( + pybamm.concatenation(*c_e_dict.values()) + ) variables.update(self._get_standard_domain_concentration_variables(c_e_dict)) c_e = ( diff --git a/pybamm/models/submodels/electrolyte_diffusion/full_diffusion.py b/pybamm/models/submodels/electrolyte_diffusion/full_diffusion.py index 464d82e4e7..2fdd937966 100644 --- a/pybamm/models/submodels/electrolyte_diffusion/full_diffusion.py +++ b/pybamm/models/submodels/electrolyte_diffusion/full_diffusion.py @@ -51,9 +51,9 @@ def get_coupled_variables(self, variables): c_e_k = eps_c_e_k / eps_k c_e_dict[domain] = c_e_k - variables[ - "Electrolyte concentration concatenation [mol.m-3]" - ] = pybamm.concatenation(*c_e_dict.values()) + variables["Electrolyte concentration concatenation [mol.m-3]"] = ( + pybamm.concatenation(*c_e_dict.values()) + ) variables.update(self._get_standard_domain_concentration_variables(c_e_dict)) c_e = ( diff --git a/pybamm/models/submodels/equivalent_circuit_elements/__init__.py b/pybamm/models/submodels/equivalent_circuit_elements/__init__.py index a4e76cc5b8..4ff6ee7c62 100644 --- a/pybamm/models/submodels/equivalent_circuit_elements/__init__.py +++ b/pybamm/models/submodels/equivalent_circuit_elements/__init__.py @@ -3,3 +3,6 @@ from .rc_element import RCElement from .thermal import ThermalSubModel from .voltage_model import VoltageModel + +__all__ = ['ocv_element', 'rc_element', 'resistor_element', 'thermal', + 'voltage_model'] diff --git a/pybamm/models/submodels/external_circuit/__init__.py b/pybamm/models/submodels/external_circuit/__init__.py index bd71790295..c8831fce7b 100644 --- a/pybamm/models/submodels/external_circuit/__init__.py +++ b/pybamm/models/submodels/external_circuit/__init__.py @@ -1,4 +1,5 @@ from .base_external_circuit import BaseModel +from .discharge_throughput import DischargeThroughput from .explicit_control_external_circuit import ( ExplicitCurrentControl, ExplicitPowerControl, @@ -11,3 +12,6 @@ ResistanceFunctionControl, CCCVFunctionControl, ) + +__all__ = ['base_external_circuit', 'explicit_control_external_circuit', + 'function_control_external_circuit'] diff --git a/pybamm/models/submodels/external_circuit/base_external_circuit.py b/pybamm/models/submodels/external_circuit/base_external_circuit.py index 713616c063..75eae091d5 100644 --- a/pybamm/models/submodels/external_circuit/base_external_circuit.py +++ b/pybamm/models/submodels/external_circuit/base_external_circuit.py @@ -9,58 +9,3 @@ class BaseModel(pybamm.BaseSubModel): def __init__(self, param, options): super().__init__(param, options=options) - - def get_fundamental_variables(self): - Q_Ah = pybamm.Variable("Discharge capacity [A.h]") - Q_Ah.print_name = "Q_Ah" - # Throughput capacity (cumulative) - Qt_Ah = pybamm.Variable("Throughput capacity [A.h]") - Qt_Ah.print_name = "Qt_Ah" - - variables = { - "Discharge capacity [A.h]": Q_Ah, - "Throughput capacity [A.h]": Qt_Ah, - } - if self.options["calculate discharge energy"] == "true": - Q_Wh = pybamm.Variable("Discharge energy [W.h]") - # Throughput energy (cumulative) - Qt_Wh = pybamm.Variable("Throughput energy [W.h]") - variables.update( - { - "Discharge energy [W.h]": Q_Wh, - "Throughput energy [W.h]": Qt_Wh, - } - ) - else: - variables.update( - { - "Discharge energy [W.h]": pybamm.Scalar(0), - "Throughput energy [W.h]": pybamm.Scalar(0), - } - ) - return variables - - def set_initial_conditions(self, variables): - Q_Ah = variables["Discharge capacity [A.h]"] - Qt_Ah = variables["Throughput capacity [A.h]"] - self.initial_conditions[Q_Ah] = pybamm.Scalar(0) - self.initial_conditions[Qt_Ah] = pybamm.Scalar(0) - if self.options["calculate discharge energy"] == "true": - Q_Wh = variables["Discharge energy [W.h]"] - Qt_Wh = variables["Throughput energy [W.h]"] - self.initial_conditions[Q_Wh] = pybamm.Scalar(0) - self.initial_conditions[Qt_Wh] = pybamm.Scalar(0) - - def set_rhs(self, variables): - # ODEs for discharge capacity and throughput capacity - Q_Ah = variables["Discharge capacity [A.h]"] - Qt_Ah = variables["Throughput capacity [A.h]"] - I = variables["Current [A]"] - self.rhs[Q_Ah] = I / 3600 # Returns to zero after a complete cycle - self.rhs[Qt_Ah] = abs(I) / 3600 # Increases with each cycle - if self.options["calculate discharge energy"] == "true": - Q_Wh = variables["Discharge energy [W.h]"] - Qt_Wh = variables["Throughput energy [W.h]"] - V = variables["Voltage [V]"] - self.rhs[Q_Wh] = I * V / 3600 # Returns to zero after a complete cycle - self.rhs[Qt_Wh] = abs(I * V) / 3600 # Increases with each cycle diff --git a/pybamm/models/submodels/external_circuit/discharge_throughput.py b/pybamm/models/submodels/external_circuit/discharge_throughput.py new file mode 100644 index 0000000000..e9aecd6aed --- /dev/null +++ b/pybamm/models/submodels/external_circuit/discharge_throughput.py @@ -0,0 +1,64 @@ +# +# Variables related to discharge and throughput capacity and energy +# +import pybamm +from .base_external_circuit import BaseModel + + +class DischargeThroughput(BaseModel): + """Model calculate discharge and throughput capacity and energy.""" + + def get_fundamental_variables(self): + Q_Ah = pybamm.Variable("Discharge capacity [A.h]") + Q_Ah.print_name = "Q_Ah" + # Throughput capacity (cumulative) + Qt_Ah = pybamm.Variable("Throughput capacity [A.h]") + Qt_Ah.print_name = "Qt_Ah" + + variables = { + "Discharge capacity [A.h]": Q_Ah, + "Throughput capacity [A.h]": Qt_Ah, + } + if self.options["calculate discharge energy"] == "true": + Q_Wh = pybamm.Variable("Discharge energy [W.h]") + # Throughput energy (cumulative) + Qt_Wh = pybamm.Variable("Throughput energy [W.h]") + variables.update( + { + "Discharge energy [W.h]": Q_Wh, + "Throughput energy [W.h]": Qt_Wh, + } + ) + else: + variables.update( + { + "Discharge energy [W.h]": pybamm.Scalar(0), + "Throughput energy [W.h]": pybamm.Scalar(0), + } + ) + return variables + + def set_initial_conditions(self, variables): + Q_Ah = variables["Discharge capacity [A.h]"] + Qt_Ah = variables["Throughput capacity [A.h]"] + self.initial_conditions[Q_Ah] = pybamm.Scalar(0) + self.initial_conditions[Qt_Ah] = pybamm.Scalar(0) + if self.options["calculate discharge energy"] == "true": + Q_Wh = variables["Discharge energy [W.h]"] + Qt_Wh = variables["Throughput energy [W.h]"] + self.initial_conditions[Q_Wh] = pybamm.Scalar(0) + self.initial_conditions[Qt_Wh] = pybamm.Scalar(0) + + def set_rhs(self, variables): + # ODEs for discharge capacity and throughput capacity + Q_Ah = variables["Discharge capacity [A.h]"] + Qt_Ah = variables["Throughput capacity [A.h]"] + I = variables["Current [A]"] + self.rhs[Q_Ah] = I / 3600 # Returns to zero after a complete cycle + self.rhs[Qt_Ah] = abs(I) / 3600 # Increases with each cycle + if self.options["calculate discharge energy"] == "true": + Q_Wh = variables["Discharge energy [W.h]"] + Qt_Wh = variables["Throughput energy [W.h]"] + V = variables["Voltage [V]"] + self.rhs[Q_Wh] = I * V / 3600 # Returns to zero after a complete cycle + self.rhs[Qt_Wh] = abs(I * V) / 3600 # Increases with each cycle diff --git a/pybamm/models/submodels/external_circuit/explicit_control_external_circuit.py b/pybamm/models/submodels/external_circuit/explicit_control_external_circuit.py index e9bf18155b..760e9e2b20 100644 --- a/pybamm/models/submodels/external_circuit/explicit_control_external_circuit.py +++ b/pybamm/models/submodels/external_circuit/explicit_control_external_circuit.py @@ -8,9 +8,6 @@ class ExplicitCurrentControl(BaseModel): """External circuit with current control.""" - def __init__(self, param, options): - super().__init__(param, options) - def get_fundamental_variables(self): # Current is given as a function of time i_cell = self.param.current_density_with_time @@ -23,18 +20,12 @@ def get_fundamental_variables(self): "C-rate": I / self.param.Q, } - # Add discharge capacity variable - variables.update(super().get_fundamental_variables()) - return variables class ExplicitPowerControl(BaseModel): """External circuit with current set explicitly to hit target power.""" - def __init__(self, param, options): - super().__init__(param, options) - def get_coupled_variables(self, variables): param = self.param @@ -58,9 +49,6 @@ def get_coupled_variables(self, variables): class ExplicitResistanceControl(BaseModel): """External circuit with current set explicitly to hit target resistance.""" - def __init__(self, param, options): - super().__init__(param, options) - def get_coupled_variables(self, variables): param = self.param diff --git a/pybamm/models/submodels/external_circuit/function_control_external_circuit.py b/pybamm/models/submodels/external_circuit/function_control_external_circuit.py index 2e64e84c9e..60d6fb0e40 100644 --- a/pybamm/models/submodels/external_circuit/function_control_external_circuit.py +++ b/pybamm/models/submodels/external_circuit/function_control_external_circuit.py @@ -32,7 +32,7 @@ def get_fundamental_variables(self): param = self.param # Current is a variable i_var = pybamm.Variable("Current variable [A]", scale=param.Q) - if self.control in ["algebraic", "differential without max"]: + if self.control in ["algebraic", "differential"]: I = i_var elif self.control == "differential with max": i_input = pybamm.FunctionParameter( @@ -50,19 +50,14 @@ def get_fundamental_variables(self): "C-rate": I / param.Q, } - # Add discharge capacity variable - variables.update(super().get_fundamental_variables()) - return variables def set_initial_conditions(self, variables): - super().set_initial_conditions(variables) # Initial condition as a guess for consistent initial conditions i_cell = variables["Current variable [A]"] self.initial_conditions[i_cell] = self.param.Q def set_rhs(self, variables): - super().set_rhs(variables) # External circuit submodels are always equations on the current # The external circuit function should provide an update law for the current # based on current/voltage/power/etc. @@ -118,7 +113,7 @@ def constant_power(self, variables): class ResistanceFunctionControl(FunctionControl): """External circuit with resistance control.""" - def __init__(self, param, options, control): + def __init__(self, param, options, control="algebraic"): super().__init__(param, self.constant_resistance, options, control=control) def constant_resistance(self, variables): diff --git a/pybamm/models/submodels/interface/__init__.py b/pybamm/models/submodels/interface/__init__.py index 1a3ffd6876..7779c8365e 100644 --- a/pybamm/models/submodels/interface/__init__.py +++ b/pybamm/models/submodels/interface/__init__.py @@ -1,2 +1,6 @@ from .base_interface import BaseInterface from .total_interfacial_current import TotalInterfacialCurrent + +__all__ = ['base_interface', 'interface_utilisation', 'kinetics', + 'lithium_plating', 'open_circuit_potential', 'sei', + 'total_interfacial_current'] diff --git a/pybamm/models/submodels/interface/base_interface.py b/pybamm/models/submodels/interface/base_interface.py index b7e160ee2f..ab9b80eae0 100644 --- a/pybamm/models/submodels/interface/base_interface.py +++ b/pybamm/models/submodels/interface/base_interface.py @@ -38,6 +38,7 @@ def __init__(self, param, domain, reaction, options, phase="primary"): if reaction in [ "lithium-ion main", "lithium metal plating", + "lithium plating", "SEI", "SEI on cracks", ]: diff --git a/pybamm/models/submodels/interface/interface_utilisation/__init__.py b/pybamm/models/submodels/interface/interface_utilisation/__init__.py index f13ee19d6b..1ffe9da5f5 100644 --- a/pybamm/models/submodels/interface/interface_utilisation/__init__.py +++ b/pybamm/models/submodels/interface/interface_utilisation/__init__.py @@ -2,3 +2,6 @@ from .full_utilisation import Full from .constant_utilisation import Constant from .current_driven_utilisation import CurrentDriven + +__all__ = ['base_utilisation', 'constant_utilisation', + 'current_driven_utilisation', 'full_utilisation'] diff --git a/pybamm/models/submodels/interface/kinetics/__init__.py b/pybamm/models/submodels/interface/kinetics/__init__.py index d99ec56783..c083271263 100644 --- a/pybamm/models/submodels/interface/kinetics/__init__.py +++ b/pybamm/models/submodels/interface/kinetics/__init__.py @@ -12,3 +12,7 @@ CurrentForInverseButlerVolmer, CurrentForInverseButlerVolmerLithiumMetal, ) + +__all__ = ['base_kinetics', 'butler_volmer', 'diffusion_limited', + 'inverse_kinetics', 'linear', 'marcus', 'msmr_butler_volmer', + 'no_reaction', 'tafel', 'total_main_kinetics'] diff --git a/pybamm/models/submodels/interface/kinetics/base_kinetics.py b/pybamm/models/submodels/interface/kinetics/base_kinetics.py index dd5ee76340..7cfa83631b 100644 --- a/pybamm/models/submodels/interface/kinetics/base_kinetics.py +++ b/pybamm/models/submodels/interface/kinetics/base_kinetics.py @@ -2,7 +2,7 @@ # Base kinetics class # import pybamm -from ..base_interface import BaseInterface +from pybamm.models.submodels.interface.base_interface import BaseInterface class BaseKinetics(BaseInterface): diff --git a/pybamm/models/submodels/interface/kinetics/diffusion_limited.py b/pybamm/models/submodels/interface/kinetics/diffusion_limited.py index b761778155..08c2db2175 100644 --- a/pybamm/models/submodels/interface/kinetics/diffusion_limited.py +++ b/pybamm/models/submodels/interface/kinetics/diffusion_limited.py @@ -3,7 +3,7 @@ # import pybamm -from ..base_interface import BaseInterface +from pybamm.models.submodels.interface.base_interface import BaseInterface class DiffusionLimited(BaseInterface): diff --git a/pybamm/models/submodels/interface/kinetics/inverse_kinetics/__init__.py b/pybamm/models/submodels/interface/kinetics/inverse_kinetics/__init__.py index e69de29bb2..dce1ad8271 100644 --- a/pybamm/models/submodels/interface/kinetics/inverse_kinetics/__init__.py +++ b/pybamm/models/submodels/interface/kinetics/inverse_kinetics/__init__.py @@ -0,0 +1 @@ +__all__ = ['inverse_butler_volmer'] diff --git a/pybamm/models/submodels/interface/kinetics/inverse_kinetics/inverse_butler_volmer.py b/pybamm/models/submodels/interface/kinetics/inverse_kinetics/inverse_butler_volmer.py index 88e1793263..959cb027c1 100644 --- a/pybamm/models/submodels/interface/kinetics/inverse_kinetics/inverse_butler_volmer.py +++ b/pybamm/models/submodels/interface/kinetics/inverse_kinetics/inverse_butler_volmer.py @@ -2,7 +2,7 @@ # Inverse Bulter-Volmer class # import pybamm -from ...base_interface import BaseInterface +from pybamm.models.submodels.interface.base_interface import BaseInterface class InverseButlerVolmer(BaseInterface): diff --git a/pybamm/models/submodels/interface/kinetics/msmr_butler_volmer.py b/pybamm/models/submodels/interface/kinetics/msmr_butler_volmer.py index 6a4b9f5023..dafda9ea89 100644 --- a/pybamm/models/submodels/interface/kinetics/msmr_butler_volmer.py +++ b/pybamm/models/submodels/interface/kinetics/msmr_butler_volmer.py @@ -117,7 +117,7 @@ def _get_standard_exchange_current_by_reaction_variables(self, j0, index): return variables def _get_kinetics_by_reaction(self, j0, ne, eta_r, T, u, index): - alpha = self.phase_param.alpha_bv_j(index) + alpha = self.phase_param.alpha_bv_j(T, index) Feta_RT = self.param.F * eta_r / (self.param.R * T) return ( u diff --git a/pybamm/models/submodels/interface/kinetics/no_reaction.py b/pybamm/models/submodels/interface/kinetics/no_reaction.py index 27d8d201ca..807be61586 100644 --- a/pybamm/models/submodels/interface/kinetics/no_reaction.py +++ b/pybamm/models/submodels/interface/kinetics/no_reaction.py @@ -3,7 +3,7 @@ # import pybamm -from ..base_interface import BaseInterface +from pybamm.models.submodels.interface.base_interface import BaseInterface class NoReaction(BaseInterface): diff --git a/pybamm/models/submodels/interface/lithium_plating/__init__.py b/pybamm/models/submodels/interface/lithium_plating/__init__.py index cea178158d..c9a5956faa 100644 --- a/pybamm/models/submodels/interface/lithium_plating/__init__.py +++ b/pybamm/models/submodels/interface/lithium_plating/__init__.py @@ -1,3 +1,6 @@ from .base_plating import BasePlating from .no_plating import NoPlating from .plating import Plating +from .total_lithium_plating import TotalLithiumPlating + +__all__ = ['base_plating', 'no_plating', 'plating', 'TotalLithiumPlating'] diff --git a/pybamm/models/submodels/interface/lithium_plating/base_plating.py b/pybamm/models/submodels/interface/lithium_plating/base_plating.py index ebfbe46831..0530476f9d 100644 --- a/pybamm/models/submodels/interface/lithium_plating/base_plating.py +++ b/pybamm/models/submodels/interface/lithium_plating/base_plating.py @@ -2,7 +2,7 @@ # Base class for lithium plating models. # import pybamm -from ..base_interface import BaseInterface +from pybamm.models.submodels.interface.base_interface import BaseInterface class BasePlating(BaseInterface): @@ -17,44 +17,30 @@ class BasePlating(BaseInterface): A dictionary of options to be passed to the model. """ - def __init__(self, param, domain, options=None): + def __init__(self, param, domain, options=None, phase="primary"): reaction = "lithium plating" - super().__init__(param, domain, reaction, options=options) + super().__init__(param, domain, reaction, options=options, phase=phase) def get_coupled_variables(self, variables): # Update some common variables domain, Domain = self.domain_Domain - if self.options.electrode_types[domain] == "porous": - j_plating = variables[ - f"{Domain} lithium plating interfacial current density [A.m-2]" - ] - j_plating_av = variables[ - f"X-averaged {domain} lithium plating " - "interfacial current density [A.m-2]" - ] - particle_phases_option = getattr(self.options, domain)["particle phases"] - if particle_phases_option == "1": - a = variables[f"{Domain} electrode surface area to volume ratio [m-1]"] - else: - a = variables[ - f"{Domain} electrode primary surface area to volume ratio [m-1]" - ] - a_j_plating = a * j_plating - a_j_plating_av = pybamm.x_average(a_j_plating) - - variables.update( - { - f"{Domain} electrode lithium plating interfacial current " - "density [A.m-2]": j_plating, - f"X-averaged {domain} electrode lithium plating " - "interfacial current density [A.m-2]": j_plating_av, - f"{Domain} lithium plating volumetric " - "interfacial current density [A.m-3]": a_j_plating, - f"X-averaged {domain} lithium plating volumetric " - "interfacial current density [A.m-3]": a_j_plating_av, - } - ) + j_plating_av = variables[ + f"X-averaged {domain} electrode {self.phase_name}lithium plating " + "interfacial current density [A.m-2]" + ] + j_plating = variables[ + f"{Domain} electrode {self.phase_name}lithium plating " + "interfacial current density [A.m-2]" + ] + variables.update( + { + f"X-averaged {domain} electrode {self.phase_name}lithium plating " + "interfacial current density [A.m-2]": j_plating_av, + f"{Domain} electrode {self.phase_name}lithium plating " + "interfacial current density [A.m-2]": j_plating, + } + ) variables.update( self._get_standard_volumetric_current_density_variables(variables) @@ -75,7 +61,8 @@ def _get_standard_concentration_variables(self, c_plated_Li, c_dead_Li): variables : dict The variables which can be derived from the plated lithium thickness. """ - param = self.param + phase_name = self.phase_name + phase_param = self.phase_param domain, Domain = self.domain_Domain # Set scales to one for the "no plating" model so that they are not required @@ -84,40 +71,43 @@ def _get_standard_concentration_variables(self, c_plated_Li, c_dead_Li): c_to_L = 1 L_k = 1 elif domain == "negative": - c_to_L = param.V_bar_Li / param.n.prim.a_typ - L_k = param.n.L + c_to_L = self.param.V_bar_Li / phase_param.a_typ + L_k = self.param.n.L elif domain == "positive": - c_to_L = param.V_bar_Li / param.p.prim.a_typ - L_k = param.p.L + c_to_L = self.param.V_bar_Li / phase_param.a_typ + L_k = self.param.p.L c_plated_Li_av = pybamm.x_average(c_plated_Li) L_plated_Li = c_plated_Li * c_to_L # plated Li thickness L_plated_Li_av = pybamm.x_average(L_plated_Li) - Q_plated_Li = c_plated_Li_av * L_k * param.L_y * param.L_z + Q_plated_Li = c_plated_Li_av * L_k * self.param.L_y * self.param.L_z c_dead_Li_av = pybamm.x_average(c_dead_Li) # dead Li "thickness", required by porosity submodel L_dead_Li = c_dead_Li * c_to_L L_dead_Li_av = pybamm.x_average(L_dead_Li) - Q_dead_Li = c_dead_Li_av * L_k * param.L_y * param.L_z + Q_dead_Li = c_dead_Li_av * L_k * self.param.L_y * self.param.L_z variables = { - f"{Domain} lithium plating concentration [mol.m-3]": c_plated_Li, - f"X-averaged {domain} lithium plating " - "concentration [mol.m-3]": c_plated_Li_av, - f"{Domain} dead lithium concentration [mol.m-3]": c_dead_Li, - f"X-averaged {domain} dead lithium concentration [mol.m-3]": c_dead_Li_av, - f"{Domain} lithium plating thickness [m]": L_plated_Li, - f"X-averaged {domain} lithium plating thickness [m]": L_plated_Li_av, - f"{Domain} dead lithium thickness [m]": L_dead_Li, - f"X-averaged {domain} dead lithium thickness [m]": L_dead_Li_av, - f"Loss of lithium to {domain} lithium plating " "[mol]": ( + f"{Domain} {phase_name}lithium plating concentration " + "[mol.m-3]": c_plated_Li, + f"X-averaged {domain} {phase_name}lithium plating concentration " + "[mol.m-3]": c_plated_Li_av, + f"{Domain} {phase_name}dead lithium concentration [mol.m-3]": c_dead_Li, + f"X-averaged {domain} {phase_name}dead lithium concentration " + "[mol.m-3]": c_dead_Li_av, + f"{Domain} {phase_name}lithium plating thickness [m]": L_plated_Li, + f"X-averaged {domain} {phase_name} lithium plating thickness " + "[m]": L_plated_Li_av, + f"{Domain} {phase_name}dead lithium thickness [m]": L_dead_Li, + f"X-averaged {domain} {phase_name}dead lithium thickness [m]": L_dead_Li_av, + f"Loss of lithium to {domain} {phase_name}lithium plating " "[mol]": ( Q_plated_Li + Q_dead_Li ), - f"Loss of capacity to {domain} lithium plating " "[A.h]": ( + f"Loss of capacity to {domain} {phase_name}lithium plating " "[A.h]": ( Q_plated_Li + Q_dead_Li ) - * param.F + * self.param.F / 3600, } @@ -140,9 +130,9 @@ def _get_standard_reaction_variables(self, j_stripping): j_stripping_av = pybamm.x_average(j_stripping) variables = { - f"{Domain} lithium plating interfacial current density " - "[A.m-2]": j_stripping, - f"X-averaged {domain} lithium plating " + f"{Domain} electrode {self.phase_name}lithium plating " + "interfacial current density [A.m-2]": j_stripping, + f"X-averaged {domain} electrode {self.phase_name}lithium plating " "interfacial current density [A.m-2]": j_stripping_av, } diff --git a/pybamm/models/submodels/interface/lithium_plating/no_plating.py b/pybamm/models/submodels/interface/lithium_plating/no_plating.py index 94697fdd89..ddc13cfb97 100644 --- a/pybamm/models/submodels/interface/lithium_plating/no_plating.py +++ b/pybamm/models/submodels/interface/lithium_plating/no_plating.py @@ -16,8 +16,8 @@ class NoPlating(BasePlating): A dictionary of options to be passed to the model. """ - def __init__(self, param, domain, options=None): - super().__init__(param, domain, options=options) + def __init__(self, param, domain, options=None, phase="primary"): + super().__init__(param, domain, options=options, phase=phase) def get_fundamental_variables(self): zero = pybamm.FullBroadcast( diff --git a/pybamm/models/submodels/interface/lithium_plating/plating.py b/pybamm/models/submodels/interface/lithium_plating/plating.py index 9f4de08d2f..f019c3b9d8 100644 --- a/pybamm/models/submodels/interface/lithium_plating/plating.py +++ b/pybamm/models/submodels/interface/lithium_plating/plating.py @@ -19,37 +19,42 @@ class Plating(BasePlating): A dictionary of options to be passed to the model. """ - def __init__(self, param, domain, x_average, options): - super().__init__(param, domain, options=options) + def __init__(self, param, domain, x_average, options, phase="primary"): + super().__init__(param, domain, options=options, phase=phase) self.x_average = x_average pybamm.citations.register("OKane2020") pybamm.citations.register("OKane2022") def get_fundamental_variables(self): domain, Domain = self.domain_Domain + scale = self.phase_param.c_Li_typ if self.x_average is True: c_plated_Li_av = pybamm.Variable( - f"X-averaged {domain} lithium plating concentration [mol.m-3]", + f"X-averaged {domain} {self.phase_name}lithium plating concentration " + "[mol.m-3]", domain="current collector", - scale=self.param.c_Li_typ, + scale=scale, ) c_plated_Li = pybamm.PrimaryBroadcast(c_plated_Li_av, f"{domain} electrode") c_dead_Li_av = pybamm.Variable( - f"X-averaged {domain} dead lithium concentration [mol.m-3]", + f"X-averaged {domain} {self.phase_name}dead lithium concentration " + "[mol.m-3]", domain="current collector", + scale=scale, ) c_dead_Li = pybamm.PrimaryBroadcast(c_dead_Li_av, f"{domain} electrode") else: c_plated_Li = pybamm.Variable( - f"{Domain} lithium plating concentration [mol.m-3]", + f"{Domain} {self.phase_name}lithium plating concentration [mol.m-3]", domain=f"{domain} electrode", auxiliary_domains={"secondary": "current collector"}, - scale=self.param.c_Li_typ, + scale=scale, ) c_dead_Li = pybamm.Variable( - f"{Domain} dead lithium concentration [mol.m-3]", + f"{Domain} {self.phase_name}dead lithium concentration [mol.m-3]", domain=f"{domain} electrode", auxiliary_domains={"secondary": "current collector"}, + scale=scale, ) variables = self._get_standard_concentration_variables(c_plated_Li, c_dead_Li) @@ -57,24 +62,30 @@ def get_fundamental_variables(self): return variables def get_coupled_variables(self, variables): - param = self.param + phase_param = self.phase_param domain, Domain = self.domain_Domain delta_phi = variables[f"{Domain} electrode surface potential difference [V]"] c_e_n = variables[f"{Domain} electrolyte concentration [mol.m-3]"] T = variables[f"{Domain} electrode temperature [K]"] - eta_sei = variables[f"{Domain} electrode SEI film overpotential [V]"] - c_plated_Li = variables[f"{Domain} lithium plating concentration [mol.m-3]"] - j0_stripping = param.j0_stripping(c_e_n, c_plated_Li, T) - j0_plating = param.j0_plating(c_e_n, c_plated_Li, T) + eta_sei = variables[ + f"{Domain} electrode {self.phase_name}SEI film overpotential [V]" + ] + c_plated_Li = variables[ + f"{Domain} {self.phase_name}lithium plating concentration [mol.m-3]" + ] + j0_stripping = phase_param.j0_stripping(c_e_n, c_plated_Li, T) + j0_plating = phase_param.j0_plating(c_e_n, c_plated_Li, T) eta_stripping = delta_phi + eta_sei eta_plating = -eta_stripping - F_RT = param.F / (param.R * T) + F_RT = self.param.F / (self.param.R * T) # NEW: transfer coefficients can be set by the user - alpha_stripping = self.param.alpha_stripping - alpha_plating = self.param.alpha_plating + alpha_stripping = phase_param.alpha_stripping + alpha_plating = phase_param.alpha_plating - lithium_plating_option = getattr(self.options, domain)["lithium plating"] + lithium_plating_option = getattr(getattr(self.options, domain), self.phase)[ + "lithium plating" + ] if lithium_plating_option in ["reversible", "partially reversible"]: j_stripping = j0_stripping * pybamm.exp( F_RT * alpha_stripping * eta_stripping @@ -87,35 +98,45 @@ def get_coupled_variables(self, variables): variables.update(self._get_standard_overpotential_variables(eta_stripping)) variables.update(self._get_standard_reaction_variables(j_stripping)) - # Update whole cell variables, which also updates the "sum of" variables + # Add other standard coupled variables variables.update(super().get_coupled_variables(variables)) return variables def set_rhs(self, variables): domain, Domain = self.domain_Domain + phase_name = self.phase_name if self.x_average is True: c_plated_Li = variables[ - f"X-averaged {domain} lithium plating concentration [mol.m-3]" + f"X-averaged {domain} {phase_name}lithium plating concentration " + "[mol.m-3]" ] c_dead_Li = variables[ - f"X-averaged {domain} dead lithium concentration [mol.m-3]" + f"X-averaged {domain} {phase_name}dead lithium concentration [mol.m-3]" ] a_j_stripping = variables[ - f"X-averaged {domain} lithium plating volumetric " + f"X-averaged {domain} electrode {phase_name}lithium plating volumetric " "interfacial current density [A.m-3]" ] - L_sei = variables[f"X-averaged {domain} total SEI thickness [m]"] + L_sei = variables[ + f"X-averaged {domain} total {phase_name}SEI thickness [m]" + ] else: - c_plated_Li = variables[f"{Domain} lithium plating concentration [mol.m-3]"] - c_dead_Li = variables[f"{Domain} dead lithium concentration [mol.m-3]"] + c_plated_Li = variables[ + f"{Domain} {phase_name}lithium plating concentration [mol.m-3]" + ] + c_dead_Li = variables[ + f"{Domain} {phase_name}dead lithium concentration [mol.m-3]" + ] a_j_stripping = variables[ - f"{Domain} lithium plating volumetric " + f"{Domain} electrode {phase_name}lithium plating volumetric " "interfacial current density [A.m-3]" ] - L_sei = variables[f"{Domain} total SEI thickness [m]"] + L_sei = variables[f"{Domain} total {phase_name}SEI thickness [m]"] - lithium_plating_option = getattr(self.options, domain)["lithium plating"] + lithium_plating_option = getattr(getattr(self.options, domain), self.phase)[ + "lithium plating" + ] if lithium_plating_option == "reversible": # In the reversible plating model, there is no dead lithium dc_plated_Li = -a_j_stripping / self.param.F @@ -127,7 +148,7 @@ def set_rhs(self, variables): elif lithium_plating_option == "partially reversible": # In the partially reversible plating model, the coupling term turns # reversible lithium into dead lithium over time. - dead_lithium_decay_rate = self.param.dead_lithium_decay_rate(L_sei) + dead_lithium_decay_rate = self.phase_param.dead_lithium_decay_rate(L_sei) coupling_term = dead_lithium_decay_rate * c_plated_Li dc_plated_Li = -a_j_stripping / self.param.F - coupling_term dc_dead_Li = coupling_term @@ -139,17 +160,23 @@ def set_rhs(self, variables): def set_initial_conditions(self, variables): domain, Domain = self.domain_Domain + phase_name = self.phase_name if self.x_average is True: c_plated_Li = variables[ - f"X-averaged {domain} lithium plating concentration [mol.m-3]" + f"X-averaged {domain} {phase_name}lithium plating concentration " + "[mol.m-3]" ] c_dead_Li = variables[ - f"X-averaged {domain} dead lithium concentration [mol.m-3]" + f"X-averaged {domain} {phase_name}dead lithium concentration [mol.m-3]" ] else: - c_plated_Li = variables[f"{Domain} lithium plating concentration [mol.m-3]"] - c_dead_Li = variables[f"{Domain} dead lithium concentration [mol.m-3]"] - c_plated_Li_0 = self.param.c_plated_Li_0 - zero = pybamm.Scalar(0) + c_plated_Li = variables[ + f"{Domain} {phase_name}lithium plating concentration [mol.m-3]" + ] + c_dead_Li = variables[ + f"{Domain} {phase_name}dead lithium concentration [mol.m-3]" + ] + c_plated_Li_0 = self.phase_param.c_plated_Li_0 + zero = 0 * c_plated_Li_0 self.initial_conditions = {c_plated_Li: c_plated_Li_0, c_dead_Li: zero} diff --git a/pybamm/models/submodels/interface/lithium_plating/total_lithium_plating.py b/pybamm/models/submodels/interface/lithium_plating/total_lithium_plating.py new file mode 100644 index 0000000000..105a9a938b --- /dev/null +++ b/pybamm/models/submodels/interface/lithium_plating/total_lithium_plating.py @@ -0,0 +1,43 @@ +# +# Class summing up contributions to the lithium plating reaction +# for cases with primary, secondary, ... reactions e.g. silicon-graphite +# +import pybamm + + +class TotalLithiumPlating(pybamm.BaseSubModel): + """ + Class summing up contributions to the lithium plating reaction + for cases with primary, secondary, ... reactions e.g. silicon-graphite + + Parameters + ---------- + param : + model parameters + options: dict + A dictionary of options to be passed to the model. + See :class:`pybamm.BaseBatteryModel` + """ + + def __init__(self, param, domain, options): + super().__init__(param, domain, options=options) + + def get_coupled_variables(self, variables): + domain, Domain = self.domain_Domain + phases = self.options.phases[domain] + # For each of the variables, the variable name without the phase name + # is constructed by summing all of the variable names with the phases + for variable_template in [ + f"{Domain} electrode {{}}lithium plating volumetric " + "interfacial current density [A.m-3]", + f"X-averaged {domain} electrode {{}}lithium plating volumetric " + "interfacial current density [A.m-3]", + f"Loss of lithium to {domain} {{}}lithium plating [mol]", + f"Loss of capacity to {domain} {{}}lithium plating [A.h]", + ]: + sumvar = sum( + variables[variable_template.format(phase + " ")] for phase in phases + ) + variables[variable_template.format("")] = sumvar + + return variables diff --git a/pybamm/models/submodels/interface/open_circuit_potential/__init__.py b/pybamm/models/submodels/interface/open_circuit_potential/__init__.py index 5f8a409bba..7313274041 100644 --- a/pybamm/models/submodels/interface/open_circuit_potential/__init__.py +++ b/pybamm/models/submodels/interface/open_circuit_potential/__init__.py @@ -2,3 +2,6 @@ from .single_ocp import SingleOpenCircuitPotential from .current_sigmoid_ocp import CurrentSigmoidOpenCircuitPotential from .msmr_ocp import MSMROpenCircuitPotential +from .wycisk_ocp import WyciskOpenCircuitPotential + +__all__ = ['base_ocp', 'current_sigmoid_ocp', 'msmr_ocp', 'single_ocp', 'wycisk_ocp'] diff --git a/pybamm/models/submodels/interface/open_circuit_potential/base_ocp.py b/pybamm/models/submodels/interface/open_circuit_potential/base_ocp.py index 35f3894dfe..309eb343c0 100644 --- a/pybamm/models/submodels/interface/open_circuit_potential/base_ocp.py +++ b/pybamm/models/submodels/interface/open_circuit_potential/base_ocp.py @@ -2,7 +2,7 @@ # Base class for open-circuit potential # import pybamm -from ..base_interface import BaseInterface +from pybamm.models.submodels.interface.base_interface import BaseInterface class BaseOpenCircuitPotential(BaseInterface): diff --git a/pybamm/models/submodels/interface/open_circuit_potential/wycisk_ocp.py b/pybamm/models/submodels/interface/open_circuit_potential/wycisk_ocp.py new file mode 100644 index 0000000000..2a79c78a48 --- /dev/null +++ b/pybamm/models/submodels/interface/open_circuit_potential/wycisk_ocp.py @@ -0,0 +1,176 @@ +# +# from Wycisk 2022 +# +import pybamm +from . import BaseOpenCircuitPotential + + +class WyciskOpenCircuitPotential(BaseOpenCircuitPotential): + """ + Class for open-circuit potential with hysteresis based on the approach outlined by Wycisk :footcite:t:'Wycisk2022'. + This approach employs a differential capacity hysteresis state variable. The decay and switching of the hysteresis state + is tunable via two additional parameters. The hysteresis state is updated based on the current and the differential capacity. + """ + + def get_fundamental_variables(self): + domain, Domain = self.domain_Domain + phase_name = self.phase_name + h = pybamm.Variable( + f"{Domain} electrode {phase_name}hysteresis state", + domains={ + "primary": f"{domain} electrode", + "secondary": "current collector", + }, + ) + return { + f"{Domain} electrode {phase_name}hysteresis state": h, + } + + def get_coupled_variables(self, variables): + domain, Domain = self.domain_Domain + phase_name = self.phase_name + phase = self.phase + + if self.reaction == "lithium-ion main": + T = variables[f"{Domain} electrode temperature [K]"] + h = variables[f"{Domain} electrode {phase_name}hysteresis state"] + + # For "particle-size distribution" models, take distribution version + # of c_s_surf that depends on particle size. + domain_options = getattr(self.options, domain) + if domain_options["particle size"] == "distribution": + sto_surf = variables[ + f"{Domain} {phase_name}particle surface stoichiometry distribution" + ] + # If variable was broadcast, take only the orphan + if isinstance(sto_surf, pybamm.Broadcast) and isinstance( + T, pybamm.Broadcast + ): + sto_surf = sto_surf.orphans[0] + T = T.orphans[0] + T = pybamm.PrimaryBroadcast(T, [f"{domain} {phase_name}particle size"]) + h = pybamm.PrimaryBroadcast(h, [f"{domain} {phase_name}particle size"]) + else: + sto_surf = variables[ + f"{Domain} {phase_name}particle surface stoichiometry" + ] + # If variable was broadcast, take only the orphan + if isinstance(sto_surf, pybamm.Broadcast) and isinstance( + T, pybamm.Broadcast + ): + sto_surf = sto_surf.orphans[0] + T = T.orphans[0] + + variables[ + f"{Domain} electrode {phase_name}hysteresis state distribution" + ] = h + + # Bulk OCP is from the average SOC and temperature + sto_bulk = variables[f"{Domain} electrode {phase_name}stoichiometry"] + c_scale = self.phase_param.c_max + variables[f"Total lithium in {phase} phase in {domain} electrode [mol]"] = ( + sto_bulk * c_scale + ) # c_s_vol * L * A + + ocp_surf_eq = self.phase_param.U(sto_surf, T) + variables[f"{Domain} electrode {phase_name}equilibrium OCP [V]"] = ( + ocp_surf_eq + ) + + T_bulk = pybamm.xyz_average(pybamm.size_average(T)) + ocp_bulk_eq = self.phase_param.U(sto_bulk, T_bulk) + variables[f"{Domain} electrode {phase_name}bulk equilibrium OCP [V]"] = ( + ocp_bulk_eq + ) + + inputs = {f"{Domain} {phase_name}particle stoichiometry": sto_surf} + lith_ref = pybamm.FunctionParameter( + f"{self.phase_param.phase_prefactor}{Domain} electrode lithiation OCP [V]", + inputs, + ) + delith_ref = pybamm.FunctionParameter( + f"{self.phase_param.phase_prefactor}{Domain} electrode delithiation OCP [V]", + inputs, + ) + H = abs(delith_ref - lith_ref) / 2 + variables[f"{Domain} electrode {phase_name}OCP hysteresis [V]"] = H + + # determine dQ/dU + if phase_name == "": + Q_mag = variables[f"{Domain} electrode capacity [A.h]"] + else: + Q_mag = variables[ + f"{Domain} electrode {phase_name}phase capacity [A.h]" + ] + + dU = self.phase_param.U(sto_surf, T_bulk).diff(sto_surf) + dQdU = Q_mag / dU + variables[ + f"{Domain} electrode {phase_name}differential capacity [A.s.V-1]" + ] = dQdU + + H_x_av = pybamm.x_average(H) + h_x_av = pybamm.x_average(h) + variables[f"X-averaged {domain} electrode {phase_name}hysteresis state"] = ( + h_x_av + ) + + # check if psd + if domain_options["particle size"] == "distribution": + # should always be true + if f"{domain} particle size" in sto_surf.domains["primary"]: + # check if MPM Model + if "current collector" in sto_surf.domains["secondary"]: + ocp_surf = ocp_surf_eq + H_x_av * h_x_av + # must be DFN with PSD model + elif ( + f"{domain} electrode" in sto_surf.domains["secondary"] + or f"{domain} {phase_name}particle size" + in sto_surf.domains["primary"] + ): + ocp_surf = ocp_surf_eq + H * h + # must not be a psd + else: + ocp_surf = ocp_surf_eq + H * h + + H_s_av = pybamm.size_average(H_x_av) + h_s_av = pybamm.size_average(h_x_av) + + ocp_bulk = ocp_bulk_eq + H_s_av * h_s_av + + dUdT = self.phase_param.dUdT(sto_surf) + + variables.update(self._get_standard_ocp_variables(ocp_surf, ocp_bulk, dUdT)) + return variables + + def set_rhs(self, variables): + domain, Domain = self.domain_Domain + phase_name = self.phase_name + + current = variables[ + f"{Domain} electrode {phase_name}interfacial current density [A.m-2]" + ] + # check if composite or not + if phase_name != "": + Q_cell = variables[f"{Domain} electrode {phase_name}phase capacity [A.h]"] + else: + Q_cell = variables[f"{Domain} electrode capacity [A.h]"] + + dQdU = variables[ + f"{Domain} electrode {phase_name}differential capacity [A.s.V-1]" + ] + dQdU = dQdU.orphans[0] + K = self.phase_param.hysteresis_decay + K_x = self.phase_param.hysteresis_switch + h = variables[f"{Domain} electrode {phase_name}hysteresis state"] + + dhdt = ( + K * (current / (Q_cell * (dQdU**K_x))) * (1 - pybamm.sign(current) * h) + ) #! current is backwards for a halfcell + self.rhs[h] = dhdt + + def set_initial_conditions(self, variables): + domain, Domain = self.domain_Domain + phase_name = self.phase_name + h = variables[f"{Domain} electrode {phase_name}hysteresis state"] + self.initial_conditions[h] = pybamm.Scalar(0) diff --git a/pybamm/models/submodels/interface/sei/__init__.py b/pybamm/models/submodels/interface/sei/__init__.py index 5f151daf0e..e7cd3d4bb1 100644 --- a/pybamm/models/submodels/interface/sei/__init__.py +++ b/pybamm/models/submodels/interface/sei/__init__.py @@ -3,3 +3,5 @@ from .no_sei import NoSEI from .constant_sei import ConstantSEI from .sei_growth import SEIGrowth + +__all__ = ['base_sei', 'constant_sei', 'no_sei', 'sei_growth', 'total_sei'] diff --git a/pybamm/models/submodels/interface/sei/base_sei.py b/pybamm/models/submodels/interface/sei/base_sei.py index b0e8db56c6..6caeac887d 100644 --- a/pybamm/models/submodels/interface/sei/base_sei.py +++ b/pybamm/models/submodels/interface/sei/base_sei.py @@ -2,7 +2,7 @@ # Base class for SEI models. # import pybamm -from ..base_interface import BaseInterface +from pybamm.models.submodels.interface.base_interface import BaseInterface class BaseModel(BaseInterface): diff --git a/pybamm/models/submodels/interface/sei/sei_growth.py b/pybamm/models/submodels/interface/sei/sei_growth.py index 7f6e2771cc..bed4b04952 100644 --- a/pybamm/models/submodels/interface/sei/sei_growth.py +++ b/pybamm/models/submodels/interface/sei/sei_growth.py @@ -71,7 +71,7 @@ def get_fundamental_variables(self): L_inner, L_outer = Ls - SEI_option = getattr(self.options, domain)["SEI"] + SEI_option = getattr(getattr(self.options, domain), self.phase)["SEI"] if SEI_option.startswith("ec reaction limited"): L_inner = 0 * L_inner # Set L_inner to zero, copying domains @@ -83,7 +83,7 @@ def get_coupled_variables(self, variables): param = self.param phase_param = self.phase_param domain, Domain = self.domain_Domain - SEI_option = getattr(self.options, domain)["SEI"] + SEI_option = getattr(getattr(self.options, domain), self.phase)["SEI"] T = variables[f"{Domain} electrode temperature [K]"] # delta_phi = phi_s - phi_e if self.reaction_loc == "interface": @@ -134,8 +134,7 @@ def get_coupled_variables(self, variables): elif SEI_option == "electron-migration limited": # Scott Marquis thesis (eq. 5.94) eta_inner = delta_phi - phase_param.U_inner - j_sei = phase_param.kappa_inner * eta_inner / L_sei_inner - + j_sei = (eta_inner < 0) * phase_param.kappa_inner * eta_inner / L_sei_inner elif SEI_option == "interstitial-diffusion limited": # Scott Marquis thesis (eq. 5.96) j_sei = -( @@ -257,7 +256,7 @@ def set_rhs(self, variables): ) # we have to add the spreading rate to account for cracking - SEI_option = getattr(self.options, domain)["SEI"] + SEI_option = getattr(getattr(self.options, domain), self.phase)["SEI"] if SEI_option.startswith("ec reaction limited"): self.rhs = {L_outer: -dLdt_SEI_outer + spreading_outer} else: @@ -285,7 +284,7 @@ def set_initial_conditions(self, variables): else: L_inner_0 = self.phase_param.L_inner_0 L_outer_0 = self.phase_param.L_outer_0 - SEI_option = getattr(self.options, domain)["SEI"] + SEI_option = getattr(getattr(self.options, domain), self.phase)["SEI"] if SEI_option.startswith("ec reaction limited"): self.initial_conditions = {L_outer: L_inner_0 + L_outer_0} else: diff --git a/pybamm/models/submodels/oxygen_diffusion/__init__.py b/pybamm/models/submodels/oxygen_diffusion/__init__.py index a5161f93f9..864bf2ef4d 100644 --- a/pybamm/models/submodels/oxygen_diffusion/__init__.py +++ b/pybamm/models/submodels/oxygen_diffusion/__init__.py @@ -2,3 +2,6 @@ from .leading_oxygen_diffusion import LeadingOrder from .full_oxygen_diffusion import Full from .no_oxygen import NoOxygen + +__all__ = ['base_oxygen_diffusion', 'full_oxygen_diffusion', + 'leading_oxygen_diffusion', 'no_oxygen'] diff --git a/pybamm/models/submodels/particle/__init__.py b/pybamm/models/submodels/particle/__init__.py index 237b2c19c8..4645e97dac 100644 --- a/pybamm/models/submodels/particle/__init__.py +++ b/pybamm/models/submodels/particle/__init__.py @@ -3,4 +3,13 @@ from .polynomial_profile import PolynomialProfile from .x_averaged_polynomial_profile import XAveragedPolynomialProfile from .total_particle_concentration import TotalConcentration -from .msmr_diffusion import MSMRDiffusion +from .msmr_diffusion import MSMRDiffusion, MSMRStoichiometryVariables + +__all__ = [ + "base_particle", + "fickian_diffusion", + "msmr_diffusion", + "polynomial_profile", + "total_particle_concentration", + "x_averaged_polynomial_profile", +] diff --git a/pybamm/models/submodels/particle/msmr_diffusion.py b/pybamm/models/submodels/particle/msmr_diffusion.py index c53f313ab4..fb712dcdef 100644 --- a/pybamm/models/submodels/particle/msmr_diffusion.py +++ b/pybamm/models/submodels/particle/msmr_diffusion.py @@ -12,6 +12,10 @@ class MSMRDiffusion(BaseParticle): :footcite:t:`Verbrugge2017`, along with parameter values for a number of substitutional materials. + In this submodel, the stoichiometry depends on the potential in the particle and + the temperature, so dUdT is not used. See `:meth:`pybamm.LithiumIonParameters.dUdT` + for more explanation. + Parameters ---------- param : parameter class @@ -45,7 +49,6 @@ def get_fundamental_variables(self): # particle-size distribution, if applicable). The potential is then used to # calculate the stoichiometry, which is used to calculate the particle # concentration. - c_max = self.phase_param.c_max if self.size_distribution is False: if self.x_average is False: U = pybamm.Variable( @@ -59,7 +62,7 @@ def get_fundamental_variables(self): U.print_name = f"U_{domain[0]}" else: U_xav = pybamm.Variable( - f"X-averaged {domain} {phase_name}particle " "potential [V]", + f"X-averaged {domain} {phase_name}particle potential [V]", f"{domain} {phase_name}particle", auxiliary_domains={"secondary": "current collector"}, ) @@ -68,7 +71,7 @@ def get_fundamental_variables(self): else: if self.x_average is False: U_distribution = pybamm.Variable( - f"{Domain} {phase_name}particle " "potential distribution [V]", + f"{Domain} {phase_name}particle potential distribution [V]", domain=f"{domain} {phase_name}particle", auxiliary_domains={ "secondary": f"{domain} {phase_name}particle size", @@ -117,24 +120,6 @@ def get_fundamental_variables(self): self._get_standard_potential_distribution_variables(U_distribution) ) - # Calculate the stoichiometry distribution from the potential distribution - x_distribution = self.phase_param.x(U_distribution) - dxdU_distribution = self.phase_param.dxdU(U_distribution) - - # Standard stoichiometry and concentration distribution variables - # (size-dependent) - c_s_distribution = x_distribution * c_max - variables.update( - self._get_standard_concentration_distribution_variables( - c_s_distribution - ) - ) - variables.update( - self._get_standard_differential_stoichiometry_distribution_variables( - dxdU_distribution - ) - ) - # Standard size-averaged variables. Average potentials using # the volume-weighted distribution since they are volume-based # quantities. Necessary for output variables "Total lithium in @@ -146,21 +131,6 @@ def get_fundamental_variables(self): # Standard potential variables variables.update(self._get_standard_potential_variables(U)) - # Standard fractional occupancy variables (these are indexed by reaction number) - variables.update(self._get_standard_fractional_occupancy_variables(U)) - variables.update( - self._get_standard_differential_fractional_occupancy_variables(U) - ) - - # Calculate the (total) stoichiometry from the potential - x = self.phase_param.x(U) - dxdU = self.phase_param.dxdU(U) - - # Standard (total) stoichiometry and concentration variables (size-independent) - c_s = x * c_max - variables.update(self._get_standard_concentration_variables(c_s)) - variables.update(self._get_standard_differential_stoichiometry_variables(dxdU)) - return variables def get_coupled_variables(self, variables): @@ -190,9 +160,7 @@ def get_coupled_variables(self, variables): f"X-averaged {domain} {phase_name}particle differential " "stoichiometry [V-1]" ] - U = variables[ - f"X-averaged {domain} {phase_name}particle " "potential [V]" - ] + U = variables[f"X-averaged {domain} {phase_name}particle potential [V]"] T = pybamm.PrimaryBroadcast( variables[f"X-averaged {domain} electrode temperature [K]"], [f"{domain} {phase_name}particle"], @@ -217,7 +185,7 @@ def get_coupled_variables(self, variables): "distribution [V-1]" ] U = variables[ - f"{Domain} {phase_name}particle potential " "distribution [V]" + f"{Domain} {phase_name}particle potential distribution [V]" ] # broadcast T to "particle size" domain then again into "particle" T = pybamm.PrimaryBroadcast( @@ -302,13 +270,11 @@ def set_rhs(self, variables): if self.x_average is False: U = variables[f"{Domain} {phase_name}particle potential [V]"] else: - U = variables[ - f"X-averaged {domain} {phase_name}particle " "potential [V]" - ] + U = variables[f"X-averaged {domain} {phase_name}particle potential [V]"] else: if self.x_average is False: U = variables[ - f"{Domain} {phase_name}particle " "potential distribution [V]" + f"{Domain} {phase_name}particle potential distribution [V]" ] else: U = variables[ @@ -325,13 +291,11 @@ def set_boundary_conditions(self, variables): if self.x_average is False: U = variables[f"{Domain} {phase_name}particle potential [V]"] else: - U = variables[ - f"X-averaged {domain} {phase_name}particle " "potential [V]" - ] + U = variables[f"X-averaged {domain} {phase_name}particle potential [V]"] else: if self.x_average is False: U = variables[ - f"{Domain} {phase_name}particle " "potential distribution [V]" + f"{Domain} {phase_name}particle potential distribution [V]" ] else: U = variables[ @@ -353,13 +317,11 @@ def set_initial_conditions(self, variables): if self.x_average is False: U = variables[f"{Domain} {phase_name}particle potential [V]"] else: - U = variables[ - f"X-averaged {domain} {phase_name}particle " "potential [V]" - ] + U = variables[f"X-averaged {domain} {phase_name}particle potential [V]"] else: if self.x_average is False: U = variables[ - f"{Domain} {phase_name}particle " "potential distribution [V]" + f"{Domain} {phase_name}particle potential distribution [V]" ] else: U = variables[ @@ -382,14 +344,14 @@ def _get_standard_potential_variables(self, U): U_av = pybamm.r_average(U_xav) variables = { f"{Domain} {phase_name}particle potential [V]": U, - f"X-averaged {domain} {phase_name}particle " "potential [V]": U_xav, - f"R-averaged {domain} {phase_name}particle " "potential [V]": U_rav, + f"X-averaged {domain} {phase_name}particle potential [V]": U_xav, + f"R-averaged {domain} {phase_name}particle potential [V]": U_rav, f"Average {domain} {phase_name}particle potential [V]": U_av, f"{Domain} {phase_name}particle surface potential [V]": U_surf, f"X-averaged {domain} {phase_name}particle " "surface potential [V]": U_surf_av, - f"Minimum {domain} {phase_name}particle potential [V]" "": pybamm.min(U), - f"Maximum {domain} {phase_name}particle potential [V]" "": pybamm.max(U), + f"Minimum {domain} {phase_name}particle potential [V]": pybamm.min(U), + f"Maximum {domain} {phase_name}particle potential [V]": pybamm.max(U), f"Minimum {domain} {phase_name}particle " "surface potential [V]": pybamm.min(U_surf), f"Maximum {domain} {phase_name}particle " @@ -492,7 +454,64 @@ def _get_standard_potential_distribution_variables(self, U): } return variables - def _get_standard_fractional_occupancy_variables(self, U): + +class MSMRStoichiometryVariables(BaseParticle): + def __init__(self, param, domain, options, phase="primary", x_average=False): + super().__init__(param, domain, options, phase) + self.x_average = x_average + + def get_coupled_variables(self, variables): + domain, Domain = self.domain_Domain + phase_name = self.phase_name + + U = variables[f"{Domain} {phase_name}particle potential [V]"] + T = variables[f"{Domain} electrode temperature [K]"] + + # Standard fractional occupancy variables (these are indexed by reaction number) + variables.update(self._get_standard_fractional_occupancy_variables(U, T)) + variables.update( + self._get_standard_differential_fractional_occupancy_variables(U, T) + ) + + # Calculate the (total) stoichiometry from the potential + x = self.phase_param.x(U, T) + dxdU = self.phase_param.dxdU(U, T) + + # Standard (total) stoichiometry and concentration variables (size-independent) + c_max = self.phase_param.c_max + c_s = x * c_max + variables.update(self._get_standard_concentration_variables(c_s)) + variables.update(self._get_standard_differential_stoichiometry_variables(dxdU)) + + if self.size_distribution is True: + U_distribution = variables[ + f"{Domain} {phase_name}particle potential distribution [V]" + ] + T = variables[f"{Domain} electrode temperature [K]"] + T_distribution = pybamm.PrimaryBroadcast( + pybamm.PrimaryBroadcast(T, f"{domain} particle size"), + f"{domain} particle", + ) + # Calculate the stoichiometry distribution from the potential distribution + x_distribution = self.phase_param.x(U_distribution, T_distribution) + dxdU_distribution = self.phase_param.dxdU(U_distribution, T_distribution) + + # Standard stoichiometry and concentration distribution variables + # (size-dependent) + c_s_distribution = x_distribution * c_max + variables.update( + self._get_standard_concentration_distribution_variables( + c_s_distribution + ) + ) + variables.update( + self._get_standard_differential_stoichiometry_distribution_variables( + dxdU_distribution + ) + ) + return variables + + def _get_standard_fractional_occupancy_variables(self, U, T): options = self.options domain = self.domain d = domain[0] @@ -500,7 +519,7 @@ def _get_standard_fractional_occupancy_variables(self, U): # Loop over all reactions N = int(getattr(options, domain)["number of MSMR reactions"]) for i in range(N): - x = self.phase_param.x_j(U, i) + x = self.phase_param.x_j(U, T, i) x_surf = pybamm.surf(x) x_surf_av = pybamm.x_average(x_surf) x_xav = pybamm.x_average(x) @@ -518,7 +537,7 @@ def _get_standard_fractional_occupancy_variables(self, U): ) return variables - def _get_standard_differential_fractional_occupancy_variables(self, U): + def _get_standard_differential_fractional_occupancy_variables(self, U, T): options = self.options domain = self.domain d = domain[0] @@ -526,7 +545,7 @@ def _get_standard_differential_fractional_occupancy_variables(self, U): # Loop over all reactions N = int(getattr(options, domain)["number of MSMR reactions"]) for i in range(N): - dxdU = self.phase_param.dxdU_j(U, i) + dxdU = self.phase_param.dxdU_j(U, T, i) dxdU_surf = pybamm.surf(dxdU) dxdU_surf_av = pybamm.x_average(dxdU_surf) dxdU_xav = pybamm.x_average(dxdU) diff --git a/pybamm/models/submodels/particle_mechanics/__init__.py b/pybamm/models/submodels/particle_mechanics/__init__.py index 9b89a95d55..7aec637e7d 100644 --- a/pybamm/models/submodels/particle_mechanics/__init__.py +++ b/pybamm/models/submodels/particle_mechanics/__init__.py @@ -2,3 +2,6 @@ from .crack_propagation import CrackPropagation from .swelling_only import SwellingOnly from .no_mechanics import NoMechanics + +__all__ = ['base_mechanics', 'crack_propagation', 'no_mechanics', + 'swelling_only'] diff --git a/pybamm/models/submodels/porosity/__init__.py b/pybamm/models/submodels/porosity/__init__.py index de09dca46c..e45768a9c1 100644 --- a/pybamm/models/submodels/porosity/__init__.py +++ b/pybamm/models/submodels/porosity/__init__.py @@ -2,3 +2,6 @@ from .constant_porosity import Constant from .reaction_driven_porosity import ReactionDriven from .reaction_driven_porosity_ode import ReactionDrivenODE + +__all__ = ['base_porosity', 'constant_porosity', 'reaction_driven_porosity', + 'reaction_driven_porosity_ode'] diff --git a/pybamm/models/submodels/thermal/__init__.py b/pybamm/models/submodels/thermal/__init__.py index 056a555e3f..cc8f769f36 100644 --- a/pybamm/models/submodels/thermal/__init__.py +++ b/pybamm/models/submodels/thermal/__init__.py @@ -2,3 +2,5 @@ from .isothermal import Isothermal from .lumped import Lumped from . import pouch_cell + +__all__ = ['base_thermal', 'isothermal', 'lumped', 'pouch_cell'] diff --git a/pybamm/models/submodels/thermal/base_thermal.py b/pybamm/models/submodels/thermal/base_thermal.py index 808cdefc67..c5ebbc7dbd 100644 --- a/pybamm/models/submodels/thermal/base_thermal.py +++ b/pybamm/models/submodels/thermal/base_thermal.py @@ -2,6 +2,7 @@ # Base class for thermal effects # import pybamm +import numpy as np class BaseThermal(pybamm.BaseSubModel): @@ -16,8 +17,12 @@ class BaseThermal(pybamm.BaseSubModel): A dictionary of options to be passed to the model. """ - def __init__(self, param, options=None): + def __init__(self, param, options=None, x_average=False): super().__init__(param, options=options) + self.x_average = x_average + + if self.options["heat of mixing"] == "true": + pybamm.citations.register("Richardson2021") def _get_standard_fundamental_variables(self, T_dict): """ @@ -117,7 +122,7 @@ def _get_standard_coupled_variables(self, variables): # Total Ohmic heating Q_ohm = Q_ohm_s + Q_ohm_e - num_phases = int(getattr(self.options, "positive")["particle phases"]) + num_phases = int(self.options.positive["particle phases"]) phase_names = [""] if num_phases > 1: phase_names = ["primary ", "secondary "] @@ -135,10 +140,10 @@ def _get_standard_coupled_variables(self, variables): dUdT_p = variables[f"Positive electrode {phase}entropic change [V.K-1]"] Q_rev_p += a_j_p * T_p * dUdT_p - num_phases = int(getattr(self.options, "negative")["particle phases"]) + num_phases = int(self.options.negative["particle phases"]) phase_names = [""] if num_phases > 1: - phase_names = ["primary", "secondary"] + phase_names = ["primary ", "secondary "] if self.options.electrode_types["negative"] == "planar": Q_rxn_n = pybamm.FullBroadcast( @@ -175,8 +180,12 @@ def _get_standard_coupled_variables(self, variables): Q_rev_n, pybamm.FullBroadcast(0, "separator", "current collector"), Q_rev_p ) + # Heat of mixing + Q_mix_s_n, Q_mix_s_s, Q_mix_s_p = self._heat_of_mixing(variables) + Q_mix = pybamm.concatenation(Q_mix_s_n, Q_mix_s_s, Q_mix_s_p) + # Total heating - Q = Q_ohm + Q_rxn + Q_rev + Q = Q_ohm + Q_rxn + Q_rev + Q_mix # Compute the X-average over the entire cell, including current collectors # Note: this can still be a function of y and z for higher-dimensional pouch @@ -184,6 +193,7 @@ def _get_standard_coupled_variables(self, variables): Q_ohm_av = self._x_average(Q_ohm, Q_ohm_s_cn, Q_ohm_s_cp) Q_rxn_av = self._x_average(Q_rxn, 0, 0) Q_rev_av = self._x_average(Q_rev, 0, 0) + Q_mix_av = self._x_average(Q_mix, 0, 0) Q_av = self._x_average(Q, Q_ohm_s_cn, Q_ohm_s_cp) # Compute the integrated heat source per unit simulated electrode-pair area @@ -192,11 +202,14 @@ def _get_standard_coupled_variables(self, variables): Q_ohm_Wm2 = Q_ohm_av * param.L Q_rxn_Wm2 = Q_rxn_av * param.L Q_rev_Wm2 = Q_rev_av * param.L + Q_mix_Wm2 = Q_mix_av * param.L Q_Wm2 = Q_av * param.L + # Now average over the electrode height and width Q_ohm_Wm2_av = self._yz_average(Q_ohm_Wm2) Q_rxn_Wm2_av = self._yz_average(Q_rxn_Wm2) Q_rev_Wm2_av = self._yz_average(Q_rev_Wm2) + Q_mix_Wm2_av = self._yz_average(Q_mix_Wm2) Q_Wm2_av = self._yz_average(Q_Wm2) # Compute total heat source terms (in W) over the *entire cell volume*, not @@ -208,6 +221,7 @@ def _get_standard_coupled_variables(self, variables): Q_ohm_W = Q_ohm_Wm2_av * n_elec * A Q_rxn_W = Q_rxn_Wm2_av * n_elec * A Q_rev_W = Q_rev_Wm2_av * n_elec * A + Q_mix_W = Q_mix_Wm2_av * n_elec * A Q_W = Q_Wm2_av * n_elec * A # Compute volume-averaged heat source terms over the *entire cell volume*, not @@ -216,14 +230,20 @@ def _get_standard_coupled_variables(self, variables): Q_ohm_vol_av = Q_ohm_W / V Q_rxn_vol_av = Q_rxn_W / V Q_rev_vol_av = Q_rev_W / V + Q_mix_vol_av = Q_mix_W / V Q_vol_av = Q_W / V + # Effective heat capacity + T_vol_av = variables["Volume-averaged cell temperature [K]"] + rho_c_p_eff_av = param.rho_c_p_eff(T_vol_av) + variables.update( { # Ohmic "Ohmic heating [W.m-3]": Q_ohm, "X-averaged Ohmic heating [W.m-3]": Q_ohm_av, "Volume-averaged Ohmic heating [W.m-3]": Q_ohm_vol_av, + "Volume-averaged heat of mixing [W.m-3]": Q_mix_vol_av, "Ohmic heating per unit electrode-pair area [W.m-2]": Q_ohm_Wm2, "Ohmic heating [W]": Q_ohm_W, # Irreversible @@ -240,6 +260,12 @@ def _get_standard_coupled_variables(self, variables): "Volume-averaged reversible heating [W.m-3]": Q_rev_vol_av, "Reversible heating per unit electrode-pair area " "[W.m-2]": Q_rev_Wm2, "Reversible heating [W]": Q_rev_W, + # Mixing + "Heat of mixing [W.m-3]": Q_mix, + "X-averaged heat of mixing [W.m-3]": Q_mix_av, + "Volume-averaged heating of mixing [W.m-3]": Q_mix_vol_av, + "Heat of mixing per unit electrode-pair area " "[W.m-2]": Q_mix_Wm2, + "Heat of mixing [W]": Q_mix_W, # Total "Total heating [W.m-3]": Q, "X-averaged total heating [W.m-3]": Q_av, @@ -249,6 +275,9 @@ def _get_standard_coupled_variables(self, variables): # Current collector "Negative current collector Ohmic heating [W.m-3]": Q_ohm_s_cn, "Positive current collector Ohmic heating [W.m-3]": Q_ohm_s_cp, + # Effective heat capacity + "Volume-averaged effective heat capacity [J.K-1.m-3]": rho_c_p_eff_av, + "Cell thermal volume [m3]": V, } ) return variables @@ -283,6 +312,74 @@ def _current_collector_heating(self, variables): Q_s_cp = self.param.p.sigma_cc * pybamm.grad_squared(phi_s_cp) return Q_s_cn, Q_s_cp + def _heat_of_mixing(self, variables): + """Compute heat of mixing source terms.""" + param = self.param + + if self.options["heat of mixing"] == "true": + F = pybamm.constants.F.value + pi = np.pi + + # Compute heat of mixing in negative electrode + if self.options.electrode_types["negative"] == "planar": + Q_mix_s_n = pybamm.FullBroadcast( + 0, ["negative electrode"], "current collector" + ) + else: + a_n = variables["Negative electrode surface area to volume ratio [m-1]"] + R_n = variables["Negative particle radius [m]"] + N_n = a_n / (4 * pi * R_n**2) + if self.x_average: + c_n = variables[ + "X-averaged negative particle concentration [mol.m-3]" + ] + T_n = variables["X-averaged negative electrode temperature [K]"] + else: + c_n = variables["Negative particle concentration [mol.m-3]"] + T_n = variables["Negative electrode temperature [K]"] + T_n_part = pybamm.PrimaryBroadcast(T_n, ["negative particle"]) + dc_n_dr2 = pybamm.inner(pybamm.grad(c_n), pybamm.grad(c_n)) + D_n = param.n.prim.D(c_n, T_n_part) + dUeq_n = param.n.prim.U(c_n / param.n.prim.c_max, T_n_part).diff(c_n) + integrand_r_n = D_n * dc_n_dr2 * dUeq_n + integration_variable_r_n = [ + pybamm.SpatialVariable("r", domain=integrand_r_n.domain) + ] + integral_r_n = pybamm.Integral(integrand_r_n, integration_variable_r_n) + Q_mix_s_n = -F * N_n * integral_r_n + + # Compute heat of mixing in positive electrode + a_p = variables["Positive electrode surface area to volume ratio [m-1]"] + R_p = variables["Positive particle radius [m]"] + N_p = a_p / (4 * pi * R_p**2) + if self.x_average: + c_p = variables["X-averaged positive particle concentration [mol.m-3]"] + T_p = variables["X-averaged positive electrode temperature [K]"] + else: + c_p = variables["Positive particle concentration [mol.m-3]"] + T_p = variables["Positive electrode temperature [K]"] + T_p_part = pybamm.PrimaryBroadcast(T_p, ["positive particle"]) + dc_p_dr2 = pybamm.inner(pybamm.grad(c_p), pybamm.grad(c_p)) + D_p = param.p.prim.D(c_p, T_p_part) + dUeq_p = param.p.prim.U(c_p / param.p.prim.c_max, T_p_part).diff(c_p) + integrand_r_p = D_p * dc_p_dr2 * dUeq_p + integration_variable_r_p = [ + pybamm.SpatialVariable("r", domain=integrand_r_p.domain) + ] + integral_r_p = pybamm.Integral(integrand_r_p, integration_variable_r_p) + Q_mix_s_p = -F * N_p * integral_r_p + Q_mix_s_s = pybamm.FullBroadcast(0, ["separator"], "current collector") + else: + Q_mix_s_n = pybamm.FullBroadcast( + 0, ["negative electrode"], "current collector" + ) + Q_mix_s_p = pybamm.FullBroadcast( + 0, ["positive electrode"], "current collector" + ) + Q_mix_s_s = pybamm.FullBroadcast(0, ["separator"], "current collector") + + return Q_mix_s_n, Q_mix_s_s, Q_mix_s_p + def _x_average(self, var, var_cn, var_cp): """ Computes the X-average over the whole cell (including current collectors) diff --git a/pybamm/models/submodels/thermal/isothermal.py b/pybamm/models/submodels/thermal/isothermal.py index 52b5277986..edcd47bbdf 100644 --- a/pybamm/models/submodels/thermal/isothermal.py +++ b/pybamm/models/submodels/thermal/isothermal.py @@ -18,8 +18,8 @@ class Isothermal(BaseThermal): A dictionary of options to be passed to the model. """ - def __init__(self, param, options=None): - super().__init__(param, options=options) + def __init__(self, param, options=None, x_average=False): + super().__init__(param, options=options, x_average=x_average) def get_fundamental_variables(self): # Set the x-averaged temperature to the ambient temperature, which can be @@ -73,6 +73,8 @@ def get_coupled_variables(self, variables): "Total heating [W]", "Negative current collector Ohmic heating [W.m-3]", "Positive current collector Ohmic heating [W.m-3]", + "Lumped total cooling [W.m-3]", + "Lumped total cooling [W]", ]: # All variables are zero variables.update({var: zero}) diff --git a/pybamm/models/submodels/thermal/lumped.py b/pybamm/models/submodels/thermal/lumped.py index 0f396a3f77..4afde4fa57 100644 --- a/pybamm/models/submodels/thermal/lumped.py +++ b/pybamm/models/submodels/thermal/lumped.py @@ -20,8 +20,8 @@ class Lumped(BaseThermal): """ - def __init__(self, param, options=None): - super().__init__(param, options=options) + def __init__(self, param, options=None, x_average=False): + super().__init__(param, options=options, x_average=x_average) pybamm.citations.register("Timms2021") def get_fundamental_variables(self): @@ -46,23 +46,31 @@ def get_fundamental_variables(self): def get_coupled_variables(self, variables): variables.update(self._get_standard_coupled_variables(variables)) + + # Newton cooling, accounting for surface area to volume ratio + T_vol_av = variables["Volume-averaged cell temperature [K]"] + T_amb = variables["Volume-averaged ambient temperature [K]"] + V = variables["Cell thermal volume [m3]"] + Q_cool_W = -self.param.h_total * (T_vol_av - T_amb) * self.param.A_cooling + Q_cool_vol_av = Q_cool_W / V + variables.update( + { + # Lumped cooling + "Lumped total cooling [W.m-3]": Q_cool_vol_av, + "Lumped total cooling [W]": Q_cool_W, + } + ) return variables def set_rhs(self, variables): T_vol_av = variables["Volume-averaged cell temperature [K]"] Q_vol_av = variables["Volume-averaged total heating [W.m-3]"] - T_amb = variables["Volume-averaged ambient temperature [K]"] + Q_cool_vol_av = variables["Lumped total cooling [W.m-3]"] + rho_c_p_eff_av = variables[ + "Volume-averaged effective heat capacity [J.K-1.m-3]" + ] - # Newton cooling, accounting for surface area to volume ratio - cell_surface_area = self.param.A_cooling - cell_volume = self.param.V_cell - Q_cool_vol_av = ( - -self.param.h_total * (T_vol_av - T_amb) * cell_surface_area / cell_volume - ) - - self.rhs = { - T_vol_av: (Q_vol_av + Q_cool_vol_av) / self.param.rho_c_p_eff(T_vol_av) - } + self.rhs = {T_vol_av: (Q_vol_av + Q_cool_vol_av) / rho_c_p_eff_av} def set_initial_conditions(self, variables): T_vol_av = variables["Volume-averaged cell temperature [K]"] diff --git a/pybamm/models/submodels/thermal/pouch_cell/__init__.py b/pybamm/models/submodels/thermal/pouch_cell/__init__.py index 7d0ddedbdf..571577e71a 100644 --- a/pybamm/models/submodels/thermal/pouch_cell/__init__.py +++ b/pybamm/models/submodels/thermal/pouch_cell/__init__.py @@ -1,3 +1,6 @@ from .x_full import OneDimensionalX from .pouch_cell_1D_current_collectors import CurrentCollector1D from .pouch_cell_2D_current_collectors import CurrentCollector2D + +__all__ = ['pouch_cell_1D_current_collectors', + 'pouch_cell_2D_current_collectors', 'x_full'] diff --git a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py index 2611dbafdc..a4908c6f5d 100644 --- a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py +++ b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py @@ -3,7 +3,7 @@ # import pybamm -from ..base_thermal import BaseThermal +from pybamm.models.submodels.thermal.base_thermal import BaseThermal class CurrentCollector1D(BaseThermal): @@ -22,8 +22,8 @@ class CurrentCollector1D(BaseThermal): """ - def __init__(self, param, options=None): - super().__init__(param, options=options) + def __init__(self, param, options=None, x_average=True): + super().__init__(param, options=options, x_average=x_average) pybamm.citations.register("Timms2021") def get_fundamental_variables(self): diff --git a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py index a5c7c42b17..7955ee4c38 100644 --- a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py +++ b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py @@ -3,7 +3,7 @@ # import pybamm -from ..base_thermal import BaseThermal +from pybamm.models.submodels.thermal.base_thermal import BaseThermal class CurrentCollector2D(BaseThermal): @@ -22,8 +22,8 @@ class CurrentCollector2D(BaseThermal): """ - def __init__(self, param, options=None): - super().__init__(param, options=options) + def __init__(self, param, options=None, x_average=True): + super().__init__(param, options=options, x_average=x_average) pybamm.citations.register("Timms2021") def get_fundamental_variables(self): diff --git a/pybamm/models/submodels/thermal/pouch_cell/x_full.py b/pybamm/models/submodels/thermal/pouch_cell/x_full.py index 64a6e687c6..f4aa07c563 100644 --- a/pybamm/models/submodels/thermal/pouch_cell/x_full.py +++ b/pybamm/models/submodels/thermal/pouch_cell/x_full.py @@ -3,7 +3,7 @@ # import pybamm -from ..base_thermal import BaseThermal +from pybamm.models.submodels.thermal.base_thermal import BaseThermal class OneDimensionalX(BaseThermal): @@ -24,8 +24,8 @@ class OneDimensionalX(BaseThermal): """ - def __init__(self, param, options=None): - super().__init__(param, options=options) + def __init__(self, param, options=None, x_average=False): + super().__init__(param, options=options, x_average=x_average) pybamm.citations.register("Timms2021") def get_fundamental_variables(self): diff --git a/pybamm/models/submodels/transport_efficiency/__init__.py b/pybamm/models/submodels/transport_efficiency/__init__.py index 13fbe8487d..3c084c4c47 100644 --- a/pybamm/models/submodels/transport_efficiency/__init__.py +++ b/pybamm/models/submodels/transport_efficiency/__init__.py @@ -1,2 +1,13 @@ from .base_transport_efficiency import BaseModel -from .bruggeman_transport_efficiency import Bruggeman +from .bruggeman import Bruggeman +from .cation_exchange_membrane import CationExchangeMembrane +from .heterogeneous_catalyst import HeterogeneousCatalyst +from .hyperbola_of_revolution import HyperbolaOfRevolution +from .ordered_packing import OrderedPacking +from .overlapping_spheres import OverlappingSpheres +from .random_overlapping_cylinders import RandomOverlappingCylinders +from .tortuosity_factor import TortuosityFactor + +__all__ = ['base_transport_efficiency', 'bruggeman', 'cation_exchange_membrane', + 'heterogeneous_catalyst', 'hyperbola_of_revolution', 'ordered_packing', 'overlapping_spheres', + 'random_overlapping_cylinders', 'tortuosity_factor'] diff --git a/pybamm/models/submodels/transport_efficiency/base_transport_efficiency.py b/pybamm/models/submodels/transport_efficiency/base_transport_efficiency.py index 6c99cea328..c0582c013e 100644 --- a/pybamm/models/submodels/transport_efficiency/base_transport_efficiency.py +++ b/pybamm/models/submodels/transport_efficiency/base_transport_efficiency.py @@ -41,6 +41,6 @@ def _get_standard_transport_efficiency_variables(self, tor_dict): ) # Override print_name - tor.print_name = r"\epsilon^{b_e}" + tor.print_name = r"\mathcal{B}" return variables diff --git a/pybamm/models/submodels/transport_efficiency/bruggeman_transport_efficiency.py b/pybamm/models/submodels/transport_efficiency/bruggeman.py similarity index 75% rename from pybamm/models/submodels/transport_efficiency/bruggeman_transport_efficiency.py rename to pybamm/models/submodels/transport_efficiency/bruggeman.py index 5110ef8d13..ec26d7955d 100644 --- a/pybamm/models/submodels/transport_efficiency/bruggeman_transport_efficiency.py +++ b/pybamm/models/submodels/transport_efficiency/bruggeman.py @@ -1,12 +1,13 @@ # -# Class for Bruggemantransport_efficiency +# Class for Bruggeman transport_efficiency # import pybamm from .base_transport_efficiency import BaseModel class Bruggeman(BaseModel): - """Submodel for Bruggeman transport_efficiency + """Submodel for Bruggeman transport_efficiency, + :footcite:t:`bruggeman1935berechnung` Parameters ---------- @@ -27,8 +28,10 @@ def get_coupled_variables(self, variables): for domain in self.options.whole_cell_domains: Domain = domain.capitalize() eps_k = variables[f"{Domain} porosity"] + pybamm.citations.register("bruggeman1935berechnung") b_k = self.param.domain_params[domain.split()[0]].b_e - tor_dict[domain] = eps_k**b_k + tor_k = eps_k**b_k + tor_dict[domain] = tor_k elif self.component == "Electrode": tor_dict = {} for domain in self.options.whole_cell_domains: @@ -36,11 +39,11 @@ def get_coupled_variables(self, variables): tor_k = pybamm.FullBroadcast(0, "separator", "current collector") else: Domain = domain.capitalize() - eps_k = variables[f"{Domain} active material volume fraction"] + phi_k = 1 - variables[f"{Domain} porosity"] + pybamm.citations.register("bruggeman1935berechnung") b_k = self.param.domain_params[domain.split()[0]].b_s - tor_k = eps_k**b_k + tor_k = phi_k**b_k tor_dict[domain] = tor_k - variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) return variables diff --git a/pybamm/models/submodels/transport_efficiency/cation_exchange_membrane.py b/pybamm/models/submodels/transport_efficiency/cation_exchange_membrane.py new file mode 100644 index 0000000000..3ffb57e7de --- /dev/null +++ b/pybamm/models/submodels/transport_efficiency/cation_exchange_membrane.py @@ -0,0 +1,47 @@ +# +# Class for cation-exchange membrane transport_efficiency +# +import pybamm +from .base_transport_efficiency import BaseModel + + +class CationExchangeMembrane(BaseModel): + """Submodel for Cation Exchange Membrane transport_efficiency, + :footcite:t:`bruggeman1935berechnung`, :footcite:t:`shen2007critical` + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + component : str + The material for the model ('electrolyte' or 'electrode'). + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, component, options=None): + super().__init__(param, component, options=options) + + def get_coupled_variables(self, variables): + pybamm.citations.register("shen2007critical") + pybamm.citations.register("mackie1955diffusion") + if self.component == "Electrolyte": + tor_dict = {} + for domain in self.options.whole_cell_domains: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} porosity"] + tor_k = ((2 - eps_k) / eps_k) ** 2 + tor_dict[domain] = tor_k + elif self.component == "Electrode": + tor_dict = {} + for domain in self.options.whole_cell_domains: + if domain == "separator": + tor_k = pybamm.FullBroadcast(0, "separator", "current collector") + else: + Domain = domain.capitalize() + phi_k = 1 - variables[f"{Domain} porosity"] + tor_k = ((2 - phi_k) / phi_k) ** 2 + tor_dict[domain] = tor_k + variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) + + return variables diff --git a/pybamm/models/submodels/transport_efficiency/heterogeneous_catalyst.py b/pybamm/models/submodels/transport_efficiency/heterogeneous_catalyst.py new file mode 100644 index 0000000000..7ec8bc3580 --- /dev/null +++ b/pybamm/models/submodels/transport_efficiency/heterogeneous_catalyst.py @@ -0,0 +1,47 @@ +# +# Class for heterogeneous catalyst transport_efficiency +# +import pybamm +from .base_transport_efficiency import BaseModel + + +class HeterogeneousCatalyst(BaseModel): + """Submodel for Heterogeneous Catalyst transport_efficiency + :footcite:t:`beeckman1990mathematical`, :footcite:t:`shen2007critical` + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + component : str + The material for the model ('electrolyte' or 'electrode'). + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, component, options=None): + super().__init__(param, component, options=options) + + def get_coupled_variables(self, variables): + pybamm.citations.register("shen2007critical") + pybamm.citations.register("beeckman1990mathematical") + if self.component == "Electrolyte": + tor_dict = {} + for domain in self.options.whole_cell_domains: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} porosity"] + tor_k = eps_k / (1 - (1 - eps_k) ** (1 / 3)) + tor_dict[domain] = tor_k + elif self.component == "Electrode": + tor_dict = {} + for domain in self.options.whole_cell_domains: + if domain == "separator": + tor_k = pybamm.FullBroadcast(0, "separator", "current collector") + else: + Domain = domain.capitalize() + phi_k = 1 - variables[f"{Domain} porosity"] + tor_k = phi_k / (1 - (1 - phi_k) ** (1 / 3)) + tor_dict[domain] = tor_k + variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) + + return variables diff --git a/pybamm/models/submodels/transport_efficiency/hyperbola_of_revolution.py b/pybamm/models/submodels/transport_efficiency/hyperbola_of_revolution.py new file mode 100644 index 0000000000..306c66b774 --- /dev/null +++ b/pybamm/models/submodels/transport_efficiency/hyperbola_of_revolution.py @@ -0,0 +1,47 @@ +# +# Class for hyperbola of revolution transport_efficiency +# +import pybamm +from .base_transport_efficiency import BaseModel + + +class HyperbolaOfRevolution(BaseModel): + """Submodel for Hyperbola of revolution transport_efficiency + :footcite:t:`petersen1958diffusion`, :footcite:t:`shen2007critical` + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + component : str + The material for the model ('electrolyte' or 'electrode'). + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, component, options=None): + super().__init__(param, component, options=options) + + def get_coupled_variables(self, variables): + pybamm.citations.register("shen2007critical") + pybamm.citations.register("petersen1958diffusion") + if self.component == "Electrolyte": + tor_dict = {} + for domain in self.options.whole_cell_domains: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} porosity"] + tor_k = 2 - eps_k + tor_dict[domain] = tor_k + elif self.component == "Electrode": + tor_dict = {} + for domain in self.options.whole_cell_domains: + if domain == "separator": + tor_k = pybamm.FullBroadcast(0, "separator", "current collector") + else: + Domain = domain.capitalize() + phi_k = 1 - variables[f"{Domain} porosity"] + tor_k = 2 - phi_k + tor_dict[domain] = tor_k + variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) + + return variables diff --git a/pybamm/models/submodels/transport_efficiency/ordered_packing.py b/pybamm/models/submodels/transport_efficiency/ordered_packing.py new file mode 100644 index 0000000000..13b3a3515e --- /dev/null +++ b/pybamm/models/submodels/transport_efficiency/ordered_packing.py @@ -0,0 +1,47 @@ +# +# Class for Ordered packing transport_efficiency +# +import pybamm +from .base_transport_efficiency import BaseModel + + +class OrderedPacking(BaseModel): + """Submodel for Ordered Packing transport_efficiency + :footcite:t:`akanni1987effective`, :footcite:t:`shen2007critical` + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + component : str + The material for the model ('electrolyte' or 'electrode'). + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, component, options=None): + super().__init__(param, component, options=options) + + def get_coupled_variables(self, variables): + pybamm.citations.register("shen2007critical") + pybamm.citations.register("akanni1987effective") + if self.component == "Electrolyte": + tor_dict = {} + for domain in self.options.whole_cell_domains: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} porosity"] + tor_k = (3 - eps_k) * 0.5 + tor_dict[domain] = tor_k + elif self.component == "Electrode": + tor_dict = {} + for domain in self.options.whole_cell_domains: + if domain == "separator": + tor_k = pybamm.FullBroadcast(0, "separator", "current collector") + else: + Domain = domain.capitalize() + phi_k = 1 - variables[f"{Domain} porosity"] + tor_k = (3 - phi_k) * 0.5 + tor_dict[domain] = tor_k + variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) + + return variables diff --git a/pybamm/models/submodels/transport_efficiency/overlapping_spheres.py b/pybamm/models/submodels/transport_efficiency/overlapping_spheres.py new file mode 100644 index 0000000000..9bbed1fd05 --- /dev/null +++ b/pybamm/models/submodels/transport_efficiency/overlapping_spheres.py @@ -0,0 +1,47 @@ +# +# Class for overlapping spheres transport_efficiency +# +import pybamm +from .base_transport_efficiency import BaseModel + + +class OverlappingSpheres(BaseModel): + """Submodel for Overlapping Spheres transport_efficiency + :footcite:t:`weissberg1963effective`, :footcite:t:`shen2007critical` + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + component : str + The material for the model ('electrolyte' or 'electrode'). + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, component, options=None): + super().__init__(param, component, options=options) + + def get_coupled_variables(self, variables): + pybamm.citations.register("shen2007critical") + pybamm.citations.register("weissberg1963effective") + if self.component == "Electrolyte": + tor_dict = {} + for domain in self.options.whole_cell_domains: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} porosity"] + tor_k = 1 - pybamm.Log(eps_k * 0.5) + tor_dict[domain] = tor_k + elif self.component == "Electrode": + tor_dict = {} + for domain in self.options.whole_cell_domains: + if domain == "separator": + tor_k = pybamm.FullBroadcast(0, "separator", "current collector") + else: + Domain = domain.capitalize() + phi_k = 1 - variables[f"{Domain} porosity"] + tor_k = 1 - pybamm.Log(phi_k * 0.5) + tor_dict[domain] = tor_k + variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) + + return variables diff --git a/pybamm/models/submodels/transport_efficiency/random_overlapping_cylinders.py b/pybamm/models/submodels/transport_efficiency/random_overlapping_cylinders.py new file mode 100644 index 0000000000..da32f2f4fe --- /dev/null +++ b/pybamm/models/submodels/transport_efficiency/random_overlapping_cylinders.py @@ -0,0 +1,47 @@ +# +# Class for random overlapping cylinders transport_efficiency +# +import pybamm +from .base_transport_efficiency import BaseModel + + +class RandomOverlappingCylinders(BaseModel): + """Submodel for Random Overlapping Cylinders transport_efficiency, + :footcite:t:`tomadakis1993transport`, :footcite:t:`shen2007critical` + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + component : str + The material for the model ('electrolyte' or 'electrode'). + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, component, options=None): + super().__init__(param, component, options=options) + + def get_coupled_variables(self, variables): + pybamm.citations.register("shen2007critical") + pybamm.citations.register("tomadakis1993transport") + if self.component == "Electrolyte": + tor_dict = {} + for domain in self.options.whole_cell_domains: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} porosity"] + tor_k = 1 - pybamm.Log(eps_k) + tor_dict[domain] = tor_k + elif self.component == "Electrode": + tor_dict = {} + for domain in self.options.whole_cell_domains: + if domain == "separator": + tor_k = pybamm.FullBroadcast(0, "separator", "current collector") + else: + Domain = domain.capitalize() + phi_k = 1 - variables[f"{Domain} porosity"] + tor_k = 1 - pybamm.Log(phi_k) + tor_dict[domain] = tor_k + variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) + + return variables diff --git a/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py b/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py new file mode 100644 index 0000000000..0f5686e476 --- /dev/null +++ b/pybamm/models/submodels/transport_efficiency/tortuosity_factor.py @@ -0,0 +1,46 @@ +# +# Class for tortuosity factor transport_efficiency +# +import pybamm +from .base_transport_efficiency import BaseModel + + +class TortuosityFactor(BaseModel): + """Submodel for user supplied tortuosity factor transport_efficiency + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + component : str + The material for the model ('electrolyte' or 'electrode'). + options : dict, optional + A dictionary of options to be passed to the model. + """ + + def __init__(self, param, component, options=None): + super().__init__(param, component, options=options) + + def get_coupled_variables(self, variables): + if self.component == "Electrolyte": + tor_dict = {} + for domain in self.options.whole_cell_domains: + Domain = domain.capitalize() + eps_k = variables[f"{Domain} porosity"] + tau_k = self.param.domain_params[domain.split()[0]].tau_e + tor_k = eps_k / tau_k + tor_dict[domain] = tor_k + elif self.component == "Electrode": + tor_dict = {} + for domain in self.options.whole_cell_domains: + if domain == "separator": + tor_k = pybamm.FullBroadcast(0, "separator", "current collector") + else: + Domain = domain.capitalize() + phi_k = 1 - variables[f"{Domain} porosity"] + tau_k = self.param.domain_params[domain.split()[0]].tau_s + tor_k = phi_k / tau_k + tor_dict[domain] = tor_k + variables.update(self._get_standard_transport_efficiency_variables(tor_dict)) + + return variables diff --git a/pybamm/parameters/__init__.py b/pybamm/parameters/__init__.py index cf81b82fdd..33d427b007 100644 --- a/pybamm/parameters/__init__.py +++ b/pybamm/parameters/__init__.py @@ -4,3 +4,9 @@ process_2D_data_csv, process_3D_data_csv, ) + +__all__ = ['base_parameters', 'bpx', 'constants', 'ecm_parameters', + 'electrical_parameters', 'geometric_parameters', + 'lead_acid_parameters', 'lithium_ion_parameters', 'parameter_sets', + 'parameter_values', 'process_parameter_data', + 'size_distribution_parameters', 'thermal_parameters'] diff --git a/pybamm/parameters/base_parameters.py b/pybamm/parameters/base_parameters.py index a7b319ec81..c686665019 100644 --- a/pybamm/parameters/base_parameters.py +++ b/pybamm/parameters/base_parameters.py @@ -19,7 +19,9 @@ def __getattribute__(self, name): return super().__getattribute__(name) except AttributeError as e: if name == "cap_init": - warnings.warn("Parameter 'cap_init' has been renamed to 'Q_init'") + warnings.warn( + "Parameter 'cap_init' has been renamed to 'Q_init'", stacklevel=2 + ) return self.Q_init for domain in ["n", "s", "p"]: if f"_{domain}_" in name or name.endswith(f"_{domain}"): @@ -32,14 +34,14 @@ def __getattribute__(self, name): raise AttributeError( f"param.{name} does not exist. It has been renamed to " f"param.{domain}.{name_without_domain}" - ) + ) from e elif hasattr(self_domain, "prim") and hasattr( self_domain.prim, name_without_domain ): raise AttributeError( f"param.{name} does not exist. It has been renamed to " f"param.{domain}.prim.{name_without_domain}" - ) + ) from e else: raise e else: diff --git a/pybamm/parameters/bpx.py b/pybamm/parameters/bpx.py index a97288b062..680477a74b 100644 --- a/pybamm/parameters/bpx.py +++ b/pybamm/parameters/bpx.py @@ -1,4 +1,5 @@ from bpx import BPX, Function, InterpolatedTable +from bpx.schema import ElectrodeBlended, ElectrodeBlendedSPM import pybamm import math from dataclasses import dataclass @@ -7,22 +8,39 @@ from pybamm import exp -import types -import functools +from functools import partial -def _copy_func(f): - """Based on http://stackoverflow.com/a/6528148/190597 (Glenn Maynard)""" - g = types.FunctionType( - f.__code__, - f.__globals__, - name=f.__name__, - argdefs=f.__defaults__, - closure=f.__closure__, - ) - g = functools.update_wrapper(g, f) - g.__kwdefaults__ = f.__kwdefaults__ - return g +def _callable_func(var, fun): + return fun(var) + + +def _interpolant_func(var, name, x, y): + return pybamm.Interpolant(x, y, var, name=name, interpolator="linear") + + +preamble = "from pybamm import exp, tanh, cosh\n\n" + + +def process_float_function_table(value, name): + """ + Process BPX FloatFunctionTable to a float, python function or data for a pybamm + Interpolant. + """ + if isinstance(value, Function): + value = value.to_python_function(preamble=preamble) + elif isinstance(value, InterpolatedTable): + # return (name, (x, y)) to match the output of + # `pybamm.parameters.process_1D_data` we will create an interpolant on a + # case-by-case basis to get the correct argument for each parameter + x = np.array(value.x) + y = np.array(value.y) + # sort the arrays as CasADi requires x to be in ascending order + sort_idx = np.argsort(x) + x = x[sort_idx] + y = y[sort_idx] + value = (name, (x, y)) + return value @dataclass @@ -43,12 +61,21 @@ class Domain: pre_name="Positive electrode ", short_pre_name="Positive ", ) +negative_particle = Domain( + name="negative particle", + pre_name="Negative particle ", + short_pre_name="Negative ", +) +positive_particle = Domain( + name="positive particle", + pre_name="Positive particle ", + short_pre_name="Positive ", +) positive_current_collector = Domain( name="positive current collector", pre_name="Positive current collector ", short_pre_name="", ) - negative_current_collector = Domain( name="negative current collector", pre_name="Negative current collector ", @@ -59,18 +86,49 @@ class Domain: separator = Domain(name="separator", pre_name="Separator ", short_pre_name="") experiment = Domain(name="experiment", pre_name="", short_pre_name="") +PHASE_NAMES = ["Primary: ", "Secondary: "] + + +def _get_phase_names(domain): + """ + Return a list of the phase names in a given domain + """ + if isinstance(domain, (ElectrodeBlended, ElectrodeBlendedSPM)): + phases = len(domain.particle.keys()) + else: + phases = 1 + if phases == 1: + return [""] + elif phases == 2: + return ["Primary: ", "Secondary: "] + else: + raise NotImplementedError( + "PyBaMM does not support more than two " + "particle phases in blended electrodes" + ) + def _bpx_to_param_dict(bpx: BPX) -> dict: - pybamm_dict = {} + """ + Turns a BPX object in to a dictionary of parameters for PyBaMM + """ + domain_phases = { + "negative electrode": _get_phase_names(bpx.parameterisation.negative_electrode), + "positive electrode": _get_phase_names(bpx.parameterisation.positive_electrode), + } + + # Loop over each component of BPX and add to pybamm dictionary + pybamm_dict: dict = {} pybamm_dict = _bpx_to_domain_param_dict( - bpx.parameterisation.cell, pybamm_dict, cell + bpx.parameterisation.positive_electrode, pybamm_dict, positive_electrode ) pybamm_dict = _bpx_to_domain_param_dict( - bpx.parameterisation.negative_electrode, pybamm_dict, negative_electrode + bpx.parameterisation.cell, pybamm_dict, cell ) pybamm_dict = _bpx_to_domain_param_dict( - bpx.parameterisation.positive_electrode, pybamm_dict, positive_electrode + bpx.parameterisation.negative_electrode, pybamm_dict, negative_electrode ) + pybamm_dict = _bpx_to_domain_param_dict( bpx.parameterisation.electrolyte, pybamm_dict, electrolyte ) @@ -88,7 +146,7 @@ def _bpx_to_param_dict(bpx: BPX) -> dict: # activity pybamm_dict["Thermodynamic factor"] = 1.0 - # assume Bruggeman relation for effection electrolyte properties + # assume Bruggeman relation for effective electrolyte properties for domain in [negative_electrode, separator, positive_electrode]: pybamm_dict[domain.pre_name + "Bruggeman coefficient (electrolyte)"] = 1.5 @@ -99,9 +157,9 @@ def _bpx_to_param_dict(bpx: BPX) -> dict: # BPX is for single cell in series, user can change this later pybamm_dict["Number of cells connected in series to make a battery"] = 1 - pybamm_dict[ - "Number of electrodes connected in parallel to make a cell" - ] = pybamm_dict["Number of electrode pairs connected in parallel to make a cell"] + pybamm_dict["Number of electrodes connected in parallel to make a cell"] = ( + pybamm_dict["Number of electrode pairs connected in parallel to make a cell"] + ) # electrode area equal_len_width = math.sqrt(pybamm_dict["Electrode area [m2]"]) @@ -119,9 +177,6 @@ def _bpx_to_param_dict(bpx: BPX) -> dict: # reference temperature T_ref = pybamm_dict["Reference temperature [K]"] - def arrhenius(Ea, T): - return exp(Ea / constants.R * (1 / T_ref - 1 / T)) - # lumped parameters for name in [ "Specific heat capacity [J.K-1.kg-1]", @@ -163,92 +218,23 @@ def arrhenius(Ea, T): {"Total heat transfer coefficient [W.m-2.K-1]": 0}, check_already_exists=False ) - # BET surface area - for domain in [negative_electrode, positive_electrode]: - pybamm_dict[domain.pre_name + "active material volume fraction"] = ( - pybamm_dict[domain.pre_name + "surface area per unit volume [m-1]"] - * pybamm_dict[domain.short_pre_name + "particle radius [m]"] - ) / 3.0 - # transport efficiency for domain in [negative_electrode, separator, positive_electrode]: pybamm_dict[domain.pre_name + "porosity"] = pybamm_dict[ domain.pre_name + "transport efficiency" ] ** (1.0 / 1.5) - # TODO: allow setting function parameters in a loop over domains + # define functional forms for pybamm parameters that depend on more than one + # variable - # ocp - - # negative electrode (only need to check for data, other cases pass through) - U_n = pybamm_dict[negative_electrode.pre_name + "OCP [V]"] - if isinstance(U_n, tuple): - - def _negative_electrode_ocp(sto): - name, (x, y) = U_n - return pybamm.Interpolant(x, y, sto, name=name, interpolator="linear") - - pybamm_dict[negative_electrode.pre_name + "OCP [V]"] = _negative_electrode_ocp - - # positive electrode (only need to check for data, other cases pass through) - U_p = pybamm_dict[positive_electrode.pre_name + "OCP [V]"] - if isinstance(U_p, tuple): - - def _positive_electrode_ocp(sto): - name, (x, y) = U_p - return pybamm.Interpolant(x, y, sto, name=name, interpolator="linear") - - pybamm_dict[positive_electrode.pre_name + "OCP [V]"] = _positive_electrode_ocp - - # entropic change - - # negative electrode - dUdT_n = pybamm_dict[ - negative_electrode.pre_name + "entropic change coefficient [V.K-1]" - ] - if callable(dUdT_n): - - def _negative_electrode_entropic_change(sto, c_s_max): - return dUdT_n(sto) - - elif isinstance(dUdT_n, tuple): - - def _negative_electrode_entropic_change(sto, c_s_max): - name, (x, y) = dUdT_n - return pybamm.Interpolant(x, y, sto, name=name, interpolator="linear") - - else: - - def _negative_electrode_entropic_change(sto, c_s_max): - return dUdT_n - - pybamm_dict[ - negative_electrode.pre_name + "OCP entropic change [V.K-1]" - ] = _negative_electrode_entropic_change - - # positive electrode - dUdT_p = pybamm_dict[ - positive_electrode.pre_name + "entropic change coefficient [V.K-1]" - ] - if callable(dUdT_p): - - def _positive_electrode_entropic_change(sto, c_s_max): - return dUdT_p(sto) - - elif isinstance(dUdT_p, tuple): - - def _positive_electrode_entropic_change(sto, c_s_max): - name, (x, y) = dUdT_p - return pybamm.Interpolant(x, y, sto, name=name, interpolator="linear") - - else: - - def _positive_electrode_entropic_change(sto, c_s_max): - return dUdT_p + def _arrhenius(Ea, T): + return exp(Ea / constants.R * (1 / T_ref - 1 / T)) - pybamm_dict[ - positive_electrode.pre_name + "OCP entropic change [V.K-1]" - ] = _positive_electrode_entropic_change + def _entropic_change(sto, c_s_max, dUdT, constant=False): + if constant: + return dUdT + else: + return dUdT(sto) # reaction rates in pybamm exchange current is defined j0 = k * sqrt(ce * cs * # (cs-cs_max)) in BPX exchange current is defined j0 = F * k_norm * sqrt((ce/ce0) * @@ -256,125 +242,120 @@ def _positive_electrode_entropic_change(sto, c_s_max): c_e = pybamm_dict["Initial concentration in electrolyte [mol.m-3]"] F = 96485 - # negative electrode - c_n_max = pybamm_dict[ - "Maximum concentration in " + negative_electrode.pre_name.lower() + "[mol.m-3]" - ] - k_n_norm = pybamm_dict[ - negative_electrode.pre_name + "reaction rate constant [mol.m-2.s-1]" - ] - Ea_k_n = pybamm_dict.get( - negative_electrode.pre_name - + "reaction rate constant activation energy [J.mol-1]", - 0.0, - ) - # Note that in BPX j = 2*F*k_norm*sqrt((ce/ce0)*(c/c_max)*(1-c/c_max))*sinh(...), - # and in PyBaMM j = 2*k*sqrt(ce*c*(c_max - c))*sinh(...) - k_n = k_n_norm * F / (c_n_max * c_e**0.5) - - def _negative_electrode_exchange_current_density(c_e, c_s_surf, c_s_max, T): - k_ref = k_n # (A/m2)(m3/mol)**1.5 - includes ref concentrations - - return ( - k_ref - * arrhenius(Ea_k_n, T) - * c_e**0.5 - * c_s_surf**0.5 - * (c_s_max - c_s_surf) ** 0.5 - ) - - pybamm_dict[ - negative_electrode.pre_name + "exchange-current density [A.m-2]" - ] = _copy_func(_negative_electrode_exchange_current_density) - - # positive electrode - c_p_max = pybamm_dict[ - "Maximum concentration in " + positive_electrode.pre_name.lower() + "[mol.m-3]" - ] - k_p_norm = pybamm_dict[ - positive_electrode.pre_name + "reaction rate constant [mol.m-2.s-1]" - ] - Ea_k_p = pybamm_dict.get( - positive_electrode.pre_name - + "reaction rate constant activation energy [J.mol-1]", - 0.0, - ) - # Note that in BPX j = 2*F*k_norm*sqrt((ce/ce0)*(c/c_max)*(1-c/c_max))*sinh(...), - # and in PyBaMM j = 2*k*sqrt(ce*c*(c_max - c))*sinh(...) - k_p = k_p_norm * F / (c_p_max * c_e**0.5) - - def _positive_electrode_exchange_current_density(c_e, c_s_surf, c_s_max, T): - k_ref = k_p # (A/m2)(m3/mol)**1.5 - includes ref concentrations - + def _exchange_current_density(c_e, c_s_surf, c_s_max, T, k_ref, Ea): return ( k_ref - * arrhenius(Ea_k_p, T) + * _arrhenius(Ea, T) * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 ) - pybamm_dict[domain.pre_name + "exchange-current density [A.m-2]"] = _copy_func( - _positive_electrode_exchange_current_density - ) - - # diffusivity - - # negative electrode - Ea_D_n = pybamm_dict.get( - negative_electrode.pre_name + "diffusivity activation energy [J.mol-1]", 0.0 - ) - D_n_ref = pybamm_dict[negative_electrode.pre_name + "diffusivity [m2.s-1]"] + def _diffusivity(sto, T, D_ref, Ea, constant=False): + if constant: + return _arrhenius(Ea, T) * D_ref + else: + return _arrhenius(Ea, T) * D_ref(sto) - if callable(D_n_ref): + def _conductivity(c_e, T, Ea, sigma_ref, constant=False): + if constant: + return _arrhenius(Ea, T) * sigma_ref + else: + return _arrhenius(Ea, T) * sigma_ref(c_e) - def _negative_electrode_diffusivity(sto, T): - return arrhenius(Ea_D_n, T) * D_n_ref(sto) - - elif isinstance(D_n_ref, tuple): - - def _negative_electrode_diffusivity(sto, T): - name, (x, y) = D_n_ref - return arrhenius(Ea_D_n, T) * pybamm.Interpolant( - x, y, sto, name=name, interpolator="linear" + # Loop over electrodes and construct derived parameters + for domain in [negative_electrode, positive_electrode]: + for phase_pre_name in domain_phases[domain.name]: + phase_domain_pre_name = phase_pre_name + domain.pre_name + + # BET surface area + pybamm_dict[phase_domain_pre_name + "active material volume fraction"] = ( + pybamm_dict[ + phase_domain_pre_name + "surface area per unit volume [m-1]" + ] + * pybamm_dict[ + phase_pre_name + domain.short_pre_name + "particle radius [m]" + ] + ) / 3.0 + + # ocp + U = pybamm_dict[phase_domain_pre_name + "OCP [V]"] + if isinstance(U, tuple): + pybamm_dict[phase_domain_pre_name + "OCP [V]"] = partial( + _interpolant_func, name=U[0], x=U[1][0], y=U[1][1] + ) + + # entropic change + dUdT = pybamm_dict[ + phase_domain_pre_name + "entropic change coefficient [V.K-1]" + ] + if callable(dUdT): + pybamm_dict[phase_domain_pre_name + "OCP entropic change [V.K-1]"] = ( + partial(_entropic_change, dUdT=dUdT) + ) + elif isinstance(dUdT, tuple): + pybamm_dict[phase_domain_pre_name + "OCP entropic change [V.K-1]"] = ( + partial( + _entropic_change, + dUdT=partial( + _interpolant_func, name=dUdT[0], x=dUdT[1][0], y=dUdT[1][1] + ), + ) + ) + else: + pybamm_dict[phase_domain_pre_name + "OCP entropic change [V.K-1]"] = ( + partial(_entropic_change, dUdT=dUdT, constant=True) + ) + + # reaction rate + c_max = pybamm_dict[ + phase_pre_name + + "Maximum concentration in " + + domain.pre_name.lower() + + "[mol.m-3]" + ] + k_norm = pybamm_dict[ + phase_domain_pre_name + "reaction rate constant [mol.m-2.s-1]" + ] + Ea_k = pybamm_dict.get( + phase_domain_pre_name + + "reaction rate constant activation energy [J.mol-1]", + 0.0, ) - - else: - - def _negative_electrode_diffusivity(sto, T): - return arrhenius(Ea_D_n, T) * D_n_ref - - pybamm_dict[negative_electrode.pre_name + "diffusivity [m2.s-1]"] = _copy_func( - _negative_electrode_diffusivity - ) - - # positive electrode - Ea_D_p = pybamm_dict.get( - positive_electrode.pre_name + "diffusivity activation energy [J.mol-1]", 0.0 - ) - D_p_ref = pybamm_dict[positive_electrode.pre_name + "diffusivity [m2.s-1]"] - - if callable(D_p_ref): - - def _positive_electrode_diffusivity(sto, T): - return arrhenius(Ea_D_p, T) * D_p_ref(sto) - - elif isinstance(D_p_ref, tuple): - - def _positive_electrode_diffusivity(sto, T): - name, (x, y) = D_p_ref - return arrhenius(Ea_D_p, T) * pybamm.Interpolant( - x, y, sto, name=name, interpolator="linear" + # Note that in BPX j = 2*F*k_norm*sqrt((ce/ce0)*(c/c_max)*(1-c/c_max))... + # *sinh(), + # and in PyBaMM j = 2*k*sqrt(ce*c*(c_max - c))*sinh() + k = k_norm * F / (c_max * c_e**0.5) + pybamm_dict[phase_domain_pre_name + "exchange-current density [A.m-2]"] = ( + partial(_exchange_current_density, k_ref=k, Ea=Ea_k) ) - else: - - def _positive_electrode_diffusivity(sto, T): - return arrhenius(Ea_D_p, T) * D_p_ref - - pybamm_dict[positive_electrode.pre_name + "diffusivity [m2.s-1]"] = _copy_func( - _positive_electrode_diffusivity - ) + # diffusivity + Ea_D = pybamm_dict.get( + phase_domain_pre_name + "diffusivity activation energy [J.mol-1]", + 0.0, + ) + pybamm_dict[ + phase_domain_pre_name + "diffusivity activation energy [J.mol-1]" + ] = Ea_D + D_ref = pybamm_dict[phase_domain_pre_name + "diffusivity [m2.s-1]"] + + if callable(D_ref): + pybamm_dict[phase_domain_pre_name + "diffusivity [m2.s-1]"] = partial( + _diffusivity, D_ref=D_ref, Ea=Ea_D + ) + elif isinstance(D_ref, tuple): + pybamm_dict[phase_domain_pre_name + "diffusivity [m2.s-1]"] = partial( + _diffusivity, + D_ref=partial( + _interpolant_func, name=D_ref[0], x=D_ref[1][0], y=D_ref[1][1] + ), + Ea=Ea_D, + ) + else: + pybamm_dict[phase_domain_pre_name + "diffusivity [m2.s-1]"] = partial( + _diffusivity, D_ref=D_ref, Ea=Ea_D, constant=True + ) # electrolyte Ea_D_e = pybamm_dict.get( @@ -383,26 +364,21 @@ def _positive_electrode_diffusivity(sto, T): D_e_ref = pybamm_dict[electrolyte.pre_name + "diffusivity [m2.s-1]"] if callable(D_e_ref): - - def _electrolyte_diffusivity(sto, T): - return arrhenius(Ea_D_e, T) * D_e_ref(sto) - + pybamm_dict[electrolyte.pre_name + "diffusivity [m2.s-1]"] = partial( + _diffusivity, D_ref=D_e_ref, Ea=Ea_D_e + ) elif isinstance(D_e_ref, tuple): - - def _electrolyte_diffusivity(sto, T): - name, (x, y) = D_e_ref - return arrhenius(Ea_D_e, T) * pybamm.Interpolant( - x, y, sto, name=name, interpolator="linear" - ) - + pybamm_dict[electrolyte.pre_name + "diffusivity [m2.s-1]"] = partial( + _diffusivity, + D_ref=partial( + _interpolant_func, name=D_e_ref[0], x=D_e_ref[1][0], y=D_e_ref[1][1] + ), + Ea=Ea_D_e, + ) else: - - def _electrolyte_diffusivity(sto, T): - return arrhenius(Ea_D_e, T) * D_e_ref - - pybamm_dict[electrolyte.pre_name + "diffusivity [m2.s-1]"] = _copy_func( - _electrolyte_diffusivity - ) + pybamm_dict[electrolyte.pre_name + "diffusivity [m2.s-1]"] = partial( + _diffusivity, D_ref=D_e_ref, Ea=Ea_D_e, constant=True + ) # conductivity Ea_sigma_e = pybamm_dict.get( @@ -411,68 +387,96 @@ def _electrolyte_diffusivity(sto, T): sigma_e_ref = pybamm_dict[electrolyte.pre_name + "conductivity [S.m-1]"] if callable(sigma_e_ref): - - def _conductivity(c_e, T): - return arrhenius(Ea_sigma_e, T) * sigma_e_ref(c_e) - + pybamm_dict[electrolyte.pre_name + "conductivity [S.m-1]"] = partial( + _conductivity, sigma_ref=sigma_e_ref, Ea=Ea_sigma_e + ) elif isinstance(sigma_e_ref, tuple): - - def _conductivity(c_e, T): - name, (x, y) = sigma_e_ref - return arrhenius(Ea_sigma_e, T) * pybamm.Interpolant( - x, y, c_e, name=name, interpolator="linear" - ) - + pybamm_dict[electrolyte.pre_name + "conductivity [S.m-1]"] = partial( + _conductivity, + sigma_ref=partial( + _interpolant_func, + name=sigma_e_ref[0], + x=sigma_e_ref[1][0], + y=sigma_e_ref[1][1], + ), + Ea=Ea_sigma_e, + ) else: + pybamm_dict[electrolyte.pre_name + "conductivity [S.m-1]"] = partial( + _conductivity, sigma_ref=sigma_e_ref, Ea=Ea_sigma_e, constant=True + ) - def _conductivity(c_e, T): - return arrhenius(Ea_sigma_e, T) * sigma_e_ref - - pybamm_dict[electrolyte.pre_name + "conductivity [S.m-1]"] = _copy_func( - _conductivity - ) - + # Add user-defined parameters, if any + user_defined = bpx.parameterisation.user_defined + if user_defined: + for name in user_defined.__dict__.keys(): + value = getattr(user_defined, name) + value = process_float_function_table(value, name) + if callable(value): + pybamm_dict[name] = partial(_callable_func, fun=value) + elif isinstance(value, tuple): + pybamm_dict[name] = partial( + _interpolant_func, name=value[0], x=value[1][0], y=value[1][1] + ) + else: + pybamm_dict[name] = value return pybamm_dict -preamble = "from pybamm import exp, tanh, cosh\n\n" +def _get_pybamm_name(pybamm_name, domain): + """ + Process pybamm name to include domain name and handle special cases + """ + pybamm_name_lower = pybamm_name[:1].lower() + pybamm_name[1:] + if pybamm_name.startswith("Initial concentration") or pybamm_name.startswith( + "Maximum concentration" + ): + init_len = len("Initial concentration ") + pybamm_name = ( + pybamm_name[:init_len] + + "in " + + domain.pre_name.lower() + + pybamm_name[init_len:] + ) + elif pybamm_name.startswith("Particle radius"): + pybamm_name = domain.short_pre_name + pybamm_name_lower + elif pybamm_name.startswith("OCP"): + pybamm_name = domain.pre_name + pybamm_name + elif pybamm_name.startswith("Cation transference number"): + pybamm_name = pybamm_name + elif domain.pre_name != "": + pybamm_name = domain.pre_name + pybamm_name_lower + return pybamm_name def _bpx_to_domain_param_dict(instance: BPX, pybamm_dict: dict, domain: Domain) -> dict: + """ + Turns a BPX instance in to a dictionary of parameters for PyBaMM for a given domain + """ + # Loop over fields in BPX instance and add to pybamm dictionary for name, field in instance.__fields__.items(): value = getattr(instance, name) - if value is None: - continue - elif isinstance(value, Function): - value = value.to_python_function(preamble=preamble) - elif isinstance(value, InterpolatedTable): - # return (name, (x, y)) to match the output of - # `pybamm.parameters.process_1D_data` we will create an interpolant on a - # case-by-case basis to get the correct argument for each parameter - x = np.array(value.x) - y = np.array(value.y) - value = (name, (x, y)) - - pybamm_name = field.field_info.alias - pybamm_name_lower = pybamm_name[:1].lower() + pybamm_name[1:] - if pybamm_name.startswith("Initial concentration") or pybamm_name.startswith( - "Maximum concentration" + # Handle blended electrodes, where the field is now an instance of + # ElectrodeBlended or ElectrodeBlendedSPM + if ( + isinstance(instance, (ElectrodeBlended, ElectrodeBlendedSPM)) + and name == "particle" ): - init_len = len("Initial concentration ") - pybamm_name = ( - pybamm_name[:init_len] - + "in " - + domain.pre_name.lower() - + pybamm_name[init_len:] - ) - elif pybamm_name.startswith("Particle radius"): - pybamm_name = domain.short_pre_name + pybamm_name_lower - elif pybamm_name.startswith("OCP"): - pybamm_name = domain.pre_name + pybamm_name - elif pybamm_name.startswith("Cation transference number"): - pybamm_name = pybamm_name - elif domain.pre_name != "": - pybamm_name = domain.pre_name + pybamm_name_lower - - pybamm_dict[pybamm_name] = value + particle_instance = instance.particle + # Loop over phases + for i, phase_name in enumerate(particle_instance.keys()): + phase_instance = particle_instance[phase_name] + # Loop over fields in phase instance and add to pybamm dictionary + for name, field in phase_instance.__fields__.items(): + value = getattr(phase_instance, name) + pybamm_name = PHASE_NAMES[i] + _get_pybamm_name( + field.field_info.alias, domain + ) + value = process_float_function_table(value, name) + pybamm_dict[pybamm_name] = value + # Handle other fields, which correspond directly to parameters + else: + pybamm_name = _get_pybamm_name(field.field_info.alias, domain) + value = process_float_function_table(value, name) + pybamm_dict[pybamm_name] = value return pybamm_dict diff --git a/pybamm/parameters/electrical_parameters.py b/pybamm/parameters/electrical_parameters.py index 946c47f53b..1efa170a69 100644 --- a/pybamm/parameters/electrical_parameters.py +++ b/pybamm/parameters/electrical_parameters.py @@ -30,15 +30,11 @@ def _set_parameters(self): ) self.voltage_low_cut = pybamm.Parameter("Lower voltage cut-off [V]") self.voltage_high_cut = pybamm.Parameter("Upper voltage cut-off [V]") - self.ocp_soc_0_dimensional = pybamm.Parameter( - "Open-circuit voltage at 0% SOC [V]" - ) - self.ocp_soc_100_dimensional = pybamm.Parameter( - "Open-circuit voltage at 100% SOC [V]" - ) + self.ocp_soc_0 = pybamm.Parameter("Open-circuit voltage at 0% SOC [V]") + self.ocp_soc_100 = pybamm.Parameter("Open-circuit voltage at 100% SOC [V]") # Current as a function of time self.current_with_time = pybamm.FunctionParameter( - "Current function [A]", {"Time[s]": pybamm.t} + "Current function [A]", {"Time [s]": pybamm.t} ) self.current_density_with_time = self.current_with_time / ( self.n_electrodes_parallel * self.geo.A_cc diff --git a/pybamm/parameters/geometric_parameters.py b/pybamm/parameters/geometric_parameters.py index ecc52e30f1..0155bbe03c 100644 --- a/pybamm/parameters/geometric_parameters.py +++ b/pybamm/parameters/geometric_parameters.py @@ -77,6 +77,7 @@ def _set_parameters(self): if self.domain == "separator": self.L = pybamm.Parameter("Separator thickness [m]") self.b_e = pybamm.Parameter("Separator Bruggeman coefficient (electrolyte)") + self.tau_e = pybamm.Parameter("Separator tortuosity factor (electrolyte)") return Domain = self.domain.capitalize() @@ -98,6 +99,12 @@ def _set_parameters(self): self.b_s = pybamm.Parameter( f"{Domain} electrode Bruggeman coefficient (electrode)" ) + self.tau_e = pybamm.Parameter( + f"{Domain} electrode tortuosity factor (electrolyte)" + ) + self.tau_s = pybamm.Parameter( + f"{Domain} electrode tortuosity factor (electrode)" + ) class ParticleGeometricParameters(BaseParameters): diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py index 12196c4044..f5a76c6d48 100644 --- a/pybamm/parameters/lithium_ion_parameters.py +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -76,8 +76,8 @@ def _set_parameters(self): self.n_cells = self.elec.n_cells self.voltage_low_cut = self.elec.voltage_low_cut self.voltage_high_cut = self.elec.voltage_high_cut - self.ocp_soc_0_dimensional = self.elec.ocp_soc_0_dimensional - self.ocp_soc_100_dimensional = self.elec.ocp_soc_100_dimensional + self.ocp_soc_0 = self.elec.ocp_soc_0 + self.ocp_soc_100 = self.elec.ocp_soc_100 # Domain parameters for domain in self.domain_params.values(): @@ -91,18 +91,10 @@ def _set_parameters(self): ] ) - # Lithium plating parameters + # Required by lithium plating and lithium metal plating reactions self.V_bar_Li = pybamm.Parameter( "Lithium metal partial molar volume [m3.mol-1]" ) - self.c_Li_typ = pybamm.Parameter( - "Typical plated lithium concentration [mol.m-3]" - ) - self.c_plated_Li_0 = pybamm.Parameter( - "Initial plated lithium concentration [mol.m-3]" - ) - self.alpha_plating = pybamm.Parameter("Lithium plating transfer coefficient") - self.alpha_stripping = 1 - self.alpha_plating # Initial conditions # Note: the initial concentration in the electrodes can be set as a function @@ -188,33 +180,6 @@ def j0_Li_metal(self, c_e, c_Li, T): "Exchange-current density for lithium metal electrode [A.m-2]", inputs ) - def j0_stripping(self, c_e, c_Li, T): - """Dimensional exchange-current density for stripping [A.m-2]""" - inputs = { - "Electrolyte concentration [mol.m-3]": c_e, - "Plated lithium concentration [mol.m-3]": c_Li, - "Temperature [K]": T, - } - return pybamm.FunctionParameter( - "Exchange-current density for stripping [A.m-2]", inputs - ) - - def j0_plating(self, c_e, c_Li, T): - """Dimensional exchange-current density for plating [A.m-2]""" - inputs = { - "Electrolyte concentration [mol.m-3]": c_e, - "Plated lithium concentration [mol.m-3]": c_Li, - "Temperature [K]": T, - } - return pybamm.FunctionParameter( - "Exchange-current density for plating [A.m-2]", inputs - ) - - def dead_lithium_decay_rate(self, L_sei): - """Dimensional dead lithium decay rate [s-1]""" - inputs = {"Total SEI thickness [m]": L_sei} - return pybamm.FunctionParameter("Dead lithium decay rate [s-1]", inputs) - class DomainLithiumIonParameters(BaseParameters): def __init__(self, domain, main_param): @@ -243,6 +208,7 @@ def _set_parameters(self): # Parameters that appear in the separator self.b_e = self.geo.b_e + self.tau_e = self.geo.tau_e self.L = self.geo.L # Thermal @@ -300,6 +266,7 @@ def _set_parameters(self): # Tortuosity parameters self.b_s = self.geo.b_s + self.tau_s = self.geo.tau_s # Mechanical parameters self.nu = pybamm.Parameter(f"{Domain} electrode Poisson's ratio") @@ -449,11 +416,12 @@ def _set_parameters(self): ) self.L_inner_0 = pybamm.Parameter(f"{pref}Initial inner SEI thickness [m]") self.L_outer_0 = pybamm.Parameter(f"{pref}Initial outer SEI thickness [m]") - - # Dividing by 10000 makes initial condition effectively zero - # without triggering division by zero errors - self.L_inner_crack_0 = self.L_inner_0 / 10000 - self.L_outer_crack_0 = self.L_outer_0 / 10000 + self.L_inner_crack_0 = pybamm.Parameter( + f"{pref}Initial inner SEI on cracks thickness [m]" + ) + self.L_outer_crack_0 = pybamm.Parameter( + f"{pref}Initial outer SEI on cracks thickness [m]" + ) self.L_sei_0 = self.L_inner_0 + self.L_outer_0 self.E_sei = pybamm.Parameter(f"{pref}SEI growth activation energy [J.mol-1]") @@ -471,6 +439,18 @@ def _set_parameters(self): self.k_sei = pybamm.Parameter(f"{pref}SEI kinetic rate constant [m.s-1]") self.U_sei = pybamm.Parameter(f"{pref}SEI open-circuit potential [V]") + # Lithium plating parameters + self.c_Li_typ = pybamm.Parameter( + f"{pref}Typical plated lithium concentration [mol.m-3]" + ) + self.c_plated_Li_0 = pybamm.Parameter( + f"{pref}Initial plated lithium concentration [mol.m-3]" + ) + self.alpha_plating = pybamm.Parameter( + f"{pref}Lithium plating transfer coefficient" + ) + self.alpha_stripping = 1 - self.alpha_plating + if main.options.electrode_types[domain] == "planar": self.n_Li_init = pybamm.Scalar(0) self.Q_Li_init = pybamm.Scalar(0) @@ -519,7 +499,7 @@ def _set_parameters(self): self.U_init = pybamm.Parameter( f"{pref}Initial voltage in {domain} electrode [V]", ) - self.c_init = self.x(self.U_init) * self.c_max + self.c_init = self.x(self.U_init, main.T_init) * self.c_max else: self.c_init = pybamm.FunctionParameter( f"{pref}Initial concentration in {domain} electrode [mol.m-3]", @@ -535,6 +515,14 @@ def _set_parameters(self): eps_c_init_av = pybamm.xyz_average( self.epsilon_s * pybamm.r_average(self.c_init) ) + # if self.options['open-circuit potential'] == 'Plett': + self.hysteresis_decay = pybamm.Parameter( + f"{pref}{Domain} particle hysteresis decay rate" + ) + self.hysteresis_switch = pybamm.Parameter( + f"{pref}{Domain} particle hysteresis switching factor" + ) + self.h_init = pybamm.Scalar(0) if self.options["open-circuit potential"] != "MSMR": self.U_init = self.U(self.sto_init_av, main.T_init) @@ -570,7 +558,7 @@ def D(self, c_s, T, lithiation=None): "Temperature [K]": T, } return pybamm.FunctionParameter( - f"{self.phase_prefactor}{Domain} electrode {lithiation}" + f"{self.phase_prefactor}{Domain} particle {lithiation}" "diffusivity [m2.s-1]", inputs, ) @@ -601,8 +589,46 @@ def j0(self, c_e, c_s_surf, T, lithiation=None): inputs, ) + def j0_stripping(self, c_e, c_Li, T): + """Dimensional exchange-current density for stripping [A.m-2]""" + Domain = self.domain.capitalize() + inputs = { + f"{Domain} electrolyte concentration [mol.m-3]": c_e, + f"{Domain} plated lithium concentration [mol.m-3]": c_Li, + f"{Domain} temperature [K]": T, + } + return pybamm.FunctionParameter( + f"{self.phase_prefactor}Exchange-current density for stripping [A.m-2]", + inputs, + ) + + def j0_plating(self, c_e, c_Li, T): + """Dimensional exchange-current density for plating [A.m-2]""" + Domain = self.domain.capitalize() + inputs = { + f"{Domain} electrolyte concentration [mol.m-3]": c_e, + f"{Domain} plated lithium concentration [mol.m-3]": c_Li, + f"{Domain} temperature [K]": T, + } + return pybamm.FunctionParameter( + f"{self.phase_prefactor}Exchange-current density for plating [A.m-2]", + inputs, + ) + + def dead_lithium_decay_rate(self, L_sei): + """Dimensional dead lithium decay rate [s-1]""" + Domain = self.domain.capitalize() + inputs = {f"{Domain} total {self.phase_name}SEI thickness [m]": L_sei} + return pybamm.FunctionParameter( + f"{self.phase_prefactor}Dead lithium decay rate [s-1]", inputs + ) + def U(self, sto, T, lithiation=None): - """Dimensional open-circuit potential [V]""" + """ + Dimensional open-circuit potential [V], calculated as + U(x,T) = U_ref(x) + dUdT(x) * (T - T_ref). See the documentation for + dUdT for more details. + """ # bound stoichiometry between tol and 1-tol. Adding 1/sto + 1/(sto-1) later # will ensure that ocp goes to +- infinity if sto goes into that region # anyway @@ -617,12 +643,14 @@ def U(self, sto, T, lithiation=None): u_ref = pybamm.FunctionParameter( f"{self.phase_prefactor}{Domain} electrode {lithiation}OCP [V]", inputs ) + + dudt_func = self.dUdT(sto) + u_ref = u_ref + (T - self.main_param.T_ref) * dudt_func + # add a term to ensure that the OCP goes to infinity at 0 and -infinity at 1 # this will not affect the OCP for most values of sto # see #1435 - u_ref = u_ref + 1e-6 * (1 / sto + 1 / (sto - 1)) - dudt_func = self.dUdT(sto) - out = u_ref + (T - self.main_param.T_ref) * dudt_func + out = u_ref + 1e-6 * (1 / sto + 1 / (sto - 1)) if self.domain == "negative": out.print_name = r"U_\mathrm{n}(c^\mathrm{surf}_\mathrm{s,n}, T)" @@ -632,7 +660,14 @@ def U(self, sto, T, lithiation=None): def dUdT(self, sto): """ - Dimensional entropic change of the open-circuit potential [V.K-1] + Dimensional entropic change of the open-circuit potential [V.K-1]. + + Note: in the "classical" formulation, the open-circuit potential is defined + as U(x,T) = U_ref(x) + dUdT(x) * (T - T_ref). The user provides U_ref and + dUdT, and the model uses these to calculate U. dUdT is also used to calculate + the reversible heat generation term in the thermal model. However, in the + "MSMR" formulation, stoichiometry is explicitly defined as a function of U and + T, and dUdT is only used to calculate the reversible heat generation term. """ domain, Domain = self.domain_Domain inputs = { @@ -645,52 +680,54 @@ def dUdT(self, sto): inputs, ) - def X_j(self, index): + def X_j(self, T, index): "Available host sites indexed by reaction j" + inputs = {"Temperature [K]": T} domain = self.domain d = domain[0] - Xj = pybamm.Parameter(f"X_{d}_{index}") + Xj = pybamm.FunctionParameter(f"X_{d}_{index}", inputs) return Xj - def U0_j(self, index): + def U0_j(self, T, index): "Equilibrium potential indexed by reaction j" + inputs = {"Temperature [K]": T} domain = self.domain d = domain[0] - U0j = pybamm.Parameter(f"U0_{d}_{index}") + U0j = pybamm.FunctionParameter(f"U0_{d}_{index}", inputs) return U0j - def w_j(self, index): + def w_j(self, T, index): "Order parameter indexed by reaction j" + inputs = {"Temperature [K]": T} domain = self.domain d = domain[0] - wj = pybamm.Parameter(f"w_{d}_{index}") + wj = pybamm.FunctionParameter(f"w_{d}_{index}", inputs) return wj - def alpha_bv_j(self, index): + def alpha_bv_j(self, T, index): "Dimensional Butler-Volmer exchange-current density indexed by reaction j" + inputs = {"Temperature [K]": T} domain = self.domain d = domain[0] - alpha_bv_j = pybamm.Parameter(f"a_{d}_{index}") + alpha_bv_j = pybamm.FunctionParameter(f"a_{d}_{index}", inputs) return alpha_bv_j - def x_j(self, U, index): + def x_j(self, U, T, index): "Fractional occupancy of site j as a function of potential" - T = self.main_param.T_ref f = self.main_param.F / (self.main_param.R * T) - U0j = self.U0_j(index) - wj = self.w_j(index) - Xj = self.X_j(index) + U0j = self.U0_j(T, index) + wj = self.w_j(T, index) + Xj = self.X_j(T, index) # Equation 5, Baker et al 2018 xj = Xj / (1 + pybamm.exp(f * (U - U0j) / wj)) return xj - def dxdU_j(self, U, index): + def dxdU_j(self, U, T, index): "Derivative of fractional occupancy of site j as a function of potential [V-1]" - T = self.main_param.T_ref f = self.main_param.F / (self.main_param.R * T) - U0j = self.U0_j(index) - wj = self.w_j(index) - Xj = self.X_j(index) + U0j = self.U0_j(T, index) + wj = self.w_j(T, index) + Xj = self.X_j(T, index) e = pybamm.exp(f * (U - U0j) / wj) # Equation 25, Baker et al 2018 dxjdU = -(f / wj) * (Xj * e) / (1 + e) ** 2 @@ -704,13 +741,13 @@ def j0_j(self, c_e, U, T, index): tol = pybamm.settings.tolerances["j0__c_e"] c_e = pybamm.maximum(c_e, tol) c_e_ref = self.main_param.c_e_init - xj = self.x_j(U, index) + xj = self.x_j(U, T, index) # xj = pybamm.maximum(pybamm.minimum(xj, (1 - tol)), tol) f = self.main_param.F / (self.main_param.R * T) - wj = self.w_j(index) - self.X_j(index) - aj = self.alpha_bv_j(index) + wj = self.w_j(T, index) + self.X_j(T, index) + aj = self.alpha_bv_j(T, index) j0_ref_j = pybamm.FunctionParameter( f"j0_ref_{d}_{index}", {"Temperature [K]": T} ) @@ -728,21 +765,21 @@ def j0_j(self, c_e, U, T, index): j0_j = ( j0_ref_j * xj**wj - * pybamm.exp(f * (1 - aj) * (U - self.U0_j(index))) + * pybamm.exp(f * (1 - aj) * (U - self.U0_j(T, index))) * (c_e / c_e_ref) ** (1 - aj) ) return j0_j - def x(self, U): + def x(self, U, T): "Stoichiometry as a function of potential (for use with MSMR models)" N = int(self.options["number of MSMR reactions"]) # Equation 6, Baker et al 2018 x = 0 for i in range(N): - x += self.x_j(U, i) + x += self.x_j(U, T, i) return x - def dxdU(self, U): + def dxdU(self, U, T): """ Differential stoichiometry as a function of potential (for use with MSMR models) """ @@ -750,7 +787,7 @@ def dxdU(self, U): # Equation 25, Baker et al 2018 dxdU = 0 for i in range(N): - dxdU += self.dxdU_j(U, i) + dxdU += self.dxdU_j(U, T, i) return dxdU def t_change(self, sto): diff --git a/pybamm/parameters/parameter_sets.py b/pybamm/parameters/parameter_sets.py index 20c20de091..a3ddd0ed2e 100644 --- a/pybamm/parameters/parameter_sets.py +++ b/pybamm/parameters/parameter_sets.py @@ -3,6 +3,7 @@ import importlib.metadata import textwrap from collections.abc import Mapping +from typing import Callable class ParameterSets(Mapping): @@ -14,14 +15,13 @@ class ParameterSets(Mapping): -------- Listing available parameter sets: - .. doctest:: + >>> import pybamm >>> list(pybamm.parameter_sets) ['Ai2020', 'Chen2020', ...] Get the docstring for a parameter set: - .. doctest:: >>> print(pybamm.parameter_sets.get_docstring("Ai2020")) @@ -56,7 +56,7 @@ def __new__(cls): def __getitem__(self, key) -> dict: return self.__load_entry_point__(key)() - def __load_entry_point__(self, key) -> callable: + def __load_entry_point__(self, key) -> Callable: """Check that ``key`` is a registered ``pybamm_parameter_sets``, and return the entry point for the parameter set, loading it needed. """ @@ -88,10 +88,10 @@ def __getattribute__(self, name): # parameter set as before when passed to `ParameterValues` if name in self: msg = ( - "Parameter sets should be called directly by their name ({0}), " - "instead of via pybamm.parameter_sets (pybamm.parameter_sets.{0})." - ).format(name) - warnings.warn(msg, DeprecationWarning) + f"Parameter sets should be called directly by their name ({name}), " + f"instead of via pybamm.parameter_sets (pybamm.parameter_sets.{name})." + ) + warnings.warn(msg, DeprecationWarning, stacklevel=2) return name raise error diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py index 5dcb3c950a..57ebf65058 100644 --- a/pybamm/parameters/parameter_values.py +++ b/pybamm/parameters/parameter_values.py @@ -5,6 +5,7 @@ import pybamm import numbers from pprint import pformat +from warnings import warn from collections import defaultdict @@ -64,7 +65,10 @@ def __init__(self, values, chemistry=None): values.pop("chemistry", None) self.update(values, check_already_exists=False) else: - raise ValueError("Invalid Parameter Value") + valid_sets = "\n".join(pybamm.parameter_sets.keys()) + raise ValueError( + f"'{values}' is not a valid parameter set. Parameter set must be one of:\n{valid_sets}" + ) # Initialise empty _processed_symbols dict (for caching) self._processed_symbols = {} @@ -94,6 +98,7 @@ def create_from_bpx(filename, target_soc=1): raise ValueError("Target SOC should be between 0 and 1") from bpx import parse_bpx_file, get_electrode_concentrations + from bpx.schema import ElectrodeBlended, ElectrodeBlendedSPM from .bpx import _bpx_to_param_dict # parse bpx @@ -111,9 +116,25 @@ def create_from_bpx(filename, target_soc=1): # ahead with the low voltage limit. # get initial concentrations based on SOC - c_n_init, c_p_init = get_electrode_concentrations(target_soc, bpx) - pybamm_dict["Initial concentration in negative electrode [mol.m-3]"] = c_n_init - pybamm_dict["Initial concentration in positive electrode [mol.m-3]"] = c_p_init + # Note: we cannot set SOC for blended electrodes, + # see https://github.com/pybamm-team/PyBaMM/issues/2682 + bpx_neg = bpx.parameterisation.negative_electrode + bpx_pos = bpx.parameterisation.positive_electrode + if isinstance(bpx_neg, (ElectrodeBlended, ElectrodeBlendedSPM)) or isinstance( + bpx_pos, (ElectrodeBlended, ElectrodeBlendedSPM) + ): + pybamm.logger.warning( + "Initial concentrations cannot be set using stoichiometry limits for " + "blend electrodes. Please set the initial concentrations manually." + ) + else: + c_n_init, c_p_init = get_electrode_concentrations(target_soc, bpx) + pybamm_dict["Initial concentration in negative electrode [mol.m-3]"] = ( + c_n_init + ) + pybamm_dict["Initial concentration in positive electrode [mol.m-3]"] = ( + c_p_init + ) return pybamm.ParameterValues(pybamm_dict) @@ -134,7 +155,7 @@ def __getitem__(self, key): "density for the lithium plating reaction in a porous negative " "electrode. To avoid this error, change your parameter file to use " "the new name." - ) + ) from err else: raise err @@ -212,7 +233,7 @@ def update(self, values, check_conflict=False, check_already_exists=True, path=" if not isinstance(values, dict): values = values._dict_items # check parameter values - self.check_parameter_values(values) + values = self.check_parameter_values(values) # update for name, value in values.items(): # check for conflicts @@ -234,7 +255,7 @@ def update(self, values, check_conflict=False, check_already_exists=True, path=" + f"have a default value. ({err.args[0]}). If you are " + "sure you want to update this parameter, use " + "param.update({{name: value}}, check_already_exists=False)" - ) + ) from err # if no conflicts, update if isinstance(value, str): if ( @@ -275,6 +296,7 @@ def set_initial_stoichiometry_half_cell( known_value="cyclable lithium capacity", inplace=True, options=None, + inputs=None, ): """ Set the initial stoichiometry of the working electrode, based on the initial @@ -282,7 +304,12 @@ def set_initial_stoichiometry_half_cell( """ param = param or pybamm.LithiumIonParameters(options) x = pybamm.lithium_ion.get_initial_stoichiometry_half_cell( - initial_value, self, param=param, known_value=known_value, options=options + initial_value, + self, + param=param, + known_value=known_value, + options=options, + inputs=inputs, ) if inplace: parameter_values = self @@ -307,6 +334,8 @@ def set_initial_stoichiometries( known_value="cyclable lithium capacity", inplace=True, options=None, + inputs=None, + tol=1e-6, ): """ Set the initial stoichiometry of each electrode, based on the initial @@ -314,7 +343,13 @@ def set_initial_stoichiometries( """ param = param or pybamm.LithiumIonParameters(options) x, y = pybamm.lithium_ion.get_initial_stoichiometries( - initial_value, self, param=param, known_value=known_value, options=options + initial_value, + self, + param=param, + known_value=known_value, + options=options, + tol=tol, + inputs=inputs, ) if inplace: parameter_values = self @@ -358,8 +393,9 @@ def set_initial_ocps( ) return parameter_values - def check_parameter_values(self, values): - for param in values: + @staticmethod + def check_parameter_values(values): + for param in list(values.keys()): if "propotional term" in param: raise ValueError( f"The parameter '{param}' has been renamed to " @@ -371,6 +407,16 @@ def check_parameter_values(self, values): raise ValueError( f"parameter '{param}' has been renamed to " "'Thermodynamic factor'" ) + if "electrode diffusivity" in param: + new_param = param.replace("electrode", "particle") + warn( + f"The parameter '{param}' has been renamed to '{new_param}'", + DeprecationWarning, + stacklevel=2, + ) + values[new_param] = values.get(param) + + return values def process_model(self, unprocessed_model, inplace=True): """Assign parameter values to a model. @@ -519,7 +565,7 @@ def process_boundary_conditions(self, model): pass # do raise error otherwise (e.g. can't process symbol) else: - raise KeyError(err) + raise err return new_boundary_conditions @@ -545,11 +591,11 @@ def process_and_check(sym): for spatial_variable, spatial_limits in geometry[domain].items(): # process tab information if using 1 or 2D current collectors if spatial_variable == "tabs": - for tab, position_size in spatial_limits.items(): - for position_size, sym in position_size.items(): - geometry[domain]["tabs"][tab][ - position_size - ] = process_and_check(sym) + for tab, position_info in spatial_limits.items(): + for position_size, sym in position_info.items(): + geometry[domain]["tabs"][tab][position_size] = ( + process_and_check(sym) + ) else: for lim, sym in spatial_limits.items(): geometry[domain][spatial_variable][lim] = process_and_check(sym) @@ -681,21 +727,10 @@ def _process_symbol(self, symbol): # Process again just to be sure return self.process_symbol(function_out) - elif isinstance(symbol, pybamm.BinaryOperator): - # process children - new_left = self.process_symbol(symbol.left) - new_right = self.process_symbol(symbol.right) - # make new symbol, ensure domain remains the same - new_symbol = symbol._binary_new_copy(new_left, new_right) - new_symbol.copy_domains(symbol) - return new_symbol - # Unary operators elif isinstance(symbol, pybamm.UnaryOperator): new_child = self.process_symbol(symbol.child) - new_symbol = symbol._unary_new_copy(new_child) - # ensure domain remains the same - new_symbol.copy_domains(symbol) + new_symbol = symbol.create_copy(new_children=[new_child]) # x_average can sometimes create a new symbol with electrode thickness # parameters, so we process again to make sure these parameters are set if isinstance(symbol, pybamm.XAverage) and not isinstance( @@ -716,15 +751,14 @@ def _process_symbol(self, symbol): new_symbol.position = new_symbol_position return new_symbol - # Functions - elif isinstance(symbol, pybamm.Function): - new_children = [self.process_symbol(child) for child in symbol.children] - return symbol._function_new_copy(new_children) - - # Concatenations - elif isinstance(symbol, pybamm.Concatenation): + # Functions, BinaryOperators & Concatenations + elif ( + isinstance(symbol, pybamm.Function) + or isinstance(symbol, pybamm.Concatenation) + or isinstance(symbol, pybamm.BinaryOperator) + ): new_children = [self.process_symbol(child) for child in symbol.children] - return symbol._concatenation_new_copy(new_children) + return symbol.create_copy(new_children) # Variables: update scale elif isinstance(symbol, pybamm.Variable): @@ -746,7 +780,7 @@ def _process_symbol(self, symbol): # Backup option: return the object return symbol - def evaluate(self, symbol): + def evaluate(self, symbol, inputs=None): """ Process and evaluate a symbol. @@ -764,7 +798,15 @@ def evaluate(self, symbol): if processed_symbol.is_constant(): return processed_symbol.evaluate() else: - raise ValueError("symbol must evaluate to a constant scalar or array") + # In the case that the only issue is an input parameter contained in inputs, + # go ahead and try and evaluate it with the inputs. If it doesn't work, raise + # the value error. + try: + return processed_symbol.evaluate(inputs=inputs) + except Exception as exc: + raise ValueError( + "symbol must evaluate to a constant scalar or array" + ) from exc def _ipython_key_completions_(self): return list(self._dict_items.keys()) diff --git a/pybamm/parameters_cli.py b/pybamm/parameters_cli.py deleted file mode 100644 index e3d4a273b8..0000000000 --- a/pybamm/parameters_cli.py +++ /dev/null @@ -1,19 +0,0 @@ -def raise_error(): - raise NotImplementedError( - "parameters cli has been deprecated. " - "Parameters should now be defined via python files (see " - "https://github.com/pybamm-team/PyBaMM/tree/develop/pybamm/input/parameters/lithium_ion/Ai2020.py" - " for example)" - ) - - -def add_parameter(arguments=None): - raise_error() - - -def remove_parameter(arguments=None): - raise_error() - - -def edit_parameter(arguments=None): - raise_error() diff --git a/pybamm/plotting/__init__.py b/pybamm/plotting/__init__.py index e69de29bb2..80a7b78ace 100644 --- a/pybamm/plotting/__init__.py +++ b/pybamm/plotting/__init__.py @@ -0,0 +1,2 @@ +__all__ = ['dynamic_plot', 'plot', 'plot2D', 'plot_summary_variables', + 'plot_thermal_components', 'plot_voltage_components', 'quick_plot'] diff --git a/pybamm/plotting/dynamic_plot.py b/pybamm/plotting/dynamic_plot.py index eb4d468be9..4cde0d3972 100644 --- a/pybamm/plotting/dynamic_plot.py +++ b/pybamm/plotting/dynamic_plot.py @@ -8,7 +8,7 @@ def dynamic_plot(*args, **kwargs): """ Creates a :class:`pybamm.QuickPlot` object (with arguments 'args' and keyword arguments 'kwargs') and then calls :meth:`pybamm.QuickPlot.dynamic_plot`. - The key-word argument 'testing' is passed to the 'dynamic_plot' method, not the + The key-word argument 'show_plot' is passed to the 'dynamic_plot' method, not the `QuickPlot` class. Returns @@ -16,7 +16,7 @@ def dynamic_plot(*args, **kwargs): plot : :class:`pybamm.QuickPlot` The 'QuickPlot' object that was created """ - kwargs_for_class = {k: v for k, v in kwargs.items() if k != "testing"} + kwargs_for_class = {k: v for k, v in kwargs.items() if k != "show_plot"} plot = pybamm.QuickPlot(*args, **kwargs_for_class) - plot.dynamic_plot(kwargs.get("testing", False)) + plot.dynamic_plot(kwargs.get("show_plot", True)) return plot diff --git a/pybamm/plotting/plot.py b/pybamm/plotting/plot.py index 88c8dfe442..4037ab8fbf 100644 --- a/pybamm/plotting/plot.py +++ b/pybamm/plotting/plot.py @@ -3,10 +3,10 @@ # import pybamm from .quick_plot import ax_min, ax_max -from pybamm.util import have_optional_dependency +from pybamm.util import import_optional_dependency -def plot(x, y, ax=None, testing=False, **kwargs): +def plot(x, y, ax=None, show_plot=True, **kwargs): """ Generate a simple 1D plot. Calls `matplotlib.pyplot.plot` with keyword arguments 'kwargs'. For a list of 'kwargs' see the @@ -20,13 +20,14 @@ def plot(x, y, ax=None, testing=False, **kwargs): The array to plot on the y axis ax : matplotlib Axis, optional The axis on which to put the plot. If None, a new figure and axis is created. - testing : bool, optional - Whether to actually make the plot (turned off for unit tests) + show_plot : bool, optional + Whether to show the plots. Default is True. Set to False if you want to + only display the plot after plt.show() has been called. kwargs Keyword arguments, passed to plt.plot """ - plt = have_optional_dependency("matplotlib.pyplot") + plt = import_optional_dependency("matplotlib.pyplot") if not isinstance(x, pybamm.Array): raise TypeError("x must be 'pybamm.Array'") @@ -34,14 +35,14 @@ def plot(x, y, ax=None, testing=False, **kwargs): raise TypeError("y must be 'pybamm.Array'") if ax is not None: - testing = True + show_plot = False else: _, ax = plt.subplots() ax.plot(x.entries, y.entries, **kwargs) ax.set_ylim([ax_min(y.entries), ax_max(y.entries)]) - if not testing: # pragma: no cover + if show_plot: # pragma: no cover plt.show() return ax diff --git a/pybamm/plotting/plot2D.py b/pybamm/plotting/plot2D.py index 3a69bab803..7d1f3c6bae 100644 --- a/pybamm/plotting/plot2D.py +++ b/pybamm/plotting/plot2D.py @@ -3,10 +3,10 @@ # import pybamm from .quick_plot import ax_min, ax_max -from pybamm.util import have_optional_dependency +from pybamm.util import import_optional_dependency -def plot2D(x, y, z, ax=None, testing=False, **kwargs): +def plot2D(x, y, z, ax=None, show_plot=True, **kwargs): """ Generate a simple 2D plot. Calls `matplotlib.pyplot.contourf` with keyword arguments 'kwargs'. For a list of 'kwargs' see the @@ -22,11 +22,12 @@ def plot2D(x, y, z, ax=None, testing=False, **kwargs): The array to plot on the z axis. Is of shape (M, N) ax : matplotlib Axis, optional The axis on which to put the plot. If None, a new figure and axis is created. - testing : bool, optional - Whether to actually make the plot (turned off for unit tests) + show_plot : bool, optional + Whether to show the plots. Default is True. Set to False if you want to + only display the plot after plt.show() has been called. """ - plt = have_optional_dependency("matplotlib.pyplot") + plt = import_optional_dependency("matplotlib.pyplot") if not isinstance(x, pybamm.Array): raise TypeError("x must be 'pybamm.Array'") @@ -36,7 +37,7 @@ def plot2D(x, y, z, ax=None, testing=False, **kwargs): raise TypeError("z must be 'pybamm.Array'") if ax is not None: - testing = True + show_plot = False else: _, ax = plt.subplots() @@ -58,7 +59,7 @@ def plot2D(x, y, z, ax=None, testing=False, **kwargs): ) plt.colorbar(plot, ax=ax) - if not testing: # pragma: no cover + if show_plot: # pragma: no cover plt.show() return ax diff --git a/pybamm/plotting/plot_summary_variables.py b/pybamm/plotting/plot_summary_variables.py index e50f38fddf..bd4db0ee6c 100644 --- a/pybamm/plotting/plot_summary_variables.py +++ b/pybamm/plotting/plot_summary_variables.py @@ -3,11 +3,11 @@ # import numpy as np import pybamm -from pybamm.util import have_optional_dependency +from pybamm.util import import_optional_dependency def plot_summary_variables( - solutions, output_variables=None, labels=None, testing=False, **kwargs_fig + solutions, output_variables=None, labels=None, show_plot=True, **kwargs_fig ): """ Generate a plot showing/comparing the summary variables. @@ -20,13 +20,14 @@ def plot_summary_variables( A list of variables to plot automatically. If None, the default ones are used. labels: list (optional) A list of labels to be added to the legend. No labels are added by default. - testing : bool (optional) - Whether to actually make the plot (turned off for unit tests). + show_plot : bool, optional + Whether to show the plots. Default is True. Set to False if you want to + only display the plot after plt.show() has been called. kwargs_fig Keyword arguments, passed to plt.subplots. """ - plt = have_optional_dependency("matplotlib.pyplot") + plt = import_optional_dependency("matplotlib.pyplot") if isinstance(solutions, pybamm.Solution): solutions = [solutions] @@ -74,7 +75,7 @@ def plot_summary_variables( # add labels in legend if labels is not None: # pragma: no cover fig.legend(labels, loc="lower right") - if not testing: # pragma: no cover + if show_plot: # pragma: no cover plt.show() return axes diff --git a/pybamm/plotting/plot_thermal_components.py b/pybamm/plotting/plot_thermal_components.py new file mode 100644 index 0000000000..8cb99a454d --- /dev/null +++ b/pybamm/plotting/plot_thermal_components.py @@ -0,0 +1,118 @@ +# +# Method for plotting voltage components +# + +from pybamm.util import import_optional_dependency +from pybamm.simulation import Simulation +from pybamm.solvers.solution import Solution +from scipy.integrate import cumulative_trapezoid + + +def plot_thermal_components( + input_data, + ax=None, + show_legend=True, + split_by_electrode=False, + show_plot=True, + **kwargs_fill, +): + """ + Generate a plot showing the component overpotentials that make up the voltage + + Parameters + ---------- + input_data : :class:`pybamm.Solution` or :class:`pybamm.Simulation` + Solution or Simulation object from which to extract voltage components. + ax : matplotlib Axis, optional + The axis on which to put the plot. If None, a new figure and axis is created. + show_legend : bool, optional + Whether to display the legend. Default is True + show_plot : bool, optional + Whether to show the plots. Default is True. Set to False if you want to + only display the plot after plt.show() has been called. + kwargs_fill + Keyword arguments, passed to ax.fill_between + + """ + # Check if the input is a Simulation and extract Solution + if isinstance(input_data, Simulation): + solution = input_data.solution + elif isinstance(input_data, Solution): + solution = input_data + plt = import_optional_dependency("matplotlib.pyplot") + + # Set a default value for alpha, the opacity + kwargs_fill = {"alpha": 0.6, **kwargs_fill} + + if ax is not None: + fig = None + show_plot = False + else: + fig, ax = plt.subplots(1, 2, figsize=(12, 4)) + + time_s = solution["Time [s]"].entries + time_h = time_s / 3600 + volume = solution["Cell thermal volume [m3]"].entries + + heating_sources = [ + "Lumped total cooling", + "Ohmic heating", + "Irreversible electrochemical heating", + "Reversible heating", + ] + try: + heats = { + name: solution[name + " [W]"].entries / volume for name in heating_sources + } + except KeyError as err: + raise NotImplementedError( + "plot_thermal_components is only implemented for lumped models" + ) from err + + cumul_heats = { + name: cumulative_trapezoid(heat, time_s, initial=0) + for name, heat in heats.items() + } + + # Plot + # Initialise + total_heat = 0 + bottom_heat = heats["Lumped total cooling"] + total_cumul_heat = 0 + bottom_cumul_heat = cumul_heats["Lumped total cooling"] + # Plot components + for name in heating_sources: + top_heat = bottom_heat + abs(heats[name]) + ax[0].fill_between(time_h, bottom_heat, top_heat, **kwargs_fill, label=name) + bottom_heat = top_heat + total_heat += heats[name] + + top_cumul_heat = bottom_cumul_heat + abs(cumul_heats[name]) + ax[1].fill_between( + time_h, bottom_cumul_heat, top_cumul_heat, **kwargs_fill, label=name + ) + bottom_cumul_heat = top_cumul_heat + total_cumul_heat += cumul_heats[name] + + ax[0].plot(time_h, total_heat, "k--") + ax[1].plot(time_h, total_cumul_heat, "k--", label="Total") + + if show_legend: + leg = ax[1].legend(loc="center left", bbox_to_anchor=(1.05, 0.5), frameon=True) + leg.get_frame().set_edgecolor("k") + + # Labels + for a in ax: + a.set_xlabel("Time [h]") + a.set_xlim([time_h[0], time_h[-1]]) + + ax[0].set_title("Heat generation [W/m$^3$]") + ax[1].set_title("Cumulative heat generation [J/m$^3$]") + + if fig is not None: + fig.tight_layout() + + if show_plot: # pragma: no cover + plt.show() + + return fig, ax diff --git a/pybamm/plotting/plot_voltage_components.py b/pybamm/plotting/plot_voltage_components.py index ef95f7016f..db0bb908a0 100644 --- a/pybamm/plotting/plot_voltage_components.py +++ b/pybamm/plotting/plot_voltage_components.py @@ -3,15 +3,17 @@ # import numpy as np -from pybamm.util import have_optional_dependency +from pybamm.util import import_optional_dependency +from pybamm.simulation import Simulation +from pybamm.solvers.solution import Solution def plot_voltage_components( - solution, + input_data, ax=None, show_legend=True, split_by_electrode=False, - testing=False, + show_plot=True, **kwargs_fill, ): """ @@ -19,8 +21,8 @@ def plot_voltage_components( Parameters ---------- - solution : :class:`pybamm.Solution` - Solution object from which to extract voltage components + input_data : :class:`pybamm.Solution` or :class:`pybamm.Simulation` + Solution or Simulation object from which to extract voltage components. ax : matplotlib Axis, optional The axis on which to put the plot. If None, a new figure and axis is created. show_legend : bool, optional @@ -28,20 +30,25 @@ def plot_voltage_components( split_by_electrode : bool, optional Whether to show the overpotentials for the negative and positive electrodes separately. Default is False. - testing : bool, optional - Whether to actually make the plot (turned off for unit tests) + show_plot : bool, optional + Whether to show the plots. Default is True. Set to False if you want to + only display the plot after plt.show() has been called. kwargs_fill - Keyword arguments, passed to ax.fill_between - + Keyword arguments: :obj:`matplotlib.axes.Axes.fill_between` """ - plt = have_optional_dependency("matplotlib.pyplot") + # Check if the input is a Simulation and extract Solution + if isinstance(input_data, Simulation): + solution = input_data.solution + elif isinstance(input_data, Solution): + solution = input_data + plt = import_optional_dependency("matplotlib.pyplot") # Set a default value for alpha, the opacity kwargs_fill = {"alpha": 0.6, **kwargs_fill} if ax is not None: fig = None - testing = True + show_plot = False else: fig, ax = plt.subplots(figsize=(8, 4)) @@ -87,7 +94,7 @@ def plot_voltage_components( time = solution["Time [h]"].entries if split_by_electrode is False: ocv = solution["Battery open-circuit voltage [V]"] - initial_ocv = ocv(0) + initial_ocv = ocv(time[0]) ocv = ocv.entries ax.fill_between( time, ocv, initial_ocv, **kwargs_fill, label="Open-circuit voltage" @@ -95,8 +102,8 @@ def plot_voltage_components( else: ocp_n = solution["Battery negative electrode bulk open-circuit potential [V]"] ocp_p = solution["Battery positive electrode bulk open-circuit potential [V]"] - initial_ocp_n = ocp_n(0) - initial_ocp_p = ocp_p(0) + initial_ocp_n = ocp_n(time[0]) + initial_ocp_p = ocp_p(time[0]) initial_ocv = initial_ocp_p - initial_ocp_n delta_ocp_n = ocp_n.entries - initial_ocp_n delta_ocp_p = ocp_p.entries - initial_ocp_p @@ -144,7 +151,7 @@ def plot_voltage_components( ) ax.set_ylim([y_min, y_max]) - if not testing: # pragma: no cover + if show_plot: # pragma: no cover plt.show() return fig, ax diff --git a/pybamm/plotting/quick_plot.py b/pybamm/plotting/quick_plot.py index 9b082fd6d4..39dc974f9b 100644 --- a/pybamm/plotting/quick_plot.py +++ b/pybamm/plotting/quick_plot.py @@ -5,11 +5,10 @@ import numpy as np import pybamm from collections import defaultdict -from pybamm.util import have_optional_dependency +from pybamm.util import import_optional_dependency class LoopList(list): - """A list which loops over itself when accessing an index so that it never runs out""" @@ -47,7 +46,7 @@ def split_long_string(title, max_words=None): def close_plots(): """Close all open figures""" - plt = have_optional_dependency("matplotlib.pyplot") + plt = import_optional_dependency("matplotlib.pyplot") plt.close("all") @@ -117,9 +116,7 @@ def __init__( else: if len(labels) != len(models): raise ValueError( - "labels '{}' have different length to models '{}'".format( - labels, [model.name for model in models] - ) + f"labels '{labels}' have different length to models '{[model.name for model in models]}'" ) self.labels = labels @@ -163,7 +160,7 @@ def __init__( self.spatial_unit = "mm" elif spatial_unit == "um": # micrometers self.spatial_factor = 1e6 - self.spatial_unit = "$\mu$m" + self.spatial_unit = r"$\mu$m" else: raise ValueError(f"spatial unit '{spatial_unit}' not recognized") @@ -225,10 +222,10 @@ def __init__( except KeyError: # if variable_tuple is not provided, default to "fixed" self.variable_limits[variable_tuple] = "fixed" - except TypeError: + except TypeError as error: raise TypeError( "variable_limits must be 'fixed', 'tight', or a dict" - ) + ) from error self.set_output_variables(output_variable_tuples, solutions) self.reset_axis() @@ -298,18 +295,13 @@ def set_output_variables(self, output_variables, solutions): if variable.domain != domain: raise ValueError( "Mismatching variable domains. " - "'{}' has domain '{}', but '{}' has domain '{}'".format( - variable_tuple[0], - domain, - variable_tuple[idx], - variable.domain, - ) + f"'{variable_tuple[0]}' has domain '{domain}', but '{variable_tuple[idx]}' has domain '{variable.domain}'" ) self.spatial_variable_dict[variable_tuple] = {} # Set the x variable (i.e. "x" or "r" for any one-dimensional variables) if first_variable.dimensions == 1: - (spatial_var_name, spatial_var_value) = self.get_spatial_var( + (spatial_var_name, spatial_var_value) = self._get_spatial_var( variable_tuple, first_variable, "first" ) self.spatial_variable_dict[variable_tuple] = { @@ -332,11 +324,11 @@ def set_output_variables(self, output_variables, solutions): ( first_spatial_var_name, first_spatial_var_value, - ) = self.get_spatial_var(variable_tuple, first_variable, "first") + ) = self._get_spatial_var(variable_tuple, first_variable, "first") ( second_spatial_var_name, second_spatial_var_value, - ) = self.get_spatial_var(variable_tuple, first_variable, "second") + ) = self._get_spatial_var(variable_tuple, first_variable, "second") self.spatial_variable_dict[variable_tuple] = { first_spatial_var_name: first_spatial_var_value, second_spatial_var_name: second_spatial_var_value, @@ -368,7 +360,7 @@ def set_output_variables(self, output_variables, solutions): self.variables[variable_tuple] = variables self.subplot_positions[variable_tuple] = (self.n_rows, self.n_cols, k + 1) - def get_spatial_var(self, key, variable, dimension): + def _get_spatial_var(self, key, variable, dimension): """Return the appropriate spatial variable(s)""" # Extract name and value @@ -472,12 +464,15 @@ def plot(self, t, dynamic=False): ---------- t : float Dimensional time (in 'time_units') at which to plot. + dynamic : bool, optional + Determine whether to allocate space for a slider at the bottom of the plot when generating a dynamic plot. + If True, creates a dynamic plot with a slider. """ - plt = have_optional_dependency("matplotlib.pyplot") - gridspec = have_optional_dependency("matplotlib.gridspec") - cm = have_optional_dependency("matplotlib", "cm") - colors = have_optional_dependency("matplotlib", "colors") + plt = import_optional_dependency("matplotlib.pyplot") + gridspec = import_optional_dependency("matplotlib.gridspec") + cm = import_optional_dependency("matplotlib", "cm") + colors = import_optional_dependency("matplotlib", "colors") t_in_seconds = t * self.time_scaling_factor self.fig = plt.figure(figsize=self.figsize) @@ -649,17 +644,18 @@ def plot(self, t, dynamic=False): bottom = max(legend_top, slider_top) self.gridspec.tight_layout(self.fig, rect=[0, bottom, 1, 1]) - def dynamic_plot(self, testing=False, step=None): + def dynamic_plot(self, show_plot=True, step=None): """ Generate a dynamic plot with a slider to control the time. Parameters ---------- - step : float + step : float, optional For notebook mode, size of steps to allow in the slider. Defaults to 1/100th of the total time. - testing : bool - Whether to actually make the plot (turned off for unit tests) + show_plot : bool, optional + Whether to show the plots. Default is True. Set to False if you want to + only display the plot after plt.show() has been called. """ if pybamm.is_notebook(): # pragma: no cover @@ -674,8 +670,8 @@ def dynamic_plot(self, testing=False, step=None): continuous_update=False, ) else: - plt = have_optional_dependency("matplotlib.pyplot") - Slider = have_optional_dependency("matplotlib.widgets", "Slider") + plt = import_optional_dependency("matplotlib.pyplot") + Slider = import_optional_dependency("matplotlib.widgets", "Slider") # create an initial plot at time self.min_t self.plot(self.min_t, dynamic=True) @@ -692,7 +688,7 @@ def dynamic_plot(self, testing=False, step=None): ) self.slider.on_changed(self.slider_update) - if not testing: # pragma: no cover + if show_plot: # pragma: no cover plt.show() def slider_update(self, t): @@ -771,16 +767,16 @@ def create_gif(self, number_of_images=80, duration=0.1, output_filename="plot.gi Parameters ---------- - number_of_images : int (optional) + number_of_images : int, optional Number of images/plots to be compiled for a GIF. - duration : float (optional) + duration : float, optional Duration of visibility of a single image/plot in the created GIF. - output_filename : str (optional) + output_filename : str, optional Name of the generated GIF file. """ - imageio = have_optional_dependency("imageio.v2") - plt = have_optional_dependency("matplotlib.pyplot") + imageio = import_optional_dependency("imageio.v2") + plt = import_optional_dependency("matplotlib.pyplot") # time stamps at which the images/plots will be created time_array = np.linspace(self.min_t, self.max_t, num=number_of_images) diff --git a/pybamm/pybamm_data.py b/pybamm/pybamm_data.py new file mode 100644 index 0000000000..657e13c020 --- /dev/null +++ b/pybamm/pybamm_data.py @@ -0,0 +1,146 @@ +import pooch +import pathlib + + +class DataLoader: + """ + Data Loader class for downloading and loading data files upstream at https://github.com/pybamm-team/pybamm-data/ + + The following files are listed in the registry - + + COMSOL Results + --------------- + + :footcite:t:`Andersson2019` + :footcite:t:`Doyle1993` + :footcite:t:`Harris2020` + :footcite:t:`Marquis2019` + :footcite:t:`Marquis2020` + + - comsol_01C.json + - comsol_05C.json + - comsol_1C.json + - comsol_1plus1D_3C.json + - comsol_2C.json + - comsol_3C.json + + Kokam SLPB 75106100 discharge data from Ecker et al (2015) + ---------------------------------------------------------- + + :footcite:t:`Ecker2015i` + :footcite:t:`Ecker2015ii` + + - Ecker_1C.csv + - Ecker_5C.csv + + Enertech cells - discharge results for beginning of life + -------------------------------------------------------- + + :footcite:t:`Andersson2019` + :footcite:t:`Doyle1993` + :footcite:t:`Harris2020` + :footcite:t:`Marquis2019` + :footcite:t:`Ai2019` + :footcite:t:`Deshpande2012` + :footcite:t:`Timms2021` + + - 0.1C_discharge_U.txt + - 0.1C_discharge_displacement.txt + - 0.5C_discharge_T.txt + - 0.5C_discharge_U.txt + - 0.5C_discharge_displacement.txt + - 1C_discharge_T.txt + - 1C_discharge_U.txt + - 1C_discharge_displacement.txt + - 2C_discharge_T.txt + - 2C_discharge_U.txt + - 2C_discharge_displacement.txt + - stn_2C.txt + - stp_2C.txt + + + Drive cycles + ------------ + + :footcite:t:`Andersson2019` + :footcite:t:`Doyle1993` + :footcite:t:`Harris2020` + :footcite:t:`Marquis2019` + :footcite:t:`Marquis2020` + + - UDDS.csv + - US06.csv + - WLTC.csv + - car_current.csv + + + """ + + def __init__(self): + """ + Create a pooch registry with the following data files available upstream at https://github.com/pybamm-team/pybamm-data/ + """ + self.version = "v1.0.0" # Version of pybamm-data release + self.path = pooch.os_cache("pybamm") + self.files = { + # COMSOL results + "comsol_01C.json": "sha256:bc5136fe961e269453bdc31fcaa97376d6f8c347d570fd30ce4b7660c68ae22c", + "comsol_05C.json": "sha256:3b044135ad88bdb88959304a33fe42b654d5ef7ef79d1271dd909cec55b257fb", + "comsol_1C.json": "sha256:d45e3ab482c497c37ebbc68898da22bab0b0263992d8f2302502028bfd5ba0e9", + "comsol_1plus1D_3C.json": "sha256:cdd5759202f9c7887d2ea6032f82212f2ca89297191fe5282b8812e1a09b1e1f", + "comsol_2C.json": "sha256:15c2637f54bf1639621c58795db859cb08611c8182b7b20ade10e4c3e2839a5b", + "comsol_3C.json": "sha256:11d5afccb70be85d4ac7e61d413c6e0f5e318e1635b1347c9a3c6784119711e6", + # Kokam SLPB 75106100 discharge data from Ecker et al (2015) + "Ecker_1C.csv": "sha256:428dc5113a6430492f430fb9e895f67d3e20f5643dc49a1cc0a922b92a5a8e01", + "Ecker_5C.csv": "sha256:a89f8bf6e305b2a4195e1fae5e803277a40ed7557d263ef726f621803dcbb495", + # Enertech cells - discharge results for beginning of life + "0.1C_discharge_U.txt": "sha256:7b9fcd137441eea4ab686faee8d57fe242c5544400939ef358ccd99c63c9579d", + "0.1C_discharge_displacement.txt": "sha256:f1329731ead5a82a2be9851cf80e4c6d68dd0774e07aee5361e2af3ab420d7be", + "0.5C_discharge_T.txt": "sha256:2140b2f6bd698135d09a25b1f04c271d35a3a02999ace118b10389e01defa2ae", + "0.5C_discharge_U.txt": "sha256:9ed8368b2c6149d2a69218e7df6aaade2511c9f7f6fc7932cda153d9a3a10f39", + "0.5C_discharge_displacement.txt": "sha256:8098565ff99bc938864797b402f483c1c64a583d6db85d086f39ab0e7b638dd1", + "1C_discharge_T.txt": "sha256:97308dfd7f7dd6c434e30f6c00fb6707c43c963855bb0800e0336809d5cc3756", + "1C_discharge_U.txt": "sha256:8fc19de45172215d65c56522c224e6fc700ee443db236b814238a829b7a14c3a", + "1C_discharge_displacement.txt": "sha256:c2e8617ac48a20921da1b40bbebac479a0a143edf16b12b2e1ff9aaaf1a32ff4", + "2C_discharge_T.txt": "sha256:4bd688fb7653539701fe3df61857474b4d54e8b142c84fdc4c8b92b9573fa5d0", + "2C_discharge_U.txt": "sha256:7b3c24b5e6df377075002abc2f62bab7c88b27d826812ba5a4c8385a1a12e723", + "2C_discharge_displacement.txt": "sha256:2b11513d80827c762325c819a084b87b3a239af7d112f234c9871481760a0013", + "stn_2C.txt": "sha256:bb2f90ccfd2cd86ad589287caae13470e554df2f4f47f0f583a5a7e3e6bd9d4c", + "stp_2C.txt": "sha256:6fe73b3a18e5fcfb95151dfd7d34c3cbe929792631447ed3ec88c047c9778223", + # Drive cycles + "UDDS.csv": "sha256:9fe6558c17aad3cc08109186923aeb7459cd3097a381c44e854bf22dd12a5a2a", + "US06.csv": "sha256:5909eb2ec7983fae86a050ff3b35a2041d0ab698710a6b0f95d5816e348077ba", + "WLTC.csv": "sha256:bb2f95018a44ac1425cb9c787c34721192af502c7385f1358f28e4f75df11fd8", + "car_current.csv": "sha256:4305b91b9df073cb048c25dd3fae725e06a94fe200e322e5c08db290d6799e36", + } + self.registry = pooch.create( + path=self.path, + base_url=f"https://github.com/pybamm-team/pybamm-data/releases/download/{self.version}", + version=self.version, + registry=self.files, + ) + + def get_data(self, filename: str): + """ + Fetches the data file from upstream and stores it in the local cache directory under pybamm directory. + + Parameters + ---------- + filename : str + Name of the data file to be fetched from the registry. + Returns + ------- + pathlib.PurePath + """ + self.registry.fetch(filename) + return pathlib.Path(f"{self.path}/{self.version}/{filename}") + + def show_registry(self): + """ + Prints the name of all the files present in the registry. + + Returns + ------- + list + """ + return list(self.files.keys()) diff --git a/pybamm/simulation.py b/pybamm/simulation.py index 8a6150cc4e..a55310870e 100644 --- a/pybamm/simulation.py +++ b/pybamm/simulation.py @@ -1,6 +1,8 @@ # # Simulation class # +from __future__ import annotations + import pickle import pybamm import numpy as np @@ -9,8 +11,7 @@ import sys from functools import lru_cache from datetime import timedelta -from pybamm.util import have_optional_dependency -from typing import Optional +from pybamm.util import import_optional_dependency from pybamm.expression_tree.operations.serialise import Serialise @@ -62,6 +63,9 @@ class Simulation: A list of variables to plot automatically C_rate: float (optional) The C-rate at which you would like to run a constant current (dis)charge. + discretisation_kwargs: dict (optional) + Any keyword arguments to pass to the Discretisation class. + See :class:`pybamm.Discretisation` for details. """ def __init__( @@ -76,16 +80,11 @@ def __init__( solver=None, output_variables=None, C_rate=None, + discretisation_kwargs=None, ): self._parameter_values = parameter_values or model.default_parameter_values self._unprocessed_parameter_values = self._parameter_values - if isinstance(model, pybamm.lithium_ion.BasicDFNHalfCell): - if experiment is not None: - raise NotImplementedError( - "BasicDFNHalfCell is not compatible with experiment simulations." - ) - if experiment is None: # Check to see if the current is provided as data (i.e. drive cycle) current = self._parameter_values.get("Current function [A]") @@ -102,7 +101,7 @@ def __init__( } ) else: - if isinstance(experiment, (str, pybamm.step._Step)): + if isinstance(experiment, (str, pybamm.step.BaseStep)): experiment = pybamm.Experiment([experiment]) elif isinstance(experiment, list): experiment = pybamm.Experiment(experiment) @@ -125,13 +124,14 @@ def __init__( self._spatial_methods = spatial_methods or self._model.default_spatial_methods self._solver = solver or self._model.default_solver self._output_variables = output_variables + self._discretisation_kwargs = discretisation_kwargs or {} # Initialize empty built states self._model_with_set_params = None self._built_model = None self._built_initial_soc = None - self.op_conds_to_built_models = None - self.op_conds_to_built_solvers = None + self.steps_to_built_models = None + self.steps_to_built_solvers = None self._mesh = None self._disc = None self._solution = None @@ -177,149 +177,41 @@ def _set_random_seed(self): def set_up_and_parameterise_experiment(self): """ - Set up a simulation to run with an experiment. This creates a dictionary of - inputs (current/voltage/power, running time, stopping condition) for each - operating condition in the experiment. The model will then be solved by - integrating the model successively with each group of inputs, one group at a - time. - This needs to be done here and not in the Experiment class because the nominal - cell capacity (from the parameters) is used to convert C-rate to current. - """ - # Update experiment using capacity - capacity = self._parameter_values["Nominal cell capacity [A.h]"] - for op_conds in self.experiment.operating_conditions_steps: - if op_conds.type == "C-rate": - op_conds.type = "current" - op_conds.value = op_conds.value * capacity - - # Add time to the experiment times - dt = op_conds.duration - if dt is None: - if op_conds.type == "current": - # Current control: max simulation time: 3h / C-rate - Crate = op_conds.value / capacity - dt = 3 / abs(Crate) * 3600 # seconds - else: - # max simulation time: 1 day - dt = 24 * 3600 # seconds - op_conds.duration = dt - - # Set up model for experiment - self.set_up_and_parameterise_model_for_experiment() - - def set_up_and_parameterise_model_for_experiment(self): - """ - Set up self._model to be able to run the experiment (new version). - In this version, a new model is created for each step. + Create and parameterise the models for each step in the experiment. This increases set-up time since several models to be processed, but reduces simulation time since the model formulation is efficient. """ + parameter_values = self._parameter_values.copy() + # Set the initial temperature to be the temperature of the first step + # We can set this globally for all steps since any subsequent steps will either + # start at the temperature at the end of the previous step (if non-isothermal + # model), or will use the "Ambient temperature" input (if isothermal model). + # In either case, the initial temperature is not used for any steps except + # the first. + init_temp = self.experiment.steps[0].temperature + if init_temp is not None: + parameter_values["Initial temperature [K]"] = init_temp + + # Process each step self.experiment_unique_steps_to_model = {} - for op_number, op in enumerate(self.experiment.unique_steps): - new_model = self._model.new_copy() - new_parameter_values = self._parameter_values.copy() - - if op.type != "current": - # Voltage or power control - # Create a new model where the current density is now a variable - # To do so, we replace all instances of the current density in the - # model with a current density variable, which is obtained from the - # FunctionControl submodel - # check which kind of external circuit model we need (differential - # or algebraic) - if op.type == "voltage": - submodel_class = pybamm.external_circuit.VoltageFunctionControl - elif op.type == "power": - submodel_class = pybamm.external_circuit.PowerFunctionControl - - # Build the new submodel and update the model with it - submodel = submodel_class(new_model.param, new_model.options) - variables = new_model.variables - submodel.variables = submodel.get_fundamental_variables() - variables.update(submodel.variables) - submodel.variables.update(submodel.get_coupled_variables(variables)) - variables.update(submodel.variables) - submodel.set_rhs(variables) - submodel.set_algebraic(variables) - submodel.set_initial_conditions(variables) - new_model.rhs.update(submodel.rhs) - new_model.algebraic.update(submodel.algebraic) - new_model.initial_conditions.update(submodel.initial_conditions) - - # Set the "current function" to be the variable defined in the submodel - new_parameter_values["Current function [A]"] = submodel.variables[ - "Current [A]" - ] - self.update_new_model_events(new_model, op) - # Update parameter values - self._original_temperature = new_parameter_values["Ambient temperature [K]"] - experiment_parameter_values = self.get_experiment_parameter_values( - op, op_number - ) - new_parameter_values.update( - experiment_parameter_values, check_already_exists=False - ) - parameterised_model = new_parameter_values.process_model( - new_model, inplace=False + for step in self.experiment.unique_steps: + parameterised_model = step.process_model(self._model, parameter_values) + self.experiment_unique_steps_to_model[step.basic_repr()] = ( + parameterised_model ) - self.experiment_unique_steps_to_model[op.basic_repr()] = parameterised_model # Set up rest model if experiment has start times if self.experiment.initial_start_time: - new_model = self._model.new_copy() - # Update parameter values - new_parameter_values = self._parameter_values.copy() - self._original_temperature = new_parameter_values["Ambient temperature [K]"] - new_parameter_values.update( - {"Current function [A]": 0, "Ambient temperature [K]": "[input]"}, - check_already_exists=False, + # duration doesn't matter, we just need the model + rest_step = pybamm.step.rest(duration=1) + # Change ambient temperature to be an input, which will be changed at + # solve time + parameter_values["Ambient temperature [K]"] = "[input]" + parameterised_model = rest_step.process_model(self._model, parameter_values) + self.experiment_unique_steps_to_model["Rest for padding"] = ( + parameterised_model ) - parameterised_model = new_parameter_values.process_model( - new_model, inplace=False - ) - self.experiment_unique_steps_to_model[ - "Rest for padding" - ] = parameterised_model - - def update_new_model_events(self, new_model, op): - for term in op.termination: - event = term.get_event(new_model.variables, op.value) - if event is not None: - new_model.events.append(event) - - # Keep the min and max voltages as safeguards but add some tolerances - # so that they are not triggered before the voltage limits in the - # experiment - for i, event in enumerate(new_model.events): - if event.name in ["Minimum voltage [V]", "Maximum voltage [V]"]: - new_model.events[i] = pybamm.Event( - event.name, event.expression + 1, event.event_type - ) - - def get_experiment_parameter_values(self, op, op_number): - experiment_parameter_values = { - f"{op.type.capitalize()} function {op.unit}": op.value - } - - if op.temperature is not None: - ambient_temperature = op.temperature - experiment_parameter_values.update( - {"Ambient temperature [K]": ambient_temperature} - ) - - # If at the first operation, then the intial temperature - # should be the ambient temperature. - if op_number == 0: - experiment_parameter_values.update( - {"Initial temperature [K]": ambient_temperature} - ) - else: - experiment_parameter_values.update( - {"Ambient temperature [K]": self._original_temperature} - ) - - return experiment_parameter_values def set_parameters(self): """ @@ -335,13 +227,13 @@ def set_parameters(self): self._parameter_values.process_geometry(self._geometry) self._model = self._model_with_set_params - def set_initial_soc(self, initial_soc): + def set_initial_soc(self, initial_soc, inputs=None): if self._built_initial_soc != initial_soc: # reset self._model_with_set_params = None self._built_model = None - self.op_conds_to_built_models = None - self.op_conds_to_built_solvers = None + self.steps_to_built_models = None + self.steps_to_built_solvers = None options = self.model.options param = self._model.param @@ -354,20 +246,28 @@ def set_initial_soc(self, initial_soc): elif options["working electrode"] == "positive": self._parameter_values = ( self._unprocessed_parameter_values.set_initial_stoichiometry_half_cell( - initial_soc, param=param, inplace=False, options=options + initial_soc, + param=param, + inplace=False, + options=options, + inputs=inputs, ) ) else: self._parameter_values = ( self._unprocessed_parameter_values.set_initial_stoichiometries( - initial_soc, param=param, inplace=False, options=options + initial_soc, + param=param, + inplace=False, + options=options, + inputs=inputs, ) ) # Save solved initial SOC in case we need to re-build the model self._built_initial_soc = initial_soc - def build(self, check_model=True, initial_soc=None): + def build(self, initial_soc=None, inputs=None): """ A method to build the model into a system of matrices and vectors suitable for performing numerical computations. If the model has already been built or @@ -377,16 +277,15 @@ def build(self, check_model=True, initial_soc=None): Parameters ---------- - check_model : bool, optional - If True, model checks are performed after discretisation (see - :meth:`pybamm.Discretisation.process_model`). Default is True. initial_soc : float, optional Initial State of Charge (SOC) for the simulation. Must be between 0 and 1. If given, overwrites the initial concentrations provided in the parameter set. + inputs : dict, optional + A dictionary of input parameters to pass to the model when solving. """ if initial_soc is not None: - self.set_initial_soc(initial_soc) + self.set_initial_soc(initial_soc, inputs=inputs) if self.built_model: return @@ -396,22 +295,24 @@ def build(self, check_model=True, initial_soc=None): else: self.set_parameters() self._mesh = pybamm.Mesh(self._geometry, self._submesh_types, self._var_pts) - self._disc = pybamm.Discretisation(self._mesh, self._spatial_methods) + self._disc = pybamm.Discretisation( + self._mesh, self._spatial_methods, **self._discretisation_kwargs + ) self._built_model = self._disc.process_model( - self._model_with_set_params, inplace=False, check_model=check_model + self._model_with_set_params, inplace=False ) # rebuilt model so clear solver setup self._solver._model_set_up = {} - def build_for_experiment(self, check_model=True, initial_soc=None): + def build_for_experiment(self, initial_soc=None, inputs=None): """ Similar to :meth:`Simulation.build`, but for the case of simulating an experiment, where there may be several models and solvers to build. """ if initial_soc is not None: - self.set_initial_soc(initial_soc) + self.set_initial_soc(initial_soc, inputs) - if self.op_conds_to_built_models: + if self.steps_to_built_models: return else: self.set_up_and_parameterise_experiment() @@ -421,34 +322,36 @@ def build_for_experiment(self, check_model=True, initial_soc=None): self._parameter_values.process_geometry(self._geometry) # Only needs to set up mesh and discretisation once self._mesh = pybamm.Mesh(self._geometry, self._submesh_types, self._var_pts) - self._disc = pybamm.Discretisation(self._mesh, self._spatial_methods) + self._disc = pybamm.Discretisation( + self._mesh, self._spatial_methods, **self._discretisation_kwargs + ) # Process all the different models - self.op_conds_to_built_models = {} - self.op_conds_to_built_solvers = {} + self.steps_to_built_models = {} + self.steps_to_built_solvers = {} for ( - op_cond, + step, model_with_set_params, ) in self.experiment_unique_steps_to_model.items(): # It's ok to modify the model with set parameters in place as it's # not returned anywhere built_model = self._disc.process_model( - model_with_set_params, inplace=True, check_model=check_model + model_with_set_params, inplace=True ) solver = self._solver.copy() - self.op_conds_to_built_solvers[op_cond] = solver - self.op_conds_to_built_models[op_cond] = built_model + self.steps_to_built_solvers[step] = solver + self.steps_to_built_models[step] = built_model def solve( self, t_eval=None, solver=None, - check_model=True, save_at_cycles=None, calc_esoh=True, starting_solution=None, initial_soc=None, callbacks=None, showprogress=False, + inputs=None, **kwargs, ): """ @@ -475,9 +378,6 @@ def solve( provided in the data. solver : :class:`pybamm.BaseSolver`, optional The solver to use to solve the model. If None, Simulation.solver is used - check_model : bool, optional - If True, model checks are performed after discretisation (see - :meth:`pybamm.Discretisation.process_model`). Default is True. save_at_cycles : int or list of ints, optional Which cycles to save the full sub-solutions for. If None, all cycles are saved. If int, every multiple of save_at_cycles is saved. If list, every @@ -511,8 +411,10 @@ def solve( callbacks = pybamm.callbacks.setup_callbacks(callbacks) logs = {} + inputs = inputs or {} + if self.operating_mode in ["without experiment", "drive cycle"]: - self.build(check_model=check_model, initial_soc=initial_soc) + self.build(initial_soc=initial_soc, inputs=inputs) if save_at_cycles is not None: raise ValueError( "'save_at_cycles' option can only be used if simulating an " @@ -565,27 +467,31 @@ def solve( to be the points in the data. """, pybamm.SolverWarning, + stacklevel=2, ) dt_data_min = np.min(np.diff(time_data)) dt_eval_max = np.max(np.diff(t_eval)) if dt_eval_max > dt_data_min + sys.float_info.epsilon: warnings.warn( - """ - The largest timestep in t_eval ({}) is larger than - the smallest timestep in the data ({}). The returned + f""" + The largest timestep in t_eval ({dt_eval_max}) is larger than + the smallest timestep in the data ({dt_data_min}). The returned solution may not have the correct resolution to accurately capture the input. Try refining t_eval. Alternatively, passing t_eval = None automatically sets t_eval to be the points in the data. - """.format(dt_eval_max, dt_data_min), + """, pybamm.SolverWarning, + stacklevel=2, ) - self._solution = solver.solve(self.built_model, t_eval, **kwargs) + self._solution = solver.solve( + self.built_model, t_eval, inputs=inputs, **kwargs + ) elif self.operating_mode == "with experiment": callbacks.on_experiment_start(logs) - self.build_for_experiment(check_model=check_model, initial_soc=initial_soc) + self.build_for_experiment(initial_soc=initial_soc, inputs=inputs) if t_eval is not None: pybamm.logger.warning( "Ignoring t_eval as solution times are specified by the experiment" @@ -594,7 +500,7 @@ def solve( # inputs without having to build the simulation again self._solution = starting_solution # Step through all experimental conditions - user_inputs = kwargs.get("inputs", {}) + user_inputs = inputs timer = pybamm.Timer() # Set up eSOH solver (for summary variables) @@ -613,6 +519,7 @@ def solve( [starting_solution], esoh_solver=esoh_solver, save_this_cycle=True, + inputs=user_inputs, ) starting_solution_cycles = [cycle_solution] starting_solution_summary_variables = [cycle_sum_vars] @@ -648,7 +555,8 @@ def solve( current_solution = starting_solution or pybamm.EmptySolution() voltage_stop = self.experiment.termination.get("voltage") - logs["stopping conditions"] = {"voltage": voltage_stop} + time_stop = self.experiment.termination.get("time") + logs["stopping conditions"] = {"voltage": voltage_stop, "time": time_stop} idx = 0 num_cycles = len(self.experiment.cycle_lengths) @@ -657,10 +565,10 @@ def solve( # Add initial padding rest if current time is earlier than first start time # This could be the case when using a starting solution if starting_solution is not None: - op_conds = self.experiment.operating_conditions_steps[0] - if op_conds.start_time is not None: + step = self.experiment.steps[0] + if step.start_time is not None: rest_time = ( - op_conds.start_time + step.start_time - ( initial_start_time + timedelta(seconds=float(current_solution.t[-1])) @@ -670,10 +578,11 @@ def solve( # logs["step operating conditions"] = "Initial rest for padding" # callbacks.on_step_start(logs) - kwargs["inputs"] = { + inputs = { **user_inputs, "Ambient temperature [K]": ( - op_conds.temperature or self._original_temperature + step.temperature + or self._parameter_values["Ambient temperature [K]"] ), "start time": current_solution.t[-1], } @@ -681,7 +590,7 @@ def solve( step_solution = current_solution.cycles[-1].steps[-1] step_solution_with_rest = self.run_padding_rest( - kwargs, rest_time, step_solution + kwargs, rest_time, step_solution, inputs ) steps[-1] = step_solution + step_solution_with_rest @@ -698,7 +607,7 @@ def solve( # check if a user has tqdm installed if showprogress: - tqdm = have_optional_dependency("tqdm") + tqdm = import_optional_dependency("tqdm") cycle_lengths = tqdm.tqdm( self.experiment.cycle_lengths, desc="Cycling", @@ -742,23 +651,15 @@ def solve( for step_num in range(1, cycle_length + 1): # Use 1-indexing for printing cycle number as it is more # human-intuitive - op_conds = self.experiment.operating_conditions_steps[idx] - - # Hacky patch to allow correct processing of end_time and next_starting time - # For efficiency purposes, op_conds treats identical steps as the same object - # regardless of the initial time. Should be refactored as part of #3176 - op_conds_unproc = ( - self.experiment.operating_conditions_steps_unprocessed[idx] - ) - + step = self.experiment.steps[idx] start_time = current_solution.t[-1] # If step has an end time, dt must take that into account - if getattr(op_conds_unproc, "end_time", None): + if step.end_time is not None: dt = min( - op_conds.duration, + step.duration, ( - op_conds_unproc.end_time + step.end_time - ( initial_start_time + timedelta(seconds=float(start_time)) @@ -766,28 +667,35 @@ def solve( ).total_seconds(), ) else: - dt = op_conds.duration - op_conds_str = str(op_conds) - model = self.op_conds_to_built_models[op_conds.basic_repr()] - solver = self.op_conds_to_built_solvers[op_conds.basic_repr()] + dt = step.duration + + # if dt + starttime is larger than time_stop, set dt to time_stop - starttime + if time_stop is not None: + dt = min(dt, time_stop - start_time) + + step_str = str(step) + model = self.steps_to_built_models[step.basic_repr()] + solver = self.steps_to_built_solvers[step.basic_repr()] logs["step number"] = (step_num, cycle_length) - logs["step operating conditions"] = op_conds_str + logs["step operating conditions"] = step_str + logs["step duration"] = step.duration callbacks.on_step_start(logs) - kwargs["inputs"] = { + inputs = { **user_inputs, "start time": start_time, } # Make sure we take at least 2 timesteps - npts = max(int(round(dt / op_conds.period)) + 1, 2) + npts = max(int(round(dt / step.period)) + 1, 2) try: step_solution = solver.step( current_solution, model, dt, - npts=npts, + t_eval=np.linspace(0, dt, npts), save=False, + inputs=inputs, **kwargs, ) except pybamm.SolverError as error: @@ -811,9 +719,9 @@ def solve( step_termination = step_solution.termination # Add a padding rest step if necessary - if getattr(op_conds_unproc, "next_start_time", None) is not None: + if step.next_start_time is not None: rest_time = ( - op_conds_unproc.next_start_time + step.next_start_time - ( initial_start_time + timedelta(seconds=float(step_solution.t[-1])) @@ -824,16 +732,17 @@ def solve( logs["step operating conditions"] = "Rest for padding" callbacks.on_step_start(logs) - kwargs["inputs"] = { + inputs = { **user_inputs, "Ambient temperature [K]": ( - op_conds.temperature or self._original_temperature + step.temperature + or self._parameter_values["Ambient temperature [K]"] ), "start time": step_solution.t[-1], } step_solution_with_rest = self.run_padding_rest( - kwargs, rest_time, step_solution + kwargs, rest_time, step_solution, inputs=inputs ) step_solution += step_solution_with_rest @@ -855,21 +764,37 @@ def solve( current_solution = cycle_solution + logs["experiment time"] = cycle_solution.t[-1] callbacks.on_step_end(logs) logs["termination"] = step_solution.termination - # Only allow events specified by experiment - if not ( + + # Check for some cases that would make the experiment end early + if step_termination == "final time" and step.uses_default_duration: + # reached the default duration of a step (typically we should + # reach an event before the default duration) + callbacks.on_experiment_infeasible_time(logs) + feasible = False + break + + elif not ( isinstance(step_solution, pybamm.EmptySolution) or step_termination == "final time" or "[experiment]" in step_termination ): - callbacks.on_experiment_infeasible(logs) + # Step has reached an event that is not specified in the + # experiment + callbacks.on_experiment_infeasible_event(logs) feasible = False break - # Increment index for next iteration - idx += 1 + elif time_stop is not None and logs["experiment time"] >= time_stop: + # reached the time limit of the experiment + break + + else: + # Increment index for next iteration, then continue + idx += 1 if save_this_cycle or feasible is False: self._solution = self._solution + cycle_solution @@ -880,7 +805,7 @@ def solve( if all(isinstance(step, pybamm.EmptySolution) for step in steps): if len(steps) == 1: raise pybamm.SolverError( - f"Step '{op_conds_str}' is infeasible " + f"Step '{step_str}' is infeasible " "due to exceeded bounds at initial conditions. " "If this step is part of a longer cycle, " "round brackets should be used to indicate this, " @@ -891,15 +816,16 @@ def solve( "])" ) else: - this_cycle = self.experiment.operating_conditions_cycles[ - cycle_num - 1 - ] + this_cycle = self.experiment.cycles[cycle_num - 1] raise pybamm.SolverError( f"All steps in the cycle {this_cycle} are infeasible " "due to exceeded bounds at initial conditions." ) cycle_sol = pybamm.make_cycle_solution( - steps, esoh_solver=esoh_solver, save_this_cycle=save_this_cycle + steps, + esoh_solver=esoh_solver, + save_this_cycle=save_this_cycle, + inputs=user_inputs, ) cycle_solution, cycle_sum_vars, cycle_first_state = cycle_sol all_cycle_solutions.append(cycle_solution) @@ -925,6 +851,13 @@ def solve( logs["stopping conditions"]["capacity"] = capacity_stop logs["elapsed time"] = timer.time() + + # Add minimum voltage to summary variable logs if there is a voltage stop + # See PR #3995 + if voltage_stop is not None: + min_voltage = np.min(cycle_solution["Battery voltage [V]"].data) + logs["summary variables"]["Minimum voltage [V]"] = min_voltage + callbacks.on_cycle_end(logs) # Break if stopping conditions are met @@ -935,7 +868,6 @@ def solve( break if voltage_stop is not None: - min_voltage = cycle_sum_vars["Minimum voltage [V]"] if min_voltage <= voltage_stop[0]: break @@ -955,9 +887,9 @@ def solve( return self.solution - def run_padding_rest(self, kwargs, rest_time, step_solution): - model = self.op_conds_to_built_models["Rest for padding"] - solver = self.op_conds_to_built_solvers["Rest for padding"] + def run_padding_rest(self, kwargs, rest_time, step_solution, inputs): + model = self.steps_to_built_models["Rest for padding"] + solver = self.steps_to_built_solvers["Rest for padding"] # Make sure we take at least 2 timesteps. The period is hardcoded to 10 # minutes,the user can always override it by adding a rest step @@ -967,15 +899,23 @@ def run_padding_rest(self, kwargs, rest_time, step_solution): step_solution, model, rest_time, - npts=npts, + t_eval=np.linspace(0, rest_time, npts), save=False, + inputs=inputs, **kwargs, ) return step_solution_with_rest def step( - self, dt, solver=None, npts=2, save=True, starting_solution=None, **kwargs + self, + dt, + solver=None, + t_eval=None, + save=True, + starting_solution=None, + inputs=None, + **kwargs, ): """ A method to step the model forward one timestep. This method will @@ -987,9 +927,10 @@ def step( The timestep over which to step the solution solver : :class:`pybamm.BaseSolver` The solver to use to solve the model. - npts : int, optional - The number of points at which the solution will be returned during - the step dt. Default is 2 (returns the solution at t0 and t0 + dt). + t_eval : list or numpy.ndarray, optional + An array of times at which to return the solution during the step + (Note: t_eval is the time measured from the start of the step, so should start at 0 and end at dt). + By default, the solution is returned at t0 and t0 + dt. save : bool Turn on to store the solution of all previous timesteps starting_solution : :class:`pybamm.Solution` @@ -1009,7 +950,13 @@ def step( starting_solution = self._solution self._solution = solver.step( - starting_solution, self.built_model, dt, npts=npts, save=save, **kwargs + starting_solution, + self.built_model, + dt, + t_eval=t_eval, + save=save, + inputs=inputs, + **kwargs, ) return self.solution @@ -1072,6 +1019,8 @@ def create_gif(self, number_of_images=80, duration=0.1, output_filename="plot.gi Name of the generated GIF file. """ + if self.solution is None: + raise ValueError("The simulation has not been solved yet.") if self.quick_plot is None: self.quick_plot = pybamm.QuickPlot(self._solution) @@ -1152,8 +1101,8 @@ def save(self, filename): ): self._solver.integrator_specs = {} - if self.op_conds_to_built_solvers is not None: - for solver in self.op_conds_to_built_solvers.values(): + if self.steps_to_built_solvers is not None: + for solver in self.steps_to_built_solvers.values(): if ( isinstance(solver, pybamm.CasadiSolver) and solver.integrator_specs != {} @@ -1165,7 +1114,7 @@ def save(self, filename): def save_model( self, - filename: Optional[str] = None, + filename: str | None = None, mesh: bool = False, variables: bool = False, ): @@ -1207,6 +1156,45 @@ def save_model( """ ) + def plot_voltage_components( + self, + ax=None, + show_legend=True, + split_by_electrode=False, + show_plot=True, + **kwargs_fill, + ): + """ + Generate a plot showing the component overpotentials that make up the voltage + + Parameters + ---------- + ax : matplotlib Axis, optional + The axis on which to put the plot. If None, a new figure and axis is created. + show_legend : bool, optional + Whether to display the legend. Default is True. + split_by_electrode : bool, optional + Whether to show the overpotentials for the negative and positive electrodes + separately. Default is False. + show_plot : bool, optional + Whether to show the plots. Default is True. Set to False if you want to + only display the plot after plt.show() has been called. + kwargs_fill + Keyword arguments, passed to ax.fill_between. + + """ + if self.solution is None: + raise ValueError("The simulation has not been solved yet.") + + return pybamm.plot_voltage_components( + self.solution, + ax=ax, + show_legend=show_legend, + split_by_electrode=split_by_electrode, + show_plot=show_plot, + **kwargs_fill, + ) + def load_sim(filename): """Load a saved simulation""" diff --git a/pybamm/solvers/__init__.py b/pybamm/solvers/__init__.py index e69de29bb2..fc8be7e2f8 100644 --- a/pybamm/solvers/__init__.py +++ b/pybamm/solvers/__init__.py @@ -0,0 +1,5 @@ +__all__ = ['algebraic_solver', 'base_solver', 'c_solvers', + 'casadi_algebraic_solver', 'casadi_solver', 'dummy_solver', + 'idaklu_jax', 'idaklu_solver', 'jax_bdf_solver', 'jax_solver', + 'lrudict', 'processed_variable', 'processed_variable_computed', + 'scipy_solver', 'solution'] diff --git a/pybamm/solvers/algebraic_solver.py b/pybamm/solvers/algebraic_solver.py index d241d5b24c..bc711ff02a 100644 --- a/pybamm/solvers/algebraic_solver.py +++ b/pybamm/solvers/algebraic_solver.py @@ -101,14 +101,12 @@ def algebraic(t, y): integration_time = 0 for idx, t in enumerate(t_eval): - def root_fun(y_alg): + def root_fun(y_alg, t=t): "Evaluates algebraic using y" y = np.concatenate([y0_diff, y_alg]) out = algebraic(t, y) pybamm.logger.debug( - "Evaluating algebraic equations at t={}, L2-norm is {}".format( - t, np.linalg.norm(out) - ) + f"Evaluating algebraic equations at t={t}, L2-norm is {np.linalg.norm(out)}" ) return out @@ -116,7 +114,7 @@ def root_fun(y_alg): if jac: if issparse(jac(t_eval[0], y0, inputs)): - def jac_fn(y_alg): + def jac_fn(y_alg, jac=jac): """ Evaluates Jacobian using y0_diff (fixed) and y_alg (varying) """ @@ -125,7 +123,7 @@ def jac_fn(y_alg): else: - def jac_fn(y_alg): + def jac_fn(y_alg, jac=jac): """ Evaluates Jacobian using y0_diff (fixed) and y_alg (varying) """ @@ -170,7 +168,7 @@ def root_norm(y): jac_norm = None else: - def jac_norm(y): + def jac_norm(y, jac_fn=jac_fn): return np.sum(2 * root_fun(y) * jac_fn(y), 0) if self.method == "minimize": diff --git a/pybamm/solvers/base_solver.py b/pybamm/solvers/base_solver.py index 69de3be968..1425bf0845 100644 --- a/pybamm/solvers/base_solver.py +++ b/pybamm/solvers/base_solver.py @@ -1,6 +1,3 @@ -# -# Base solver class -# import copy import itertools from scipy.sparse import block_diag @@ -8,12 +5,14 @@ import numbers import sys import warnings +import platform import casadi import numpy as np import pybamm from pybamm.expression_tree.binary_operators import _Heaviside +from pybamm import ParameterValues class BaseSolver: @@ -51,7 +50,7 @@ def __init__( root_method=None, root_tol=1e-6, extrap_tol=None, - output_variables=[], + output_variables=None, ): self.method = method self.rtol = rtol @@ -59,7 +58,7 @@ def __init__( self.root_tol = root_tol self.root_method = root_method self.extrap_tol = extrap_tol or -1e-10 - self.output_variables = output_variables + self.output_variables = [] if output_variables is None else output_variables self._model_set_up = {} # Defaults, can be overwritten by specific solver @@ -68,6 +67,7 @@ def __init__( self.algebraic_solver = False self._on_extrapolation = "warn" self.computed_var_fcns = {} + self._mp_context = self.get_platform_context(platform.system()) @property def root_method(self): @@ -339,7 +339,7 @@ def _check_and_prepare_model_inplace(self, model, inputs, ics_only): raise pybamm.DiscretisationError( "Cannot automatically discretise model, " f"model should be discretised before solving ({e})" - ) + ) from e if ( isinstance(self, (pybamm.CasadiSolver, pybamm.CasadiAlgebraicSolver)) @@ -357,7 +357,8 @@ def _check_and_prepare_model_inplace(self, model, inputs, ics_only): ) model.convert_to_format = "casadi" - def _get_vars_for_processing(self, model, inputs, calculate_sensitivities_explicit): + @staticmethod + def _get_vars_for_processing(model, inputs, calculate_sensitivities_explicit): vars_for_processing = { "model": model, "calculate_sensitivities_explicit": calculate_sensitivities_explicit, @@ -412,8 +413,9 @@ def _get_vars_for_processing(self, model, inputs, calculate_sensitivities_explic return vars_for_processing + @staticmethod def _set_up_model_sensitivities_inplace( - self, model, inputs, calculate_sensitivities_explicit + model, inputs, calculate_sensitivities_explicit ): """ Set up model attributes related to sensitivities. @@ -499,16 +501,15 @@ def _set_up_events(self, model, t_eval, inputs, vars_for_processing): model.concatenated_algebraic.pre_order(), ): if isinstance(symbol, _Heaviside): - found_t = False + expr = None if symbol.right == pybamm.t: expr = symbol.left - found_t = True - elif symbol.left == pybamm.t: - expr = symbol.right - found_t = True + else: + if symbol.left == pybamm.t: + expr = symbol.right # Update the events if the heaviside function depended on t - if found_t: + if expr is not None: model.events.append( pybamm.Event( str(symbol), @@ -519,12 +520,11 @@ def _set_up_events(self, model, t_eval, inputs, vars_for_processing): elif isinstance(symbol, pybamm.Modulo): if symbol.left == pybamm.t: expr = symbol.right - if t_eval is None: - N_events = 200 - else: - N_events = t_eval[-1] // expr.value + num_events = 200 + if t_eval is not None: + num_events = t_eval[-1] // expr.value - for i in np.arange(N_events): + for i in np.arange(num_events): model.events.append( pybamm.Event( str(symbol), @@ -532,6 +532,8 @@ def _set_up_events(self, model, t_eval, inputs, vars_for_processing): pybamm.EventType.DISCONTINUITY, ) ) + else: + pass casadi_switch_events = [] terminate_events = [] @@ -684,7 +686,9 @@ def calculate_consistent_state(self, model, time=0, inputs=None): try: root_sol = self.root_method._integrate(model, np.array([time]), inputs) except pybamm.SolverError as e: - raise pybamm.SolverError(f"Could not find consistent states: {e.args[0]}") + raise pybamm.SolverError( + f"Could not find consistent states: {e.args[0]}" + ) from e pybamm.logger.debug("Found consistent states") self.check_extrapolation(root_sol, model.events) @@ -709,16 +713,16 @@ def solve( The model whose solution to calculate. Must have attributes rhs and initial_conditions. All calls to solve must pass in the same model or an error is raised - t_eval : numeric type - The times (in seconds) at which to compute the solution + t_eval : None, list or ndarray, optional + The times (in seconds) at which to compute the solution. Defaults to None. inputs : dict or list, optional A dictionary or list of dictionaries describing any input parameters to pass to the model when solving nproc : int, optional Number of processes to use when solving for more than one set of input parameters. Defaults to value returned by "os.cpu_count()". - calculate_sensitivities : list of str or bool - If true, solver calculates sensitivities of all input parameters. + calculate_sensitivities : list of str or bool, optional + Whether the solver calculates sensitivities of all input parameters. Defaults to False. If only a subset of sensitivities are required, can also pass a list of input parameter names @@ -824,7 +828,7 @@ def solve( ) # It is assumed that when len(inputs_list) > 1, model set # up (initial condition, time-scale and length-scale) does - # not depend on input parameters. Thefore only `model_inputs[0]` + # not depend on input parameters. Therefore, only `model_inputs[0]` # is passed to `set_up`. # See https://github.com/pybamm-team/PyBaMM/pull/1261 self.set_up(model, model_inputs_list[0], t_eval) @@ -844,9 +848,9 @@ def solve( # If the new initial conditions are different # and cannot be evaluated directly, set up again self.set_up(model, model_inputs_list[0], t_eval, ics_only=True) - self._model_set_up[model][ - "initial conditions" - ] = model.concatenated_initial_conditions + self._model_set_up[model]["initial conditions"] = ( + model.concatenated_initial_conditions + ) set_up_time = timer.time() timer.reset() @@ -892,10 +896,7 @@ def solve( solutions = None for start_index, end_index in zip(start_indices, end_indices): pybamm.logger.verbose( - "Calling solver for {} < t < {}".format( - t_eval[start_index], - t_eval[end_index - 1], - ) + f"Calling solver for {t_eval[start_index]} < t < {t_eval[end_index - 1]}" ) ninputs = len(model_inputs_list) if ninputs == 1: @@ -914,7 +915,7 @@ def solve( model_inputs_list, ) else: - with mp.Pool(processes=nproc) as p: + with mp.get_context(self._mp_context).Pool(processes=nproc) as p: new_solutions = p.starmap( self._integrate, zip( @@ -972,24 +973,13 @@ def solve( if len(solutions) == 1: pybamm.logger.info(f"Finish solving {model.name} ({termination})") pybamm.logger.info( - ( - "Set-up time: {}, Solve time: {} (of which integration time: {}), " - "Total time: {}" - ).format( - solutions[0].set_up_time, - solutions[0].solve_time, - solutions[0].integration_time, - solutions[0].total_time, - ) + f"Set-up time: {solutions[0].set_up_time}, Solve time: {solutions[0].solve_time} (of which integration time: {solutions[0].integration_time}), " + f"Total time: {solutions[0].total_time}" ) else: pybamm.logger.info(f"Finish solving {model.name} for all inputs") pybamm.logger.info( - ("Set-up time: {}, Solve time: {}, Total time: {}").format( - solutions[0].set_up_time, - solutions[0].solve_time, - solutions[0].total_time, - ) + f"Set-up time: {solutions[0].set_up_time}, Solve time: {solutions[0].solve_time}, Total time: {solutions[0].total_time}" ) # Raise error if solutions[0] only contains one timestep (except for algebraic @@ -1010,8 +1000,9 @@ def solve( else: return solutions - def _get_discontinuity_start_end_indices(self, model, inputs, t_eval): - if model.discontinuity_events_eval == []: + @staticmethod + def _get_discontinuity_start_end_indices(model, inputs, t_eval): + if not model.discontinuity_events_eval: pybamm.logger.verbose("No discontinuity events found") return [0], [len(t_eval)], t_eval @@ -1050,7 +1041,7 @@ def _get_discontinuity_start_end_indices(self, model, inputs, t_eval): ) # insert time points around discontinuities in t_eval - # keep track of sub sections to integrate by storing start and end indices + # keep track of subsections to integrate by storing start and end indices start_indices = [0] end_indices = [] eps = sys.float_info.epsilon @@ -1069,7 +1060,8 @@ def _get_discontinuity_start_end_indices(self, model, inputs, t_eval): return start_indices, end_indices, t_eval - def _check_events_with_initial_conditions(self, t_eval, model, inputs_dict): + @staticmethod + def _check_events_with_initial_conditions(t_eval, model, inputs_dict): num_terminate_events = len(model.terminate_events_eval) if num_terminate_events == 0: return @@ -1102,7 +1094,8 @@ def step( old_solution, model, dt, - npts=2, + t_eval=None, + npts=None, inputs=None, save=True, ): @@ -1120,14 +1113,15 @@ def step( initial_conditions dt : numeric type The timestep (in seconds) over which to step the solution - npts : int, optional - The number of points at which the solution will be returned during - the step dt. default is 2 (returns the solution at t0 and t0 + dt). + t_eval : list or numpy.ndarray, optional + An array of times at which to return the solution during the step + (Note: t_eval is the time measured from the start of the step, so should start at 0 and end at dt). + By default, the solution is returned at t0 and t0 + dt. + npts : deprecated inputs : dict, optional Any input parameters to pass to the model when solving - save : bool - Turn on to store the solution of all previous timesteps - + save : bool, optional + Save solution with all previous timesteps. Defaults to True. Raises ------ :class:`pybamm.ModelError` @@ -1161,10 +1155,28 @@ def step( f"Step time must be at least {pybamm.TimerTime(step_start_offset)}" ) + # Raise deprecation warning for npts and convert it to t_eval + if npts is not None: + warnings.warn( + "The 'npts' parameter is deprecated, use 't_eval' instead.", + DeprecationWarning, + stacklevel=2, + ) + t_eval = np.linspace(0, dt, npts) + + if t_eval is not None: + # Checking if t_eval lies within range + if t_eval[0] != 0 or t_eval[-1] != dt: + raise pybamm.SolverError( + "Elements inside array t_eval must lie in the closed interval 0 to dt" + ) + + else: + t_eval = np.array([0, dt]) + t_start = old_solution.t[-1] + t_eval = t_start + t_eval t_end = t_start + dt - # Calculate t_eval - t_eval = np.linspace(t_start, t_end, npts) if t_start == 0: t_start_shifted = t_start @@ -1248,15 +1260,8 @@ def step( # Report times pybamm.logger.verbose(f"Finish stepping {model.name} ({termination})") pybamm.logger.verbose( - ( - "Set-up time: {}, Step time: {} (of which integration time: {}), " - "Total time: {}" - ).format( - solution.set_up_time, - solution.solve_time, - solution.integration_time, - solution.total_time, - ) + f"Set-up time: {solution.set_up_time}, Step time: {solution.solve_time} (of which integration time: {solution.integration_time}), " + f"Total time: {solution.total_time}" ) # Return solution @@ -1265,7 +1270,8 @@ def step( else: return old_solution + solution - def get_termination_reason(self, solution, events): + @staticmethod + def get_termination_reason(solution, events): """ Identify the cause for termination. In particular, if the solver terminated due to an event, (try to) pinpoint which event was responsible. If an event @@ -1375,8 +1381,9 @@ def check_extrapolation(self, solution, events): f"While solving {name} extrapolation occurred " f"for {extrap_events}", pybamm.SolverWarning, + stacklevel=2, ) - # Add the event dictionaryto the solution object + # Add the event dictionary to the solution object solution.extrap_events = extrap_events elif self._on_extrapolation == "error": raise pybamm.SolverError( @@ -1386,9 +1393,19 @@ def check_extrapolation(self, solution, events): "outside these bounds." ) - def _set_up_model_inputs(self, model, inputs): + def get_platform_context(self, system_type: str): + # Set context for parallel processing depending on the platform + if system_type.lower() in ["linux", "darwin"]: + return "fork" + return "spawn" + + @staticmethod + def _set_up_model_inputs(model, inputs): """Set up input parameters""" - inputs = inputs or {} + if inputs is None: + inputs = {} + else: + inputs = ParameterValues.check_parameter_values(inputs) # Go through all input parameters that can be found in the model # Only keep the ones that are actually used in the model @@ -1434,21 +1451,21 @@ def process( jac: :class:`pybamm.EvaluatorPython` or :class:`pybamm.EvaluatorJaxJacobian` or :class:`casadi.Function` - evaluator for the Jacobian $\frac{\partial f}{\partial y}$ + evaluator for the Jacobian $\\frac{\\partial f}{\\partial y}$ of the function given by `symbol` jacp: :class:`pybamm.EvaluatorPython` or :class:`pybamm.EvaluatorJaxSensitivities` or :class:`casadi.Function` evaluator for the parameter sensitivities - $\frac{\partial f}{\partial p}$ + $\frac{\\partial f}{\\partial p}$ of the function given by `symbol` jac_action: :class:`pybamm.EvaluatorPython` or :class:`pybamm.EvaluatorJax` or :class:`casadi.Function` evaluator for product of the Jacobian with a vector $v$, - i.e. $\frac{\partial f}{\partial y} * v$ + i.e. $\\frac{\\partial f}{\\partial y} * v$ """ def report(string): diff --git a/pybamm/solvers/c_solvers/idaklu.cpp b/pybamm/solvers/c_solvers/idaklu.cpp index be90955b9c..9f99d4d3f4 100644 --- a/pybamm/solvers/c_solvers/idaklu.cpp +++ b/pybamm/solvers/c_solvers/idaklu.cpp @@ -1,6 +1,6 @@ -#include "idaklu/casadi_solver.hpp" -#include "idaklu/common.hpp" -#include "idaklu/python.hpp" +#include +#include +#include #include #include @@ -8,7 +8,10 @@ #include #include -#include +#include "idaklu/casadi_solver.hpp" +#include "idaklu/idaklu_jax.hpp" +#include "idaklu/common.hpp" +#include "idaklu/python.hpp" Function generate_function(const std::string &data) { @@ -56,9 +59,6 @@ PYBIND11_MODULE(idaklu, m) py::arg("inputs"), py::return_value_policy::take_ownership); - //py::bind_vector>(m, "VectorFunction"); - //py::implicitly_convertible>(); - m.def("create_casadi_solver", &create_casadi_solver, "Create a casadi idaklu solver object", py::arg("number_of_states"), @@ -90,6 +90,49 @@ PYBIND11_MODULE(idaklu, m) py::arg("string"), py::return_value_policy::take_ownership); + // IdakluJax interface routines + py::class_(m, "IdakluJax") + .def( + "register_callback_eval", + &IdakluJax::register_callback_eval, + "Register a callback for function evaluation", + py::arg("callback") + ) + .def( + "register_callback_jvp", + &IdakluJax::register_callback_jvp, + "Register a callback for JVP evaluation", + py::arg("callback") + ) + .def( + "register_callback_vjp", + &IdakluJax::register_callback_vjp, + "Register a callback for the VJP evaluation", + py::arg("callback") + ) + .def( + "register_callbacks", + &IdakluJax::register_callbacks, + "Register callbacks for function evaluation, JVP evaluation, and VJP evaluation", + py::arg("callback_eval"), + py::arg("callback_jvp"), + py::arg("callback_vjp") + ) + .def( + "get_index", + &IdakluJax::get_index, + "Get the index of the JAXified instance" + ); + m.def( + "create_idaklu_jax", + &create_idaklu_jax, + "Create an idaklu jax object" + ); + m.def( + "registrations", + &Registrations + ); + py::class_(m, "Function"); py::class_(m, "solution") diff --git a/pybamm/solvers/c_solvers/idaklu/common.hpp b/pybamm/solvers/c_solvers/idaklu/common.hpp index 55fd4b1c5d..e0abbb5a1d 100644 --- a/pybamm/solvers/c_solvers/idaklu/common.hpp +++ b/pybamm/solvers/c_solvers/idaklu/common.hpp @@ -10,7 +10,6 @@ #include /* defs. of SUNRabs, SUNRexp, etc. */ #include /* defs. of realtype, sunindextype */ - #if SUNDIALS_VERSION_MAJOR >= 6 #include #endif @@ -26,8 +25,6 @@ #include /* access to sparse SUNMatrix */ #include /* access to dense SUNMatrix */ - - #include #include @@ -36,15 +33,12 @@ using np_array = py::array_t; using np_array_dense = py::array_t; using np_array_int = py::array_t; -#ifdef NDEBUG -#define DEBUG(x) -#else -#define DEBUG(x) do { std::cerr << __FILE__ << ':' << __LINE__ << ' ' << x << std::endl; } while (0) -#endif - #ifdef NDEBUG #define DEBUG_VECTOR(vector) #define DEBUG_VECTORn(vector) +#define DEBUG_v(v, N) +#define DEBUG(x) +#define DEBUG_n(x) #else #define DEBUG_VECTORn(vector, N) {\ @@ -80,6 +74,14 @@ using np_array_int = py::array_t; } \ std::cout << "]" << std::endl; } +#define DEBUG(x) { \ + std::cerr << __FILE__ << ":" << __LINE__ << " " << x << std::endl; \ + } + +#define DEBUG_n(x) { \ + std::cerr << __FILE__ << ":" << __LINE__ << "," << #x << " = " << x << std::endl; \ + } + #endif #endif // PYBAMM_IDAKLU_COMMON_HPP diff --git a/pybamm/solvers/c_solvers/idaklu/idaklu_jax.cpp b/pybamm/solvers/c_solvers/idaklu/idaklu_jax.cpp new file mode 100644 index 0000000000..b338560259 --- /dev/null +++ b/pybamm/solvers/c_solvers/idaklu/idaklu_jax.cpp @@ -0,0 +1,264 @@ +#include "idaklu_jax.hpp" + +#include +#include +#include +#include +#include + +#include +#include +#include + +// Initialise static variable +std::int64_t IdakluJax::universal_count = 0; + +// Repository of instantiated IdakluJax objects +std::map idaklu_jax_instances; + +// Create a new IdakluJax object, assign identifier, add to the objects list and return as pointer +IdakluJax *create_idaklu_jax() { + IdakluJax *p = new IdakluJax(); + idaklu_jax_instances[p->get_index()] = p; + return p; +} + +IdakluJax::IdakluJax() { + index = universal_count++; +} + +IdakluJax::~IdakluJax() { + idaklu_jax_instances.erase(index); +} + +void IdakluJax::register_callback_eval(CallbackEval h) { + callback_eval = h; +} + +void IdakluJax::register_callback_jvp(CallbackJvp h) { + callback_jvp = h; +} + +void IdakluJax::register_callback_vjp(CallbackVjp h) { + callback_vjp = h; +} + +void IdakluJax::register_callbacks(CallbackEval h_eval, CallbackJvp h_jvp, CallbackVjp h_vjp) { + register_callback_eval(h_eval); + register_callback_jvp(h_jvp); + register_callback_vjp(h_vjp); +} + +void IdakluJax::cpu_idaklu_eval(void *out_tuple, const void **in) { + // Parse the inputs --- note that these come from jax lowering and are NOT np_array's + int k = 1; // Start indexing at 1 to skip idaklu_jax index + const std::int64_t n_t = *reinterpret_cast(in[k++]); + const std::int64_t n_vars = *reinterpret_cast(in[k++]); + const std::int64_t n_inputs = *reinterpret_cast(in[k++]); + const realtype *t = reinterpret_cast(in[k++]); + realtype *inputs = new realtype(n_inputs); + for (int i = 0; i < n_inputs; i++) + inputs[i] = reinterpret_cast(in[k++])[0]; + void *out = reinterpret_cast(out_tuple); + + // Log + DEBUG("cpu_idaklu"); + DEBUG_n(index); + DEBUG_n(n_t); + DEBUG_n(n_vars); + DEBUG_n(n_inputs); + DEBUG_v(t, n_t); + DEBUG_v(inputs, n_inputs); + + // Acquire GIL (since this function is called as a capsule) + py::gil_scoped_acquire acquire; + PyGILState_STATE state = PyGILState_Ensure(); + + // Convert time vector to an np_array + py::capsule t_capsule(t, "t_capsule"); + np_array t_np = np_array({n_t}, {sizeof(realtype)}, t, t_capsule); + + // Convert inputs to an np_array + py::capsule in_capsule(inputs, "in_capsule"); + np_array in_np = np_array({n_inputs}, {sizeof(realtype)}, inputs, in_capsule); + + // Call solve function in python to obtain an np_array + np_array out_np = callback_eval(t_np, in_np); + auto out_buf = out_np.request(); + const realtype *out_ptr = reinterpret_cast(out_buf.ptr); + + // Arrange into 'out' array + memcpy(out, out_ptr, n_t * n_vars * sizeof(realtype)); + + // Release GIL + PyGILState_Release(state); +} + +void IdakluJax::cpu_idaklu_jvp(void *out_tuple, const void **in) { + // Parse the inputs --- note that these come from jax lowering and are NOT np_array's + int k = 1; // Start indexing at 1 to skip idaklu_jax index + const std::int64_t n_t = *reinterpret_cast(in[k++]); + const std::int64_t n_vars = *reinterpret_cast(in[k++]); + const std::int64_t n_inputs = *reinterpret_cast(in[k++]); + const realtype *primal_t = reinterpret_cast(in[k++]); + realtype *primal_inputs = new realtype(n_inputs); + for (int i = 0; i < n_inputs; i++) + primal_inputs[i] = reinterpret_cast(in[k++])[0]; + const realtype *tangent_t = reinterpret_cast(in[k++]); + realtype *tangent_inputs = new realtype(n_inputs); + for (int i = 0; i < n_inputs; i++) + tangent_inputs[i] = reinterpret_cast(in[k++])[0]; + void *out = reinterpret_cast(out_tuple); + + // Log + DEBUG("cpu_idaklu_jvp"); + DEBUG_n(n_t); + DEBUG_n(n_vars); + DEBUG_n(n_inputs); + DEBUG_v(primal_t, n_t); + DEBUG_v(primal_inputs, n_inputs); + DEBUG_v(tangent_t, n_t); + DEBUG_v(tangent_inputs, n_inputs); + + // Acquire GIL (since this function is called as a capsule) + py::gil_scoped_acquire acquire; + PyGILState_STATE state = PyGILState_Ensure(); + + // Form primals time vector as np_array + py::capsule primal_t_capsule(primal_t, "primal_t_capsule"); + np_array primal_t_np = np_array( + {n_t}, + {sizeof(realtype)}, + primal_t, + primal_t_capsule + ); + + // Pack primals as np_array + py::capsule primal_inputs_capsule(primal_inputs, "primal_inputs_capsule"); + np_array primal_inputs_np = np_array( + {n_inputs}, + {sizeof(realtype)}, + primal_inputs, + primal_inputs_capsule + ); + + // Form tangents time vector as np_array + py::capsule tangent_t_capsule(tangent_t, "tangent_t_capsule"); + np_array tangent_t_np = np_array( + {n_t}, + {sizeof(realtype)}, + tangent_t, + tangent_t_capsule + ); + + // Pack tangents as np_array + py::capsule tangent_inputs_capsule(tangent_inputs, "tangent_inputs_capsule"); + np_array tangent_inputs_np = np_array( + {n_inputs}, + {sizeof(realtype)}, + tangent_inputs, + tangent_inputs_capsule + ); + + // Call JVP function in python to obtain an np_array + np_array y_dot = callback_jvp( + primal_t_np, primal_inputs_np, + tangent_t_np, tangent_inputs_np + ); + auto buf = y_dot.request(); + const realtype *ptr = reinterpret_cast(buf.ptr); + + // Arrange into 'out' array + memcpy(out, ptr, n_t * n_vars * sizeof(realtype)); + + // Release GIL + PyGILState_Release(state); +} + +void IdakluJax::cpu_idaklu_vjp(void *out_tuple, const void **in) { + int k = 1; // Start indexing at 1 to skip idaklu_jax index + const std::int64_t n_t = *reinterpret_cast(in[k++]); + const std::int64_t n_inputs = *reinterpret_cast(in[k++]); + const std::int64_t n_y_bar0 = *reinterpret_cast(in[k++]); + const std::int64_t n_y_bar1 = *reinterpret_cast(in[k++]); + const std::int64_t n_y_bar = (n_y_bar1 > 0) ? (n_y_bar0*n_y_bar1) : n_y_bar0; + const realtype *y_bar = reinterpret_cast(in[k++]); + const std::int64_t *invar = reinterpret_cast(in[k++]); + const realtype *t = reinterpret_cast(in[k++]); + realtype *inputs = new realtype(n_inputs); + for (int i = 0; i < n_inputs; i++) + inputs[i] = reinterpret_cast(in[k++])[0]; + realtype *out = reinterpret_cast(out_tuple); + + // Log + DEBUG("cpu_idaklu_vjp"); + DEBUG_n(n_t); + DEBUG_n(n_inputs); + DEBUG_n(n_y_bar0); + DEBUG_n(n_y_bar1); + DEBUG_v(y_bar, n_y_bar0*n_y_bar1); + DEBUG_v(invar, 1); + DEBUG_v(t, n_t); + DEBUG_v(inputs, n_inputs); + + // Acquire GIL (since this function is called as a capsule) + py::gil_scoped_acquire acquire; + PyGILState_STATE state = PyGILState_Ensure(); + + // Convert time vector to an np_array + py::capsule t_capsule(t, "t_capsule"); + np_array t_np = np_array({n_t}, {sizeof(realtype)}, t, t_capsule); + + // Convert y_bar to an np_array + py::capsule y_bar_capsule(y_bar, "y_bar_capsule"); + np_array y_bar_np = np_array( + {n_y_bar}, + {sizeof(realtype)}, + y_bar, + y_bar_capsule + ); + + // Convert inputs to an np_array + py::capsule in_capsule(inputs, "in_capsule"); + np_array in_np = np_array({n_inputs}, {sizeof(realtype)}, inputs, in_capsule); + + // Call VJP function in python to obtain an np_array + np_array y_dot = callback_vjp(y_bar_np, n_y_bar0, n_y_bar1, invar[0], t_np, in_np); + auto buf = y_dot.request(); + const realtype *ptr = reinterpret_cast(buf.ptr); + + // Arrange output + //memcpy(out, ptr, sizeof(realtype)); + out[0] = ptr[0]; // output is scalar + + // Release GIL + PyGILState_Release(state); +} + +template +pybind11::capsule EncapsulateFunction(T* fn) { + return pybind11::capsule(reinterpret_cast(fn), "xla._CUSTOM_CALL_TARGET"); +} + +void wrap_cpu_idaklu_eval_f64(void *out_tuple, const void **in) { + const std::int64_t index = *reinterpret_cast(in[0]); + idaklu_jax_instances[index]->cpu_idaklu_eval(out_tuple, in); +} + +void wrap_cpu_idaklu_jvp_f64(void *out_tuple, const void **in) { + const std::int64_t index = *reinterpret_cast(in[0]); + idaklu_jax_instances[index]->cpu_idaklu_jvp(out_tuple, in); +} + +void wrap_cpu_idaklu_vjp_f64(void *out_tuple, const void **in) { + const std::int64_t index = *reinterpret_cast(in[0]); + idaklu_jax_instances[index]->cpu_idaklu_vjp(out_tuple, in); +} + +pybind11::dict Registrations() { + pybind11::dict dict; + dict["cpu_idaklu_f64"] = EncapsulateFunction(wrap_cpu_idaklu_eval_f64); + dict["cpu_idaklu_jvp_f64"] = EncapsulateFunction(wrap_cpu_idaklu_jvp_f64); + dict["cpu_idaklu_vjp_f64"] = EncapsulateFunction(wrap_cpu_idaklu_vjp_f64); + return dict; +} diff --git a/pybamm/solvers/c_solvers/idaklu/idaklu_jax.hpp b/pybamm/solvers/c_solvers/idaklu/idaklu_jax.hpp new file mode 100644 index 0000000000..251567d241 --- /dev/null +++ b/pybamm/solvers/c_solvers/idaklu/idaklu_jax.hpp @@ -0,0 +1,117 @@ +#ifndef PYBAMM_IDAKLU_JAX_SOLVER_HPP +#define PYBAMM_IDAKLU_JAX_SOLVER_HPP + +#include "common.hpp" + +/** + * @brief Callback function type for JAX evaluation + */ +using CallbackEval = std::function; + +/** + * @brief Callback function type for JVP evaluation + */ +using CallbackJvp = std::function; + +/** + * @brief Callback function type for VJP evaluation + */ +using CallbackVjp = std::function; + +/** + * @brief IDAKLU-JAX interface class. + * + * This class provides an interface to the IDAKLU-JAX solver. It is called by the + * IDAKLUJax class in python and provides the lowering rules for the JAX evaluation, + * JVP and VJP primitive functions. Each of these make use of the IDAKLU solver via + * the IDAKLUSolver python class, so this IDAKLUJax class provides a wrapper which + * redirects calls back to the IDAKLUSolver via python callbacks. + */ +class IdakluJax { +private: + static std::int64_t universal_count; // Universal count for IdakluJax objects + std::int64_t index; // Instance index +public: + /** + * @brief Constructor + */ + IdakluJax(); + + /** + * @brief Destructor + */ + ~IdakluJax(); + + /** + * @brief Callback for JAX evaluation + */ + CallbackEval callback_eval; + + /** + * @brief Callback for JVP evaluation + */ + CallbackJvp callback_jvp; + + /** + * @brief Callback for VJP evaluation + */ + CallbackVjp callback_vjp; + + /** + * @brief Register callbacks for JAX evaluation, JVP and VJP + */ + void register_callback_eval(CallbackEval h); + + /** + * @brief Register callback for JAX evaluation + */ + void register_callback_jvp(CallbackJvp h); + + /** + * @brief Register callback for JVP evaluation + */ + void register_callback_vjp(CallbackVjp h); + + /** + * @brief Register callback for VJP evaluation + */ + void register_callbacks(CallbackEval h, CallbackJvp h_jvp, CallbackVjp h_vjp); + + /** + * @brief JAX evaluation primitive function + */ + void cpu_idaklu_eval(void *out_tuple, const void **in); + + /** + * @brief JVP primitive function + */ + void cpu_idaklu_jvp(void *out_tuple, const void **in); + + /** + * @brief VJP primitive function + */ + void cpu_idaklu_vjp(void *out_tuple, const void **in); + + /** + * @brief Get the instance index + */ + std::int64_t get_index() { return index; }; +}; + +/** + * @brief Non-member function to create a new IdakluJax object + */ +IdakluJax *create_idaklu_jax(); + +/** + * @brief (Non-member) encapsulation helper function + */ +template +pybind11::capsule EncapsulateFunction(T* fn); + +/** + * @brief (Non-member) function dictionary + */ +pybind11::dict Registrations(); + +#endif // PYBAMM_IDAKLU_JAX_SOLVER_HPP diff --git a/pybamm/solvers/casadi_algebraic_solver.py b/pybamm/solvers/casadi_algebraic_solver.py index ec7305906a..635adb5d34 100644 --- a/pybamm/solvers/casadi_algebraic_solver.py +++ b/pybamm/solvers/casadi_algebraic_solver.py @@ -1,6 +1,3 @@ -# -# Casadi algebraic solver class -# import casadi import pybamm import numpy as np @@ -19,7 +16,7 @@ class CasadiAlgebraicSolver(pybamm.BaseSolver): The tolerance for the solver (default is 1e-6). extra_options : dict, optional Any options to pass to the CasADi rootfinder. - Please consult `CasADi documentation `_ for + Please consult `CasADi documentation `_ for details. """ @@ -110,7 +107,7 @@ def _integrate(self, model, t_eval, inputs_dict=None): timer = pybamm.Timer() integration_time = 0 - for idx, t in enumerate(t_eval): + for _, t in enumerate(t_eval): # Solve try: timer.reset() @@ -149,11 +146,11 @@ def _integrate(self, model, t_eval, inputs_dict=None): ) else: raise pybamm.SolverError( - """ + f""" Could not find acceptable solution: solver terminated - successfully, but maximum solution error ({}) - above tolerance ({}) - """.format(casadi.mmax(casadi.fabs(fun)), self.tol) + successfully, but maximum solution error ({casadi.mmax(casadi.fabs(fun))}) + above tolerance ({self.tol}) + """ ) # Concatenate differential part diff --git a/pybamm/solvers/casadi_solver.py b/pybamm/solvers/casadi_solver.py index 02ff4a2cd9..f67a2decb4 100644 --- a/pybamm/solvers/casadi_solver.py +++ b/pybamm/solvers/casadi_solver.py @@ -1,6 +1,3 @@ -# -# CasADi Solver class -# import casadi import pybamm import numpy as np @@ -51,7 +48,7 @@ class CasadiSolver(pybamm.BaseSolver): The tolerance to assert whether extrapolation occurs or not. Default is 0. extra_options_setup : dict, optional Any options to pass to the CasADi integrator when creating the integrator. - Please consult `CasADi documentation `_ for + Please consult `CasADi documentation `_ for details. Some useful options: - "max_num_steps": Maximum number of integrator steps @@ -59,7 +56,7 @@ class CasadiSolver(pybamm.BaseSolver): extra_options_call : dict, optional Any options to pass to the CasADi integrator when calling the integrator. - Please consult `CasADi documentation `_ for + Please consult `CasADi documentation `_ for details. return_solution_if_failed_early : bool, optional Whether to return a Solution object if the solver fails to reach the end of @@ -276,7 +273,9 @@ def _integrate(self, model, t_eval, inputs_dict=None): "time steps or period of the experiment." ) if first_ts_solved and self.return_solution_if_failed_early: - warnings.warn(message, pybamm.SolverWarning) + warnings.warn( + message, pybamm.SolverWarning, stacklevel=2 + ) termination_due_to_small_dt = True break else: @@ -285,7 +284,7 @@ def _integrate(self, model, t_eval, inputs_dict=None): + " Set `return_solution_if_failed_early=True` to " "return the solution object up to the point where " "failure occured." - ) + ) from error if termination_due_to_small_dt: break # Check if the sign of an event changes, if so find an accurate @@ -360,7 +359,7 @@ def find_t_event(sol, typ): # Evaluations of the "event" function are (relatively) expensive f_eval = {} - def f(idx): + def f(idx, f_eval=f_eval, event=event): try: return f_eval[idx] except KeyError: @@ -682,7 +681,7 @@ def _run_integrator( except RuntimeError as error: # If it doesn't work raise error pybamm.logger.debug(f"Casadi integrator failed with error {error}") - raise pybamm.SolverError(error.args[0]) + raise pybamm.SolverError(error.args[0]) from error pybamm.logger.debug("Finished casadi integrator") integration_time = timer.time() # Manually add initial conditions and concatenate @@ -720,7 +719,7 @@ def _run_integrator( except RuntimeError as error: # If it doesn't work raise error pybamm.logger.debug(f"Casadi integrator failed with error {error}") - raise pybamm.SolverError(error.args[0]) + raise pybamm.SolverError(error.args[0]) from error integration_time = timer.time() x = casadi_sol["xf"] z = casadi_sol["zf"] diff --git a/pybamm/solvers/idaklu_jax.py b/pybamm/solvers/idaklu_jax.py new file mode 100644 index 0000000000..ba1c80c1c4 --- /dev/null +++ b/pybamm/solvers/idaklu_jax.py @@ -0,0 +1,1080 @@ +import pybamm +import numpy as np +import logging +import warnings +import numbers + +from typing import Union + +from functools import lru_cache + +import importlib.util +import importlib + +logger = logging.getLogger("pybamm.solvers.idaklu_jax") + +idaklu_spec = importlib.util.find_spec("pybamm.solvers.idaklu") +if idaklu_spec is not None: + try: + idaklu = importlib.util.module_from_spec(idaklu_spec) + if idaklu_spec.loader: + idaklu_spec.loader.exec_module(idaklu) + except ImportError: # pragma: no cover + idaklu_spec = None + +if pybamm.have_jax(): + import jax + from jax import lax + from jax import numpy as jnp + from jax.interpreters import ad + from jax.interpreters import mlir + from jax.interpreters import batching + from jax.interpreters.mlir import custom_call + from jax.lib import xla_client + from jax.tree_util import tree_flatten + + +class IDAKLUJax: + """JAX wrapper for IDAKLU solver + + Objects of this class should be created via an IDAKLUSolver object. + + Log information is available for this module via the named + 'pybamm.solvers.idaklu_jax' logger. + + Parameters + ---------- + solver : :class:`pybamm.IDAKLUSolver` + The IDAKLU solver object to be wrapped + """ + + def __init__( + self, + solver, + model, + t_eval, + output_variables=None, + calculate_sensitivities=True, + ): + if not pybamm.have_jax(): + raise ModuleNotFoundError( + "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-jaxsolver" + ) # pragma: no cover + if not pybamm.have_idaklu(): + raise ModuleNotFoundError( + "IDAKLU is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html" + ) # pragma: no cover + self.jaxpr = ( + None # JAX expression representing the IDAKLU-wrapped solver object + ) + self.idaklu_jax_obj = None # Low-level IDAKLU-JAX primitives object + self.solver = solver # Originating IDAKLU Solver object + self.jax_inputs = {k: [] for k, v in model.get_parameter_info().items()} + + # JAXify the solver ready for use + self.jaxify( + model, + t_eval, + output_variables=output_variables, + calculate_sensitivities=calculate_sensitivities, + ) + + def get_jaxpr(self): + """Returns a JAX expression representing the IDAKLU-wrapped solver object + + Returns + ------- + Callable + A JAX expression with the following call signature: + f(t, inputs=None) + where: + t : float | np.ndarray + Time sample or vector of time samples + inputs : dict, optional + dictionary of input values, e.g. + {'Current function [A]': 0.222, 'Separator porosity': 0.3} + """ + if self.jaxpr is None: + raise pybamm.SolverError("jaxify() must be called before get_jaxpr()") + return self.jaxpr + + def get_var( + self, + *args, + ): + """Helper function to extract a single variable + + Isolates a single variable from the model output. + Can be called on a JAX expression (which returns a JAX expression), or on a + numeric (np.ndarray) object (which returns a slice of the output). + + Example call using default JAX expression, returns a JAX expression:: + + f = idaklu_jax.get_var("Voltage [V]") + data = f(t, inputs=None) + + Example call using a custom function, returns a JAX expression:: + + f = idaklu_jax.get_var(jax.jit(f), "Voltage [V]") + data = f(t, inputs=None) + + Example call to slice a matrix, returns an np.array:: + + data = idaklu_jax.get_var( + jax.fwd(f, argnums=1)(t_eval, inputs)['Current function [A]'], + 'Voltage [V]' + ) + + Parameters + ---------- + f : Callable | np.ndarray, optional + Expression or array from which to extract the target variable + varname : str + The name of the variable to extract + + Returns + ------- + Callable + If called with a JAX expression, returns a JAX expression with the following + call signature: + + f(t, inputs=None) + + where: + t : float | np.ndarray + Time sample or vector of time samples + inputs : dict, optional + dictionary of input values, e.g. + {'Current function [A]': 0.222, 'Separator porosity': 0.3} + np.ndarray + If called with a numeric (np.ndarray) object, returns a slice of the output + corresponding to the target variable. + """ + + # Identify the call signature + if len(args) == 1: + # Called on the default JAX expression + f = self.jaxpr + varname = args[0] + elif len(args) == 2: + # Called on a custom function + f = args[0] + varname = args[1] + else: + raise ValueError("Invalid call signature") + + # Utility function to slice the output + def slice_out(out): + index = self.jax_output_variables.index(varname) + if out.ndim == 0: + return out # pragma: no cover + elif out.ndim == 1: + return out[index] + else: + return out[:, index] + + # If the jaxified expression is not a function, return a slice + if not callable(f): + return slice_out(f) + + # Otherwise, return a function that slices the output + def f_isolated(*args, **kwargs): + return slice_out(self.jaxify_f(*args, **kwargs)) + + return f_isolated + + def get_vars( + self, + *args, + ): + """Helper function to extract a list of variables + + Isolates a list of variables from the model output. + Can be called on a JAX expression (which returns a JAX expression), or on a + numeric (np.ndarray) object (which returns a slice of the output). + + Example call using default JAX expression, returns a JAX expression:: + + f = idaklu_jax.get_vars(["Voltage [V]", "Current [A]"]) + data = f(t, inputs=None) + + Example call using a custom function, returns a JAX expression:: + + f = idaklu_jax.get_vars(jax.jit(f), ["Voltage [V]", "Current [A]"]) + data = f(t, inputs=None) + + Example call to slice a matrix, returns an np.array:: + + data = idaklu_jax.get_vars( + jax.fwd(f, argnums=1)(t_eval, inputs)['Current function [A]'], + ["Voltage [V]", "Current [A]"] + ) + + Parameters + ---------- + f : Callable | np.ndarray, optional + Expression or array from which to extract the target variables + varname : list of str + The names of the variables to extract + + Returns + ------- + Callable + If called with a JAX expression, returns a JAX expression with the following + call signature: + + f(t, inputs=None) + + where: + t : float | np.ndarray + Time sample or vector of time samples + inputs : dict, optional + dictionary of input values, e.g. + {'Current function [A]': 0.222, 'Separator porosity': 0.3} + np.ndarray + If called with a numeric (np.ndarray) object, returns a slice of the output + corresponding to the target variables. + """ + + # Identify the call signature + if len(args) == 1: + # Called on the default JAX expression + f = self.jaxpr + varnames = args[0] + elif len(args) == 2: + # Called on a custom function + f = args[0] + varnames = args[1] + else: + raise ValueError("Invalid call signature") + + # Utility function to slice the output + def slice_out(out): + index = np.array( + [self.jax_output_variables.index(varname) for varname in varnames] + ) + if out.ndim == 0: + return out # pragma: no cover + elif out.ndim == 1: + return out[index] + else: + return out[:, index] + + # If the jaxified expression is not a function, return a slice + if not callable(f): + return slice_out(f) + + # Otherwise, return a function that slices the output + def f_isolated(*args, **kwargs): + return slice_out(self.jaxify_f(*args, **kwargs)) + + return f_isolated + + def jax_value( + self, + t: np.ndarray = None, + inputs: Union[dict, None] = None, + output_variables: Union[list[str], None] = None, + ): + """Helper function to compute the gradient of a jaxified expression + + Returns a numeric (np.ndarray) object (not a JAX expression). + Parameters are inferred from the base object, but can be overridden. + + Parameters + ---------- + t : float | np.ndarray + Time sample or vector of time samples + inputs : dict + dictionary of input values + output_variables : list of str, optional + The variables to be returned. If None, the variables in the model are used. + """ + if self.jaxpr is None: + raise pybamm.SolverError("jaxify() must be called before get_jaxpr()") + output_variables = ( + output_variables if output_variables else self.jax_output_variables + ) + d = {} + for outvar in output_variables: + d[outvar] = jax.vmap( + self.get_var(outvar), + in_axes=(0, None), + )(t, inputs) + return d + + def jax_grad( + self, + t: np.ndarray = None, + inputs: Union[dict, None] = None, + output_variables: Union[list[str], None] = None, + ): + """Helper function to compute the gradient of a jaxified expression + + Returns a numeric (np.ndarray) object (not a JAX expression). + Parameters are inferred from the base object, but can be overridden. + + Parameters + ---------- + t : float | np.ndarray + Time sample or vector of time samples + inputs : dict + dictionary of input values + output_variables : list of str, optional + The variables to be returned. If None, the variables in the model are used. + """ + if self.jaxpr is None: + raise pybamm.SolverError("jaxify() must be called before get_jaxpr()") + output_variables = ( + output_variables if output_variables else self.jax_output_variables + ) + d = {} + for outvar in output_variables: + d[outvar] = jax.vmap( + jax.grad( + self.get_var(outvar), + argnums=1, + ), + in_axes=(0, None), + )(t, inputs) + return d + + class _hashabledict(dict): + def __hash__(self): + return hash(tuple(sorted(self.items()))) + + @lru_cache(maxsize=1) # noqa: B019 + def _cached_solve(self, model, t_hashable, *args, **kwargs): + """Cache the last solve for reuse""" + return self.solve(model, t_hashable, *args, **kwargs) + + def _jaxify_solve(self, t, invar, *inputs_values): + """Solve the model using the IDAKLU solver + + This method is called by the JAX primitive definition and caches the last solve + fo reuse. + """ + # Reconstruct dictionary of inputs + if self.jax_inputs is None: + d = self._hashabledict() + else: + # Use hashable dictionaries for caching the solve + d = self._hashabledict() + for key, value in zip(self.jax_inputs.keys(), inputs_values): + d[key] = value + # Solver + logger.debug("_jaxify_solve:") + logger.debug(f" t_eval: {self.jax_t_eval}") + logger.debug(f" t: {t}") + logger.debug(f" invar: {invar}") + logger.debug(f" inputs: {dict(d)}") + logger.debug(f" calculate_sensitivities: {invar is not None}") + sim = IDAKLUJax._cached_solve( + self.solver, + self.jax_model, + tuple(self.jax_t_eval), + inputs=self._hashabledict(d), + calculate_sensitivities=self.jax_calculate_sensitivities, + ) + if invar is not None: + if isinstance(invar, numbers.Number): + invar = list(self.jax_inputs.keys())[invar] + # Provide vector support for time + if t.ndim == 0: + t = np.array([t]) + tk = list(map(lambda t: np.argmin(abs(self.jax_t_eval - t)), t)) + out = jnp.array( + [ + jnp.array(sim[outvar].sensitivities[invar][tk]) + for outvar in self.jax_output_variables + ] + ).squeeze() + return out.T + else: + return jnp.array( + [np.array(sim[outvar](t)) for outvar in self.jax_output_variables] + ).T + + def _jax_solve_array_inputs(self, t, inputs_array): + """Wrapper for _jax_solve used by IDAKLU callback + + This version assumes all parameters are provided as np.ndarray vectors + """ + logger.info("jax_solve_array_inputs") + logger.debug(f" t: {type(t)}, {t}") + logger.debug(f" inputs_array: {type(inputs_array)}, {inputs_array}") + inputs = tuple([k for k in inputs_array]) + logger.debug(f" inputs: {type(inputs)}, {inputs}") + return self._jax_solve(t, *inputs) + + def _jax_solve( + self, + t: Union[float, np.ndarray], + *inputs, + ) -> np.ndarray: + """Solver implementation used by f-bind""" + logger.info("jax_solve") + logger.debug(f" t: {type(t)}, {t}") + logger.debug(f" inputs: {type(inputs)}, {inputs}") + # Returns a jax array + out = self._jaxify_solve(t, None, *inputs) + # Convert to numpy array + return np.array(out) + + def _jax_jvp_impl( + self, + *args: Union[np.ndarray], + ): + """JVP implementation used by f_jvp bind""" + primals = args[: len(args) // 2] + tangents = args[len(args) // 2 :] + t = primals[0] + inputs = primals[1:] + inputs_t = tangents[1:] + + if t.ndim == 0: + y_dot = jnp.zeros_like(t) + else: + # This permits direct vector indexing with time for jacfwd + y_dot = jnp.zeros((len(t), len(self.jax_output_variables))) + for index, value in enumerate(inputs_t): + # Skipping zero values greatly improves performance + if value > 0.0: + invar = list(self.jax_inputs.keys())[index] + js = self._jaxify_solve(t, invar, *inputs) + if js.ndim == 0: + js = jnp.array([js]) + if js.ndim == 1 and t.ndim > 0: + # This permits direct vector indexing with time + js = js.reshape((t.shape[0], -1)) + y_dot += value * js + + return np.array(y_dot) + + def _jax_jvp_impl_array_inputs( + self, + primal_t, + primal_inputs, + tangent_t, + tangent_inputs, + ): + """Wrapper for JVP implementation used by IDAKLU callback + + This version assumes all parameters are provided as np.ndarray vectors + """ + primals = primal_t, *tuple([k for k in primal_inputs]) + tangents = tangent_t, *tuple([k for k in tangent_inputs]) + return self._jax_jvp_impl(*primals, *tangents) + + def _jax_vjp_impl( + self, + y_bar: np.ndarray, + invar: Union[str, int], # index or name of input variable + *primals: np.ndarray, + ): + """VJP implementation used by f_vjp bind""" + logger.info("py:f_vjp_p_impl") + logger.debug(f" py:y_bar: {type(y_bar)}, {y_bar}") + logger.debug(f" py:invar: {type(invar)}, {invar}") + logger.debug(f" py:primals: {type(primals)}, {primals}") + + t = primals[0] + inputs = primals[1:] + + if isinstance(invar, float): + invar = round(invar) + if isinstance(t, float): + t = np.array(t) + + if t.ndim == 0 or (t.ndim == 1 and t.shape[0] == 1): + # scalar time input + logger.debug("scalar time") + y_dot = jnp.zeros_like(t) + js = self._jaxify_solve(t, invar, *inputs) + if js.ndim == 0: + js = jnp.array([js]) + for index, value in enumerate(y_bar): + if value > 0.0: + y_dot += value * js[index] + else: + logger.debug("vector time") + # vector time input + js = self._jaxify_solve(t, invar, *inputs) + if len(self.jax_output_variables) == 1 and len(t) > 1: + js = np.array([js]).T + y_dot = jnp.zeros(()) + for ix, y_outvar in enumerate(y_bar.T): + y_dot += jnp.dot(y_outvar, js[:, ix]) + logger.debug(f"_jax_vjp_impl [exit]: {type(y_dot)}, {y_dot}, {y_dot.shape}") + y_dot = np.array(y_dot) + return y_dot + + def _jax_vjp_impl_array_inputs( + self, + y_bar, + y_bar_s0, + y_bar_s1, + invar, + primal_t, + primal_inputs, + ): + """Wrapper for VJP implementation used by IDAKLU callback + + This version assumes all parameters are provided as np.ndarray vectors + """ + # Reshape y_bar + logger.debug(f"Reshaping y_bar to ({y_bar_s0}, {y_bar_s1})") + y_bar = y_bar.reshape(y_bar_s0, y_bar_s1) + logger.debug(f"y_bar is now: {y_bar}") + primals = primal_t, *tuple([k for k in primal_inputs]) + return self._jax_vjp_impl(y_bar, invar, *primals) + + def _register_callbacks(self): + """Register the solve method with the IDAKLU solver""" + logger.info("_register_callbacks") + self.idaklu_jax_obj.register_callbacks( + self._jax_solve_array_inputs, + self._jax_jvp_impl_array_inputs, + self._jax_vjp_impl_array_inputs, + ) + + def _unique_name(self): + """Return a unique name for this solver object for naming the JAX primitives""" + return f"{self.idaklu_jax_obj.get_index()}" + + def jaxify( + self, + model, + t_eval, + *, + output_variables=None, + calculate_sensitivities=True, + ): + """JAXify the model and solver + + Creates a JAX expression representing the IDAKLU-wrapped solver + object. + + Parameters + ---------- + model : :class:`pybamm.BaseModel` + The model to be solved + t_eval : numeric type, optional + The times at which to compute the solution. If None, the times in the model + are used. + output_variables : list of str, optional + The variables to be returned. If None, the variables in the model are used. + calculate_sensitivities : bool, optional + Whether to calculate sensitivities. Default is True. + """ + if self.jaxpr is not None: + warnings.warn( + "JAX expression has already been created. " + "Overwriting with new expression.", + UserWarning, + stacklevel=2, + ) + self.jaxpr = self._jaxify( + model, + t_eval, + output_variables=output_variables, + calculate_sensitivities=calculate_sensitivities, + ) + return self.jaxpr + + def _jaxify( + self, + model, + t_eval, + *, + output_variables=None, + calculate_sensitivities=True, + ): + """JAXify the model and solver""" + + self.jax_model = model + self.jax_t_eval = t_eval + self.jax_output_variables = ( + output_variables if output_variables else self.solver.output_variables + ) + if not self.jax_output_variables: + raise pybamm.SolverError("output_variables must be specified") + self.jax_calculate_sensitivities = calculate_sensitivities + + self.idaklu_jax_obj = idaklu.create_idaklu_jax() # Create IDAKLU-JAX object + self._register_callbacks() # Register python methods as callbacks in IDAKLU-JAX + + for _name, _value in idaklu.registrations().items(): + xla_client.register_custom_call_target( + f"{_name}_{self._unique_name()}", _value, platform="cpu" + ) + + # --- JAX PRIMITIVE DEFINITION ------------------------------------------------ + + logger.debug(f"Creating new primitive: {self._unique_name()}") + f_p = jax.core.Primitive(f"f_{self._unique_name()}") + f_p.multiple_results = False # Returns a single multi-dimensional array + + def f(t, inputs=None): + """Main function wrapper for the JAX primitive function + + Parameters + ---------- + t : float | np.ndarray + Time sample or vector of time samples + inputs : dict, optional + dictionary of input values, e.g. + {'Current function [A]': 0.222, 'Separator porosity': 0.3} + """ + logger.info("f") + flatargs, treedef = tree_flatten((t, inputs)) + self.jax_inputs = inputs + out = f_p.bind(*flatargs) + return out + + self.jaxify_f = f + + @f_p.def_impl + def f_impl(t, *inputs): + """Concrete implementation of Primitive (used for non-jitted evaluation)""" + logger.info("f_impl") + term_v = self._jaxify_solve(t, None, *inputs) + logger.debug(f"f_impl [exit]: {type(term_v)}, {term_v}") + return term_v + + @f_p.def_abstract_eval + def f_abstract_eval(t, *inputs): + """Abstract evaluation of Primitive""" + logger.info("f_abstract_eval") + dtype = jax.dtypes.canonicalize_dtype(t.dtype) + y_aval = jax.core.ShapedArray( + (*t.shape, len(self.jax_output_variables)), dtype + ) + return y_aval + + def f_batch(args, batch_axes): + """Batch rule for Primitive + + Takes batched inputs, returns batched outputs and batched axes""" + logger.info(f"f_batch: {type(args)}, {type(batch_axes)}") + t = args[0] + inputs = args[1:] + if batch_axes[0] is not None and all([b is None for b in batch_axes[1:]]): + # Temporal batching + return jnp.stack(list(map(lambda tp: f_p.bind(tp, *inputs), t))), 0 + else: + raise NotImplementedError( + f"jaxify: batching not implemented for batch_axes = {batch_axes}" + ) + + batching.primitive_batchers[f_p] = f_batch + + def f_lowering_cpu(ctx, t, *inputs): + """CPU lowering rule for Primitive + + This function calls the IDAKLU-JAX custom call target, which reroutes the + call to the python callbacks, which call the standard IDAKLU solver. + """ + logger.info("f_lowering_cpu") + + t_aval = ctx.avals_in[0] + np_dtype = np.dtype(t_aval.dtype) + if np_dtype == np.float64: + op_name = f"cpu_idaklu_f64_{self._unique_name()}" + op_dtype = mlir.ir.F64Type.get() + else: + raise NotImplementedError( + f"Unsupported dtype {np_dtype}" + ) # pragma: no cover + + dtype_t = mlir.ir.RankedTensorType(t.type) + dims_t = dtype_t.shape + layout_t = tuple(range(len(dims_t) - 1, -1, -1)) + size_t = np.prod(dims_t).astype(np.int64) + + input_aval = ctx.avals_in[1] + dtype_input = mlir.ir.RankedTensorType.get(input_aval.shape, op_dtype) + dims_input = dtype_input.shape + layout_input = tuple(range(len(dims_input) - 1, -1, -1)) + + y_aval = ctx.avals_out[0] + dtype_out = mlir.ir.RankedTensorType.get(y_aval.shape, op_dtype) + dims_out = dtype_out.shape + layout_out = tuple(range(len(dims_out) - 1, -1, -1)) + + results = custom_call( + op_name, + # Output types + result_types=[dtype_out], + # The inputs + operands=[ + mlir.ir_constant( + self.idaklu_jax_obj.get_index() + ), # solver index reference + mlir.ir_constant(size_t), # 'size' argument + mlir.ir_constant(len(self.jax_output_variables)), # 'vars' argument + mlir.ir_constant(len(inputs)), # 'vars' argument + t, + *inputs, + ], + # Layout specification + operand_layouts=[ + (), # solver index reference + (), # 'size' + (), # 'vars' + (), # number of inputs + layout_t, # t + *([layout_input] * len(inputs)), # inputs + ], + result_layouts=[layout_out], + ) + return results.results + + mlir.register_lowering( + f_p, + f_lowering_cpu, + platform="cpu", + ) + + # --- JAX PRIMITIVE JVP DEFINITION -------------------------------------------- + + def f_jvp(primals, tangents): + """Main wrapper for the JVP function""" + logger.info("f_jvp") + + # Deal with Zero tangents + def make_zero(prim, tan): + return lax.zeros_like_array(prim) if type(tan) is ad.Zero else tan + + zero_mapped_tangents = tuple( + map(lambda pt: make_zero(pt[0], pt[1]), zip(primals, tangents)) + ) + + y = f_p.bind(*primals) + y_dot = f_jvp_p.bind( + *primals, + *zero_mapped_tangents, + ) + logger.debug(f"f_jvp [exit]: {type(y)}, {y}, {type(y_dot)}, {y_dot}") + return y, y_dot + + ad.primitive_jvps[f_p] = f_jvp + + f_jvp_p = jax.core.Primitive(f"f_jvp_{self._unique_name()}") + + @f_jvp_p.def_impl + def f_jvp_eval(*args): + """Concrete implementation of JVP primitive (for non-jitted evaluation)""" + logger.info(f"f_jvp_p_eval: {type(args)}") + return self._jax_jvp_impl(*args) + + def f_jvp_batch(args, batch_axes): + """Batch rule for JVP primitive""" + logger.info("f_jvp_batch") + primals = args[: len(args) // 2] + tangents = args[len(args) // 2 :] + batch_primals = batch_axes[: len(batch_axes) // 2] + batch_tangents = batch_axes[len(batch_axes) // 2 :] + + if ( + batch_primals[0] is not None + and all([b is None for b in batch_primals[1:]]) + and all([b is None for b in batch_tangents]) + ): + # Temporal batching (primals) only + t = primals[0] + inputs = primals[1:] + return ( + jnp.stack( + list(map(lambda tp: f_jvp_p.bind(tp, *inputs, *tangents), t)) + ), + 0, + ) + elif ( + batch_tangents[0] is not None + and all([b is None for b in batch_tangents[1:]]) + and all([b is None for b in batch_primals]) + ): + # Batch over derivates wrt time + raise NotImplementedError( + "Taking the derivative with respect to time is not supported" + ) + elif ( + batch_tangents[0] is None + and any([b is not None for b in batch_tangents[1:]]) + and all([b is None for b in batch_primals]) + ): + # Batch over (some combination of) inputs + batch_axis_indices = [ + i for i, b in enumerate(batch_tangents) if b is not None + ] + out = [] + for i in range(len(batch_axis_indices)): + tangents_item = list(tangents) + for k in range(len(batch_axis_indices)): + tangents_item[batch_axis_indices[k]] = tangents[ + batch_axis_indices[k] + ][i] + out.append(f_jvp_p.bind(*primals, *tangents_item)) + return jnp.stack(out), 0 + else: + raise NotImplementedError( + "f_jvp_batch: batching not implemented for batch_axes = " + f"{batch_axes}" + ) # pragma: no cover + + batching.primitive_batchers[f_jvp_p] = f_jvp_batch + + @f_jvp_p.def_abstract_eval + def f_jvp_abstract_eval(*args): + """Abstract evaluation of JVP primitive""" + logger.info("f_jvp_abstract_eval") + primals = args[: len(args) // 2] + t = primals[0] + out = jax.core.ShapedArray( + (*t.shape, len(self.jax_output_variables)), t.dtype + ) + logger.info("<- f_jvp_abstract_eval") + return out + + def f_jvp_transpose(y_bar, *args): + """Transpose rule for JVP primitive""" + + # Note: y_bar indexes the OUTPUT variable, e.g. [1, 0, 0] is the + # first of three outputs. The function returns primals and tangents + # corresponding to how each of the inputs derives that output, e.g. + # (..., dout/din1, dout/din2) + logger.info("f_jvp_transpose") + primals = args[: len(args) // 2] + + tangents_out = [] + for invar in self.jax_inputs.keys(): + js = f_vjp(y_bar, invar, *primals) + tangents_out.append(js) + + out = ( + None, + *([None] * len(tangents_out)), # primals + None, + *tangents_out, # tangents + ) + logger.debug("<- f_jvp_transpose") + return out + + ad.primitive_transposes[f_jvp_p] = f_jvp_transpose + + def f_jvp_lowering_cpu(ctx, *args): + """CPU lowering rule for JVP primitive""" + logger.info("f_jvp_lowering_cpu") + + primals = args[: len(args) // 2] + t_primal = primals[0] + inputs_primals = primals[1:] + + tangents = args[len(args) // 2 :] + t_tangent = tangents[0] + inputs_tangents = tangents[1:] + + t_aval = ctx.avals_in[0] + np_dtype = np.dtype(t_aval.dtype) + if np_dtype == np.float64: + op_name = f"cpu_idaklu_jvp_f64_{self._unique_name()}" + op_dtype = mlir.ir.F64Type.get() + else: + raise NotImplementedError( + f"Unsupported dtype {np_dtype}" + ) # pragma: no cover + + dtype_t = mlir.ir.RankedTensorType(t_primal.type) + dims_t = dtype_t.shape + layout_t_primal = tuple(range(len(dims_t) - 1, -1, -1)) + layout_t_tangent = layout_t_primal + size_t = np.prod(dims_t).astype(np.int64) + + input_aval = ctx.avals_in[1] + dtype_input = mlir.ir.RankedTensorType.get(input_aval.shape, op_dtype) + dims_input = dtype_input.shape + layout_inputs_primals = tuple(range(len(dims_input) - 1, -1, -1)) + layout_inputs_tangents = layout_inputs_primals + + y_aval = ctx.avals_out[0] + dtype_out = mlir.ir.RankedTensorType.get(y_aval.shape, op_dtype) + dims_out = dtype_out.shape + layout_out = tuple(range(len(dims_out) - 1, -1, -1)) + + results = custom_call( + op_name, + # Output types + result_types=[dtype_out], + # The inputs + operands=[ + mlir.ir_constant( + self.idaklu_jax_obj.get_index() + ), # solver index reference + mlir.ir_constant(size_t), # 'size' argument + mlir.ir_constant(len(self.jax_output_variables)), # 'vars' argument + mlir.ir_constant(len(inputs_primals)), # 'vars' argument + t_primal, # 't' + *inputs_primals, # inputs + t_tangent, # 't' + *inputs_tangents, # inputs + ], + # Layout specification + operand_layouts=[ + (), # solver index reference + (), # 'size' + (), # 'vars' + (), # number of inputs + layout_t_primal, # 't' + *([layout_inputs_primals] * len(inputs_primals)), # inputs + layout_t_tangent, # 't' + *([layout_inputs_tangents] * len(inputs_tangents)), # inputs + ], + result_layouts=[layout_out], + ) + return results.results + + mlir.register_lowering( + f_jvp_p, + f_jvp_lowering_cpu, + platform="cpu", + ) + + # --- JAX PRIMITIVE VJP DEFINITION -------------------------------------------- + + f_vjp_p = jax.core.Primitive(f"f_vjp_{self._unique_name()}") + + def f_vjp(y_bar, invar, *primals): + """Main wrapper for the VJP function""" + logger.info("f_vjp") + logger.debug(f" y_bar: {y_bar}, {type(y_bar)}, {y_bar.shape}") + if isinstance(invar, str): + invar = list(self.jax_inputs.keys()).index(invar) + return f_vjp_p.bind(y_bar, invar, *primals) + + @f_vjp_p.def_impl + def f_vjp_impl(y_bar, invar, *primals): + """Concrete implementation of VJP primitive (for non-jitted evaluation)""" + logger.info("f_vjp_impl") + return self._jax_vjp_impl(y_bar, invar, *primals) + + @f_vjp_p.def_abstract_eval + def f_vjp_abstract_eval(*args): + """Abstract evaluation of VJP primitive""" + logger.info("f_vjp_abstract_eval") + primals = args[: len(args) // 2] + t = primals[0] + out = jax.core.ShapedArray((), t.dtype) + logger.debug("<- f_vjp_abstract_eval") + return out + + def f_vjp_batch(args, batch_axes): + """Batch rule for VJP primitive""" + logger.info("f_vjp_p_batch") + y_bars, invar, t, *inputs = args + + if batch_axes[0] is not None and all([b is None for b in batch_axes[1:]]): + # Batch over y_bar + out = list(map(lambda yb: f_vjp(yb, invar, t, *inputs), y_bars)) + return jnp.stack(out), 0 + elif ( + batch_axes[2] is not None + and all([b is None for b in batch_axes[:2]]) + and all([b is None for b in batch_axes[3:]]) + ): + # Batch over time + out = list(map(lambda yt: f_vjp(y_bars, invar, yt, *inputs), t)) + return jnp.stack(out), 0 + else: + raise Exception( + "Batch mode not supported for batch_axes = ", batch_axes + ) # pragma: no cover + + batching.primitive_batchers[f_vjp_p] = f_vjp_batch + + def f_vjp_lowering_cpu(ctx, y_bar, invar, *primals): + """CPU lowering rule for VJP primitive""" + logger.info("f_vjp_lowering_cpu") + + t_primal = primals[0] + inputs_primals = primals[1:] + + t_aval = ctx.avals_in[2] + np_dtype = np.dtype(t_aval.dtype) + if np_dtype == np.float64: + op_name = f"cpu_idaklu_vjp_f64_{self._unique_name()}" + op_dtype = mlir.ir.F64Type.get() + else: + raise NotImplementedError( + f"Unsupported dtype {np_dtype}" + ) # pragma: no cover + + y_bar_aval = ctx.avals_in[0] + dtype_y_bar = mlir.ir.RankedTensorType.get(y_bar_aval.shape, op_dtype) + dims_y_bar = dtype_y_bar.shape + logger.debug(f" y_bar shape: {dims_y_bar}") + layout_y_bar = tuple(range(len(dims_y_bar) - 1, -1, -1)) + + invar_aval = ctx.avals_in[1] + dtype_invar = mlir.ir.RankedTensorType.get(invar_aval.shape, op_dtype) + dims_invar = dtype_invar.shape + layout_invar = tuple(range(len(dims_invar) - 1, -1, -1)) + + dtype_t = mlir.ir.RankedTensorType(t_primal.type) + dims_t = dtype_t.shape + layout_t_primal = tuple(range(len(dims_t) - 1, -1, -1)) + size_t = np.prod(dims_t).astype(np.int64) + + input_aval = ctx.avals_in[3] + dtype_input = mlir.ir.RankedTensorType.get(input_aval.shape, op_dtype) + dims_input = dtype_input.shape + layout_inputs_primals = tuple(range(len(dims_input) - 1, -1, -1)) + + y_aval = ctx.avals_out[0] + dtype_out = mlir.ir.RankedTensorType.get(y_aval.shape, op_dtype) + dims_out = dtype_out.shape + layout_out = tuple(range(len(dims_out) - 1, -1, -1)) + + results = custom_call( + op_name, + # Output types + result_types=[dtype_out], + # The inputs + operands=[ + mlir.ir_constant( + self.idaklu_jax_obj.get_index() + ), # solver index reference + mlir.ir_constant(size_t), # 'size' argument + mlir.ir_constant(len(self.jax_inputs)), # number of inputs + mlir.ir_constant(dims_y_bar[0]), # 'y_bar' shape[0] + mlir.ir_constant( # 'y_bar' shape[1] + dims_y_bar[1] if len(dims_y_bar) > 1 else -1 + ), # 'y_bar' argument + y_bar, # 'y_bar' + invar, # 'invar' + t_primal, # 't' + *inputs_primals, # inputs + ], + # Layout specification + operand_layouts=[ + (), # solver index reference + (), # 'size' + (), # number of inputs + (), # 'y_bar' shape[0] + (), # 'y_bar' shape[1] + layout_y_bar, # 'y_bar' + layout_invar, # 'invar' + layout_t_primal, # 't' + *([layout_inputs_primals] * len(inputs_primals)), # inputs + ], + result_layouts=[layout_out], + ) + return results.results + + mlir.register_lowering( + f_vjp_p, + f_vjp_lowering_cpu, + platform="cpu", + ) + + return f diff --git a/pybamm/solvers/idaklu_solver.py b/pybamm/solvers/idaklu_solver.py index 6c81bf91e7..fef4cbce3c 100644 --- a/pybamm/solvers/idaklu_solver.py +++ b/pybamm/solvers/idaklu_solver.py @@ -1,6 +1,7 @@ # # Solver class using sundials with the KLU sparse linear solver # +# mypy: ignore-errors import casadi import pybamm import numpy as np @@ -13,7 +14,8 @@ if idaklu_spec is not None: try: idaklu = importlib.util.module_from_spec(idaklu_spec) - idaklu_spec.loader.exec_module(idaklu) + if idaklu_spec.loader: + idaklu_spec.loader.exec_module(idaklu) except ImportError: # pragma: no cover idaklu_spec = None @@ -87,7 +89,7 @@ def __init__( root_method="casadi", root_tol=1e-6, extrap_tol=None, - output_variables=[], + output_variables=None, options=None, ): # set default options, @@ -110,7 +112,7 @@ def __init__( options[key] = value self._options = options - self.output_variables = output_variables + self.output_variables = [] if output_variables is None else output_variables if idaklu_spec is None: # pragma: no cover raise ImportError("KLU is not installed") @@ -647,7 +649,7 @@ def _integrate(self, model, t_eval, inputs_dict=None): number_of_samples = sol.y.shape[0] // number_of_timesteps sol.y = sol.y.reshape((number_of_timesteps, number_of_samples)) startk = 0 - for vark, var in enumerate(self.output_variables): + for _, var in enumerate(self.output_variables): # ExplicitTimeIntegral's are not computed as part of the solver and # do not need to be converted if isinstance( @@ -675,3 +677,37 @@ def _integrate(self, model, t_eval, inputs_dict=None): return newsol else: raise pybamm.SolverError("idaklu solver failed") + + def jaxify( + self, + model, + t_eval, + *, + output_variables=None, + calculate_sensitivities=True, + ): + """JAXify the solver object + + Creates a JAX expression representing the IDAKLU-wrapped solver + object. + + Parameters + ---------- + model : :class:`pybamm.BaseModel` + The model to be solved + t_eval : numeric type, optional + The times at which to compute the solution. If None, the times in the model + are used. + output_variables : list of str, optional + The variables to be returned. If None, all variables in the model are used. + calculate_sensitivities : bool, optional + Whether to calculate sensitivities. Default is True. + """ + obj = pybamm.IDAKLUJax( + self, # IDAKLU solver instance + model, + t_eval, + output_variables=output_variables, + calculate_sensitivities=calculate_sensitivities, + ) + return obj diff --git a/pybamm/solvers/jax_bdf_solver.py b/pybamm/solvers/jax_bdf_solver.py index df6714e52a..2c7bdc6d17 100644 --- a/pybamm/solvers/jax_bdf_solver.py +++ b/pybamm/solvers/jax_bdf_solver.py @@ -1,3 +1,4 @@ +# mypy: ignore-errors import collections import operator as op from functools import partial diff --git a/pybamm/solvers/jax_solver.py b/pybamm/solvers/jax_solver.py index 6c89bed4dd..fbe047b3cc 100644 --- a/pybamm/solvers/jax_solver.py +++ b/pybamm/solvers/jax_solver.py @@ -30,9 +30,9 @@ class JaxSolver(pybamm.BaseSolver): Parameters ---------- - method: str - 'RK45' (default) uses jax.experimental.odeint - 'BDF' uses custom jax_bdf_integrate (see jax_bdf_integrate.py for details) + method: str, optional (see `jax.experimental.ode.odeint` for details) + * 'RK45' (default) uses jax.experimental.ode.odeint + * 'BDF' uses custom jax_bdf_integrate (see `jax_bdf_integrate.py` for details) root_method: str, optional Method to use to calculate consistent initial conditions. By default this uses the newton chord method internal to the jax bdf solver, otherwise choose from diff --git a/pybamm/solvers/processed_variable.py b/pybamm/solvers/processed_variable.py index d33d6894dd..38314ca5c2 100644 --- a/pybamm/solvers/processed_variable.py +++ b/pybamm/solvers/processed_variable.py @@ -110,6 +110,9 @@ def __init__( + "(note processing of 3D variables is not yet implemented)" ) + # xr_data_array is initialized when needed + self._xr_data_array = None + def initialise_0D(self): # initialise empty array of the correct size entries = np.empty(len(self.t_pts)) @@ -130,8 +133,9 @@ def initialise_0D(self): entries, self.t_pts, initial=float(self.cumtrapz_ic) ) - # set up interpolation - self._xr_data_array = xr.DataArray(entries, coords=[("t", self.t_pts)]) + # save attributes for interpolation + self.entries_for_interp = entries + self.coords_for_interp = {"t": self.t_pts} self.entries = entries self.dimensions = 0 @@ -185,11 +189,9 @@ def initialise_1D(self, fixed_t=False): # Set first_dim_pts to edges for nicer plotting self.first_dim_pts = edges - # set up interpolation - self._xr_data_array = xr.DataArray( - entries_for_interp, - coords=[(self.first_dimension, pts_for_interp), ("t", self.t_pts)], - ) + # save attributes for interpolation + self.entries_for_interp = entries_for_interp + self.coords_for_interp = {self.first_dimension: pts_for_interp, "t": self.t_pts} def initialise_2D(self): """ @@ -289,15 +291,13 @@ def initialise_2D(self): self.first_dim_pts = first_dim_edges self.second_dim_pts = second_dim_edges - # set up interpolation - self._xr_data_array = xr.DataArray( - entries_for_interp, - coords={ - self.first_dimension: first_dim_pts_for_interp, - self.second_dimension: second_dim_pts_for_interp, - "t": self.t_pts, - }, - ) + # save attributes for interpolation + self.entries_for_interp = entries_for_interp + self.coords_for_interp = { + self.first_dimension: first_dim_pts_for_interp, + self.second_dimension: second_dim_pts_for_interp, + "t": self.t_pts, + } def initialise_2D_scikit_fem(self): y_sol = self.mesh.edges["y"] @@ -331,11 +331,9 @@ def initialise_2D_scikit_fem(self): self.first_dim_pts = y_sol self.second_dim_pts = z_sol - # set up interpolation - self._xr_data_array = xr.DataArray( - entries, - coords={"y": y_sol, "z": z_sol, "t": self.t_pts}, - ) + # save attributes for interpolation + self.entries_for_interp = entries + self.coords_for_interp = {"y": y_sol, "z": z_sol, "t": self.t_pts} def _process_spatial_variable_names(self, spatial_variable): if len(spatial_variable) == 0: @@ -366,11 +364,23 @@ def _process_spatial_variable_names(self, spatial_variable): f"Spatial variable name not recognized for {spatial_variable}" ) + def _initialize_xr_data_array(self): + """ + Initialize the xarray DataArray for interpolation. We don't do this by + default as it has some overhead (~75 us) and sometimes we only need the entries + of the processed variable, not the xarray object for interpolation. + """ + entries = self.entries_for_interp + coords = self.coords_for_interp + self._xr_data_array = xr.DataArray(entries, coords=coords) + def __call__(self, t=None, x=None, r=None, y=None, z=None, R=None, warn=True): """ Evaluate the variable at arbitrary *dimensional* t (and x, r, y, z and/or R), using interpolation """ + if self._xr_data_array is None: + self._initialize_xr_data_array() kwargs = {"t": t, "x": x, "r": r, "y": y, "z": z, "R": R} # Remove any None arguments kwargs = {key: value for key, value in kwargs.items() if value is not None} diff --git a/pybamm/solvers/processed_variable_computed.py b/pybamm/solvers/processed_variable_computed.py index fd17dfab7b..a069342254 100644 --- a/pybamm/solvers/processed_variable_computed.py +++ b/pybamm/solvers/processed_variable_computed.py @@ -149,8 +149,10 @@ def unroll_1D(self, realdata=None): .transpose() ) - def unroll_2D(self, realdata=None, n_dim1=None, n_dim2=None, axis_swaps=[]): + def unroll_2D(self, realdata=None, n_dim1=None, n_dim2=None, axis_swaps=None): # initialise settings on first run + if axis_swaps is None: + axis_swaps = [] if not self.unroll_params: self.unroll_params["n_dim1"] = n_dim1 self.unroll_params["n_dim2"] = n_dim2 diff --git a/pybamm/solvers/scikits_dae_solver.py b/pybamm/solvers/scikits_dae_solver.py deleted file mode 100644 index a5bf1e5a4f..0000000000 --- a/pybamm/solvers/scikits_dae_solver.py +++ /dev/null @@ -1,182 +0,0 @@ -# -# Solver class using Scipy's adaptive time stepper -# -import casadi -import pybamm - -import numpy as np -import importlib -import scipy.sparse as sparse - -scikits_odes_spec = importlib.util.find_spec("scikits") -if scikits_odes_spec is not None: - scikits_odes_spec = importlib.util.find_spec("scikits.odes") - if scikits_odes_spec is not None: - scikits_odes = importlib.util.module_from_spec(scikits_odes_spec) - scikits_odes_spec.loader.exec_module(scikits_odes) - - -class ScikitsDaeSolver(pybamm.BaseSolver): - """Solve a discretised model, using scikits.odes. - - Parameters - ---------- - method : str, optional - The method to use in solve_ivp (default is "BDF") - rtol : float, optional - The relative tolerance for the solver (default is 1e-6). - atol : float, optional - The absolute tolerance for the solver (default is 1e-6). - root_method : str or pybamm algebraic solver class, optional - The method to use to find initial conditions (for DAE solvers). - If a solver class, must be an algebraic solver class. - If "casadi", - the solver uses casadi's Newton rootfinding algorithm to find initial - conditions. Otherwise, the solver uses 'scipy.optimize.root' with method - specified by 'root_method' (e.g. "lm", "hybr", ...) - root_tol : float, optional - The tolerance for the initial-condition solver (default is 1e-6). - extrap_tol : float, optional - The tolerance to assert whether extrapolation occurs or not (default is 0). - extra_options : dict, optional - Any options to pass to the solver. - Please consult `scikits.odes documentation - `_ for details. - Some common keys: - - - 'max_steps': maximum (int) number of steps the solver can take - """ - - def __init__( - self, - method="ida", - rtol=1e-6, - atol=1e-6, - root_method="casadi", - root_tol=1e-6, - extrap_tol=None, - extra_options=None, - ): - if scikits_odes_spec is None: - raise ImportError("scikits.odes is not installed") - - super().__init__(method, rtol, atol, root_method, root_tol, extrap_tol) - self.name = f"Scikits DAE solver ({method})" - - self.extra_options = extra_options or {} - - pybamm.citations.register("Malengier2018") - pybamm.citations.register("Hindmarsh2000") - pybamm.citations.register("Hindmarsh2005") - - def _integrate(self, model, t_eval, inputs_dict=None): - """ - Solve a model defined by dydt with initial conditions y0. - - Parameters - ---------- - model : :class:`pybamm.BaseModel` - The model whose solution to calculate. - t_eval : numeric type - The times at which to compute the solution - inputs_dict : dict, optional - Any input parameters to pass to the model when solving - - """ - inputs_dict = inputs_dict or {} - if model.convert_to_format == "casadi": - inputs = casadi.vertcat(*[x for x in inputs_dict.values()]) - else: - inputs = inputs_dict - - y0 = model.y0 - if isinstance(y0, casadi.DM): - y0 = y0.full() - y0 = y0.flatten() - - rhs_algebraic_eval = model.rhs_algebraic_eval - events = model.terminate_events_eval - jacobian = model.jac_rhs_algebraic_eval - if model.convert_to_format == "jax": - mass_matrix = model.mass_matrix.entries.toarray() - else: - mass_matrix = model.mass_matrix.entries - - if model.convert_to_format == "casadi": - - def eqsres(t, y, ydot, return_residuals): - return_residuals[:] = ( - rhs_algebraic_eval(t, y, inputs).full().flatten() - - mass_matrix @ ydot - ) - - else: - - def eqsres(t, y, ydot, return_residuals): - return_residuals[:] = ( - rhs_algebraic_eval(t, y, inputs).flatten() - mass_matrix @ ydot - ) - - def rootfn(t, y, ydot, return_root): - return_root[:] = [float(event(t, y, inputs)) for event in events] - - extra_options = { - **self.extra_options, - "old_api": False, - "rtol": self.rtol, - "atol": self.atol, - } - - if jacobian: - jac_y0_t0 = jacobian(t_eval[0], y0, inputs) - if sparse.issparse(jac_y0_t0): - - def jacfn(t, y, ydot, residuals, cj, J): - jac_eval = jacobian(t, y, inputs) - cj * mass_matrix - J[:][:] = jac_eval.toarray() - - else: - - def jacfn(t, y, ydot, residuals, cj, J): - jac_eval = jacobian(t, y, inputs) - cj * mass_matrix - J[:][:] = jac_eval - - extra_options.update({"jacfn": jacfn}) - - if events: - extra_options.update({"rootfn": rootfn, "nr_rootfns": len(events)}) - - # solver works with ydot0 set to zero - ydot0 = np.zeros_like(y0) - - # set up and solve - dae_solver = scikits_odes.dae(self.method, eqsres, **extra_options) - timer = pybamm.Timer() - sol = dae_solver.solve(t_eval, y0, ydot0) - integration_time = timer.time() - - # return solution, we need to tranpose y to match scipy's interface - if sol.flag in [0, 2]: - # 0 = solved for all t_eval - if sol.flag == 0: - termination = "final time" - # 2 = found root(s) - elif sol.flag == 2: - termination = "event" - if sol.roots.t is None: - t_root = None - else: - t_root = sol.roots.t - sol = pybamm.Solution( - sol.values.t, - np.transpose(sol.values.y), - model, - inputs_dict, - t_root, - np.transpose(sol.roots.y), - termination, - ) - sol.integration_time = integration_time - return sol - else: - raise pybamm.SolverError(sol.message) diff --git a/pybamm/solvers/scikits_ode_solver.py b/pybamm/solvers/scikits_ode_solver.py deleted file mode 100644 index 9f5ee67604..0000000000 --- a/pybamm/solvers/scikits_ode_solver.py +++ /dev/null @@ -1,186 +0,0 @@ -# -# Solver class using Scipy's adaptive time stepper -# -import casadi -import pybamm - -import numpy as np -import importlib -import scipy.sparse as sparse - -scikits_odes_spec = importlib.util.find_spec("scikits") -if scikits_odes_spec is not None: - scikits_odes_spec = importlib.util.find_spec("scikits.odes") - if scikits_odes_spec is not None: - scikits_odes = importlib.util.module_from_spec(scikits_odes_spec) - scikits_odes_spec.loader.exec_module(scikits_odes) - - -def have_scikits_odes(): - return scikits_odes_spec is not None - - -class ScikitsOdeSolver(pybamm.BaseSolver): - """Solve a discretised model, using scikits.odes. - - Parameters - ---------- - method : str, optional - The method to use in solve_ivp (default is "BDF") - rtol : float, optional - The relative tolerance for the solver (default is 1e-6). - atol : float, optional - The absolute tolerance for the solver (default is 1e-6). - extrap_tol : float, optional - The tolerance to assert whether extrapolation occurs or not (default is 0). - extra_options : dict, optional - Any options to pass to the solver. - Please consult `scikits.odes documentation - `_ for details. - Some common keys: - - - 'linsolver': can be 'dense' (= default), 'lapackdense', 'spgmr', 'spbcgs', \ - 'sptfqmr' - """ - - def __init__( - self, - method="cvode", - rtol=1e-6, - atol=1e-6, - extrap_tol=None, - extra_options=None, - ): - if scikits_odes_spec is None: # pragma: no cover - raise ImportError("scikits.odes is not installed") - - super().__init__(method, rtol, atol, extrap_tol=extrap_tol) - self.extra_options = extra_options or {} - self.ode_solver = True - self.name = f"Scikits ODE solver ({method})" - - pybamm.citations.register("Malengier2018") - pybamm.citations.register("Hindmarsh2000") - pybamm.citations.register("Hindmarsh2005") - - def _integrate(self, model, t_eval, inputs_dict=None): - """ - Solve a model defined by dydt with initial conditions y0. - - Parameters - ---------- - model : :class:`pybamm.BaseModel` - The model whose solution to calculate. - t_eval : numeric type - The times at which to compute the solution - inputs_dict : dict, optional - Any input parameters to pass to the model when solving - - """ - inputs_dict = inputs_dict or {} - if model.convert_to_format == "casadi": - inputs = casadi.vertcat(*[x for x in inputs_dict.values()]) - else: - inputs = inputs_dict - - y0 = model.y0 - if isinstance(y0, casadi.DM): - y0 = y0.full() - y0 = y0.flatten() - - derivs = model.rhs_eval - events = model.terminate_events_eval - jacobian = model.jac_rhs_eval - - if model.convert_to_format == "casadi": - - def eqsydot(t, y, return_ydot): - return_ydot[:] = derivs(t, y, inputs).full().flatten() - - else: - - def eqsydot(t, y, return_ydot): - return_ydot[:] = derivs(t, y, inputs).flatten() - - def rootfn(t, y, return_root): - return_root[:] = [float(event(t, y, inputs)) for event in events] - - if jacobian: - jac_y0_t0 = jacobian(t_eval[0], y0, inputs) - if sparse.issparse(jac_y0_t0): - - def jacfn(t, y, fy, J): - J[:][:] = jacobian(t, y, inputs).toarray() - - def jac_times_vecfn(v, Jv, t, y, userdata): - Jv[:] = userdata._jac_eval * v - return 0 - - else: - - def jacfn(t, y, fy, J): - J[:][:] = jacobian(t, y, inputs) - - def jac_times_vecfn(v, Jv, t, y, userdata): - Jv[:] = np.matmul(userdata._jac_eval, v) - return 0 - - def jac_times_setupfn(t, y, fy, userdata): - userdata._jac_eval = jacobian(t, y, inputs) - return 0 - - extra_options = { - **self.extra_options, - "old_api": False, - "rtol": self.rtol, - "atol": self.atol, - } - - # Read linsolver (defaults to dense) - linsolver = extra_options.get("linsolver", "dense") - - if jacobian: - if linsolver in ("dense", "lapackdense"): - extra_options.update({"jacfn": jacfn}) - elif linsolver in ("spgmr", "spbcgs", "sptfqmr"): - extra_options.update( - { - "jac_times_setupfn": jac_times_setupfn, - "jac_times_vecfn": jac_times_vecfn, - "user_data": self, - } - ) - - if events: - extra_options.update({"rootfn": rootfn, "nr_rootfns": len(events)}) - - ode_solver = scikits_odes.ode(self.method, eqsydot, **extra_options) - timer = pybamm.Timer() - sol = ode_solver.solve(t_eval, y0) - integration_time = timer.time() - - # return solution, we need to tranpose y to match scipy's ivp interface - if sol.flag in [0, 2]: - # 0 = solved for all t_eval - if sol.flag == 0: - termination = "final time" - # 2 = found root(s) - elif sol.flag == 2: - termination = "event" - if sol.roots.t is None: - t_root = None - else: - t_root = sol.roots.t - sol = pybamm.Solution( - sol.values.t, - np.transpose(sol.values.y), - model, - inputs_dict, - t_root, - np.transpose(sol.roots.y), - termination, - ) - sol.integration_time = integration_time - return sol - else: - raise pybamm.SolverError(sol.message) diff --git a/pybamm/solvers/solution.py b/pybamm/solvers/solution.py index 90712960cc..39281ef4b0 100644 --- a/pybamm/solvers/solution.py +++ b/pybamm/solvers/solution.py @@ -14,8 +14,9 @@ class NumpyEncoder(json.JSONEncoder): """ - Numpy serialiser helper class that converts numpy arrays to a list - https://stackoverflow.com/questions/26646362/numpy-array-is-not-json-serializable + Numpy serialiser helper class that converts numpy arrays to a list. + Numpy arrays cannot be directly converted to JSON, so the arrays are + converted to python list objects before encoding. """ def default(self, obj): @@ -297,11 +298,11 @@ def set_y(self): self._y = casadi.horzcat(*self.all_ys) else: self._y = np.hstack(self.all_ys) - except ValueError: + except ValueError as error: raise pybamm.SolverError( "The solution is made up from different models, so `y` cannot be " "computed explicitly." - ) + ) from error def check_ys_are_not_too_large(self): # Only check last one so that it doesn't take too long @@ -478,7 +479,15 @@ def update(self, variables): for i, (model, ys, inputs, var_pybamm) in enumerate( zip(self.all_models, self.all_ys, self.all_inputs, vars_pybamm) ): - if isinstance(var_pybamm, pybamm.ExplicitTimeIntegral): + if ys.size == 0 and var_pybamm.has_symbol_of_classes( + pybamm.expression_tree.state_vector.StateVector + ): + raise KeyError( + f"Cannot process variable '{key}' as it was not part of the " + "solve. Please re-run the solve with `output_variables` set to " + "include this variable." + ) + elif isinstance(var_pybamm, pybamm.ExplicitTimeIntegral): cumtrapz_ic = var_pybamm.initial_condition cumtrapz_ic = cumtrapz_ic.evaluate() var_pybamm = var_pybamm.child @@ -702,9 +711,7 @@ def save_data( for name, var in data.items(): if var.ndim >= 2: raise ValueError( - "only 0D variables can be saved to csv, but '{}' is {}D".format( - name, var.ndim - 1 - ) + f"only 0D variables can be saved to csv, but '{name}' is {var.ndim - 1}D" ) df = pd.DataFrame(data) return df.to_csv(filename, index=False) @@ -801,6 +808,43 @@ def copy(self): return new_sol + def plot_voltage_components( + self, + ax=None, + show_legend=True, + split_by_electrode=False, + show_plot=True, + **kwargs_fill, + ): + """ + Generate a plot showing the component overpotentials that make up the voltage + + Parameters + ---------- + ax : matplotlib Axis, optional + The axis on which to put the plot. If None, a new figure and axis is created. + show_legend : bool, optional + Whether to display the legend. Default is True. + split_by_electrode : bool, optional + Whether to show the overpotentials for the negative and positive electrodes + separately. Default is False. + show_plot : bool, optional + Whether to show the plots. Default is True. Set to False if you want to + only display the plot after plt.show() has been called. + kwargs_fill + Keyword arguments, passed to ax.fill_between. + + """ + # Use 'self' here as the solution object + return pybamm.plot_voltage_components( + self, + ax=ax, + show_legend=show_legend, + split_by_electrode=split_by_electrode, + show_plot=show_plot, + **kwargs_fill, + ) + class EmptySolution: def __init__(self, termination=None, t=None): @@ -824,7 +868,9 @@ def copy(self): return EmptySolution(termination=self.termination, t=self.t) -def make_cycle_solution(step_solutions, esoh_solver=None, save_this_cycle=True): +def make_cycle_solution( + step_solutions, esoh_solver=None, save_this_cycle=True, inputs=None +): """ Function to create a Solution for an entire cycle, and associated summary variables @@ -870,7 +916,9 @@ def make_cycle_solution(step_solutions, esoh_solver=None, save_this_cycle=True): cycle_solution.steps = step_solutions - cycle_summary_variables = _get_cycle_summary_variables(cycle_solution, esoh_solver) + cycle_summary_variables = _get_cycle_summary_variables( + cycle_solution, esoh_solver, user_inputs=inputs + ) cycle_first_state = cycle_solution.first_state @@ -882,37 +930,16 @@ def make_cycle_solution(step_solutions, esoh_solver=None, save_this_cycle=True): return cycle_solution, cycle_summary_variables, cycle_first_state -def _get_cycle_summary_variables(cycle_solution, esoh_solver): +def _get_cycle_summary_variables(cycle_solution, esoh_solver, user_inputs=None): + user_inputs = user_inputs or {} model = cycle_solution.all_models[0] cycle_summary_variables = pybamm.FuzzyDict({}) - # Measured capacity variables - if "Discharge capacity [A.h]" in model.variables: - Q = cycle_solution["Discharge capacity [A.h]"].data - min_Q, max_Q = np.min(Q), np.max(Q) - - cycle_summary_variables.update( - { - "Minimum measured discharge capacity [A.h]": min_Q, - "Maximum measured discharge capacity [A.h]": max_Q, - "Measured capacity [A.h]": max_Q - min_Q, - } - ) - - # Voltage variables - if "Battery voltage [V]" in model.variables: - V = cycle_solution["Battery voltage [V]"].data - min_V, max_V = np.min(V), np.max(V) - - cycle_summary_variables.update( - {"Minimum voltage [V]": min_V, "Maximum voltage [V]": max_V} - ) - - # Degradation variables - degradation_variables = model.summary_variables + # Summary variables + summary_variables = model.summary_variables first_state = cycle_solution.first_state last_state = cycle_solution.last_state - for var in degradation_variables: + for var in summary_variables: data_first = first_state[var].data data_last = last_state[var].data cycle_summary_variables[var] = data_last[0] @@ -926,20 +953,20 @@ def _get_cycle_summary_variables(cycle_solution, esoh_solver): esoh_solver is not None and isinstance(model, pybamm.lithium_ion.BaseModel) and model.options.electrode_types["negative"] == "porous" + and "Negative electrode capacity [A.h]" in model.variables + and "Positive electrode capacity [A.h]" in model.variables ): Q_n = last_state["Negative electrode capacity [A.h]"].data[0] Q_p = last_state["Positive electrode capacity [A.h]"].data[0] Q_Li = last_state["Total lithium capacity in particles [A.h]"].data[0] - - inputs = {"Q_n": Q_n, "Q_p": Q_p, "Q_Li": Q_Li} - + all_inputs = {**user_inputs, "Q_n": Q_n, "Q_p": Q_p, "Q_Li": Q_Li} try: - esoh_sol = esoh_solver.solve(inputs) - except pybamm.SolverError: # pragma: no cover + esoh_sol = esoh_solver.solve(inputs=all_inputs) + except pybamm.SolverError as error: # pragma: no cover raise pybamm.SolverError( "Could not solve for summary variables, run " "`sim.solve(calc_esoh=False)` to skip this step" - ) + ) from error cycle_summary_variables.update(esoh_sol) diff --git a/pybamm/spatial_methods/__init__.py b/pybamm/spatial_methods/__init__.py index e69de29bb2..56b9988536 100644 --- a/pybamm/spatial_methods/__init__.py +++ b/pybamm/spatial_methods/__init__.py @@ -0,0 +1,2 @@ +__all__ = ['finite_volume', 'scikit_finite_element', 'spatial_method', + 'spectral_volume', 'zero_dimensional_method'] diff --git a/pybamm/spatial_methods/finite_volume.py b/pybamm/spatial_methods/finite_volume.py index 11313a1450..5aaf7ea123 100644 --- a/pybamm/spatial_methods/finite_volume.py +++ b/pybamm/spatial_methods/finite_volume.py @@ -613,8 +613,13 @@ def add_ghost_nodes(self, symbol, discretised_symbol, bcs): n = submesh.npts second_dim_repeats = self._get_auxiliary_domain_repeats(symbol.domains) - lbc_value, lbc_type = bcs["left"] - rbc_value, rbc_type = bcs["right"] + # Catch if no boundary conditions are defined + if "left" not in bcs.keys() and "right" not in bcs.keys(): + raise ValueError(f"No boundary conditions have been provided for {symbol}") + + # Allow to only pass one boundary condition (for upwind/downwind) + lbc_value, lbc_type = bcs.get("left", (None, None)) + rbc_value, rbc_type = bcs.get("right", (None, None)) # Add ghost node(s) to domain where necessary and count number of # Dirichlet boundary conditions @@ -637,7 +642,7 @@ def add_ghost_nodes(self, symbol, discretised_symbol, bcs): else: left_ghost_constant = 2 * lbc_value lbc_vector = pybamm.Matrix(lbc_matrix) @ left_ghost_constant - elif lbc_type == "Neumann": + elif lbc_type in ["Neumann", None]: lbc_vector = pybamm.Vector(np.zeros((n + n_bcs) * second_dim_repeats)) else: raise ValueError( @@ -656,7 +661,7 @@ def add_ghost_nodes(self, symbol, discretised_symbol, bcs): else: right_ghost_constant = 2 * rbc_value rbc_vector = pybamm.Matrix(rbc_matrix) @ right_ghost_constant - elif rbc_type == "Neumann": + elif rbc_type in ["Neumann", None]: rbc_vector = pybamm.Vector(np.zeros((n + n_bcs) * second_dim_repeats)) else: raise ValueError( @@ -1116,9 +1121,7 @@ def process_binary_operators(self, bin_op, left, right, disc_left, disc_right): method = "arithmetic" disc_left = self.node_to_edge(disc_left, method=method) # Return new binary operator with appropriate class - out = pybamm.simplify_if_constant( - bin_op._binary_new_copy(disc_left, disc_right) - ) + out = pybamm.simplify_if_constant(bin_op.create_copy([disc_left, disc_right])) return out @@ -1390,8 +1393,6 @@ def upwind_or_downwind(self, symbol, discretised_symbol, bcs, direction): direction : str Direction in which to apply the operator (upwind or downwind) """ - submesh = self.mesh[symbol.domain] - n = submesh.npts if symbol not in bcs: raise pybamm.ModelError( @@ -1399,36 +1400,17 @@ def upwind_or_downwind(self, symbol, discretised_symbol, bcs, direction): ) if direction == "upwind": - bc, typ = bcs[symbol]["left"] - if typ != "Dirichlet": - raise pybamm.ModelError( - "Dirichlet boundary conditions must be provided for " - f"upwinding '{symbol}'" - ) - - concat_bc = pybamm.NumpyConcatenation(bc, discretised_symbol) - - upwind_mat = vstack( - [ - csr_matrix(([1], ([0], [0])), shape=(1, n + 1)), - diags([-0.5, 1.5], [0, 1], shape=(n, n + 1)), - ] - ) - symbol_out = pybamm.Matrix(upwind_mat) @ concat_bc + bc_side = "left" elif direction == "downwind": - bc, typ = bcs[symbol]["right"] - if typ != "Dirichlet": - raise pybamm.ModelError( - "Dirichlet boundary conditions must be provided for " - f"downwinding '{symbol}'" - ) + bc_side = "right" - concat_bc = pybamm.NumpyConcatenation(discretised_symbol, bc) - downwind_mat = vstack( - [ - diags([1.5, -0.5], [0, 1], shape=(n, n + 1)), - csr_matrix(([1], ([0], [n])), shape=(1, n + 1)), - ] + if bcs[symbol][bc_side][1] != "Dirichlet": + raise pybamm.ModelError( + "Dirichlet boundary conditions must be provided for " + f"{direction}ing '{symbol}'" ) - symbol_out = pybamm.Matrix(downwind_mat) @ concat_bc + + # Extract only the relevant boundary condition as the model might have both + bc_subset = {bc_side: bcs[symbol][bc_side]} + symbol_out, _ = self.add_ghost_nodes(symbol, discretised_symbol, bc_subset) return symbol_out diff --git a/pybamm/spatial_methods/scikit_finite_element.py b/pybamm/spatial_methods/scikit_finite_element.py index 07a3c0e1be..23d30e93eb 100644 --- a/pybamm/spatial_methods/scikit_finite_element.py +++ b/pybamm/spatial_methods/scikit_finite_element.py @@ -7,7 +7,7 @@ from scipy.sparse.linalg import inv import numpy as np -from pybamm.util import have_optional_dependency +from pybamm.util import import_optional_dependency class ScikitFiniteElement(pybamm.SpatialMethod): @@ -18,12 +18,7 @@ class ScikitFiniteElement(pybamm.SpatialMethod): solving the Poisson problem -grad^2 u = f in the y-z plane (i.e. not the through-cell direction). - For broadcast we follow the default behaviour from SpatialMethod. - - Parameters - ---------- - mesh : :class:`pybamm.Mesh` - Contains all the submeshes for discretisation + For broadcast, we follow the default behaviour from SpatialMethod. """ def __init__(self, options=None): @@ -88,7 +83,7 @@ def gradient(self, symbol, discretised_symbol, boundary_conditions): to the y-component of the gradient and the second column corresponds to the z component of the gradient. """ - skfem = have_optional_dependency("skfem") + skfem = import_optional_dependency("skfem") domain = symbol.domain[0] mesh = self.mesh[domain] @@ -144,7 +139,7 @@ def gradient_matrix(self, symbol, boundary_conditions): :class:`pybamm.Matrix` The (sparse) finite element gradient matrix for the domain """ - skfem = have_optional_dependency("skfem") + skfem = import_optional_dependency("skfem") # get primary domain mesh domain = symbol.domain[0] mesh = self.mesh[domain] @@ -190,7 +185,7 @@ def laplacian(self, symbol, discretised_symbol, boundary_conditions): Contains the result of acting the discretised gradient on the child discretised_symbol """ - skfem = have_optional_dependency("skfem") + skfem = import_optional_dependency("skfem") domain = symbol.domain[0] mesh = self.mesh[domain] @@ -258,7 +253,7 @@ def stiffness_matrix(self, symbol, boundary_conditions): :class:`pybamm.Matrix` The (sparse) finite element stiffness matrix for the domain """ - skfem = have_optional_dependency("skfem") + skfem = import_optional_dependency("skfem") # get primary domain mesh domain = symbol.domain[0] mesh = self.mesh[domain] @@ -275,10 +270,10 @@ def stiffness_form(u, v, w): try: _, neg_bc_type = boundary_conditions[symbol]["negative tab"] _, pos_bc_type = boundary_conditions[symbol]["positive tab"] - except KeyError: + except KeyError as error: raise pybamm.ModelError( f"No boundary conditions provided for symbol `{symbol}``" - ) + ) from error # adjust matrix for Dirichlet boundary conditions if neg_bc_type == "Dirichlet": @@ -305,9 +300,9 @@ def definite_integral_matrix(self, child, vector_type="row"): the entire domain .. math:: - I = \\int_{\Omega}\\!f(s)\\,dx + I = \\int_{\\Omega}\\!f(s)\\,dx - for where :math:`\Omega` is the domain. + for where :math:`\\Omega` is the domain. Parameters ---------- @@ -321,7 +316,7 @@ def definite_integral_matrix(self, child, vector_type="row"): :class:`pybamm.Matrix` The finite element integral vector for the domain """ - skfem = have_optional_dependency("skfem") + skfem = import_optional_dependency("skfem") # get primary domain mesh domain = child.domain[0] mesh = self.mesh[domain] @@ -383,7 +378,7 @@ def boundary_integral_vector(self, domain, region): :class:`pybamm.Matrix` The finite element integral vector for the domain """ - skfem = have_optional_dependency("skfem") + skfem = import_optional_dependency("skfem") # get primary domain mesh mesh = self.mesh[domain[0]] @@ -501,7 +496,7 @@ def assemble_mass_form(self, symbol, boundary_conditions, region="interior"): :class:`pybamm.Matrix` The (sparse) mass matrix for the spatial method. """ - skfem = have_optional_dependency("skfem") + skfem = import_optional_dependency("skfem") # get primary domain mesh domain = symbol.domain[0] mesh = self.mesh[domain] @@ -533,14 +528,14 @@ def mass_form(u, v, w): def bc_apply(self, M, boundary, zero=False): """ - Adjusts the assemled finite element matrices to account for boundary conditons. + Adjusts the assembled finite element matrices to account for boundary conditions. Parameters ---------- M: :class:`scipy.sparse.coo_matrix` - The assemled finite element matrix to adjust. + The assembled finite element matrix to adjust. boundary: :class:`numpy.array` - Array of the indicies which correspond to the boundary. + Array of the indices which correspond to the boundary. zero: bool, optional If True, the rows of M given by the indicies in boundary are set to zero. If False, the diagonal element is set to one. default is False. diff --git a/pybamm/spatial_methods/spatial_method.py b/pybamm/spatial_methods/spatial_method.py index a461d6c150..68c8e1adea 100644 --- a/pybamm/spatial_methods/spatial_method.py +++ b/pybamm/spatial_methods/spatial_method.py @@ -1,6 +1,3 @@ -# -# A general spatial method class -# import pybamm import numpy as np from scipy.sparse import eye, kron, coo_matrix, csr_matrix, vstack @@ -13,11 +10,6 @@ class SpatialMethod: All spatial methods will follow the general form of SpatialMethod in that they contain a method for broadcasting variables onto a mesh, a gradient operator, and a divergence operator. - - Parameters - ---------- - mesh : :class: `pybamm.Mesh` - Contains all the submeshes for discretisation """ def __init__(self, options=None): @@ -458,6 +450,7 @@ def process_binary_operators(self, bin_op, left, right, disc_left, disc_right): Discretised binary operator """ + # Don't want to copy the domains, so use _binary_new_copy return bin_op._binary_new_copy(disc_left, disc_right) def concatenation(self, disc_children): diff --git a/pybamm/spatial_methods/spectral_volume.py b/pybamm/spatial_methods/spectral_volume.py index 50e1cadf25..11c6dfd6d2 100644 --- a/pybamm/spatial_methods/spectral_volume.py +++ b/pybamm/spatial_methods/spectral_volume.py @@ -342,12 +342,12 @@ def gradient_matrix(self, domain, domains): sub_matrix[i * d, i * (d + 1) : (i + 1) * (d + 1)] = ( f * sub_matrix_raw[i * (d + 1), i * (d + 1) : (i + 1) * (d + 1)] ) - sub_matrix[ - i * d + 1 : (i + 1) * d, i * (d + 1) : (i + 1) * (d + 1) - ] = sub_matrix_raw[ - i * (d + 1) + 1 : (i + 1) * (d + 1) - 1, - i * (d + 1) : (i + 1) * (d + 1), - ] + sub_matrix[i * d + 1 : (i + 1) * d, i * (d + 1) : (i + 1) * (d + 1)] = ( + sub_matrix_raw[ + i * (d + 1) + 1 : (i + 1) * (d + 1) - 1, + i * (d + 1) : (i + 1) * (d + 1), + ] + ) sub_matrix[(i + 1) * d, i * (d + 1) : (i + 1) * (d + 1)] = ( f * sub_matrix_raw[i * (d + 1) + d, i * (d + 1) : (i + 1) * (d + 1)] ) @@ -391,7 +391,11 @@ def penalty_matrix(self, domains): e = np.zeros(n - 1) e[d - 1 :: d] = 1 / submesh.d_nodes[d - 1 :: d] sub_matrix = vstack( - [np.zeros(n), diags([-e, e], [0, 1], shape=(n - 1, n)), np.zeros(n)] + [ + np.zeros((1, n)), + diags([-e, e], [0, 1], shape=(n - 1, n)), + np.zeros((1, n)), + ] ) # number of repeats diff --git a/pybamm/type_definitions.py b/pybamm/type_definitions.py new file mode 100644 index 0000000000..376189b41a --- /dev/null +++ b/pybamm/type_definitions.py @@ -0,0 +1,17 @@ +from __future__ import annotations + +from typing import Union +from typing_extensions import TypeAlias +import numpy as np +import pybamm + +# numbers.Number should not be used for type hints +Numeric: TypeAlias = Union[int, float, np.number] + +# expression tree +ChildValue: TypeAlias = Union[float, np.ndarray] +ChildSymbol: TypeAlias = Union[float, np.ndarray, pybamm.Symbol] + +DomainType: TypeAlias = Union[list[str], str, None] +AuxiliaryDomainType: TypeAlias = Union[dict[str, str], None] +DomainsType: TypeAlias = Union[dict[str, Union[list[str], str]], None] diff --git a/pybamm/util.py b/pybamm/util.py index 8f76566171..130cb5ba48 100644 --- a/pybamm/util.py +++ b/pybamm/util.py @@ -18,13 +18,12 @@ import difflib from warnings import warn -import numpy as np import pybamm # Versions of jax and jaxlib compatible with PyBaMM. Note: these are also defined in -# in the extras dependencies in pyproject.toml, and therefore must be kept in sync. -JAX_VERSION = "0.4" -JAXLIB_VERSION = "0.4" +# the extras dependencies in pyproject.toml, and therefore must be kept in sync. +JAX_VERSION = "0.4.27" +JAXLIB_VERSION = "0.4.27" def root_dir(): @@ -57,14 +56,30 @@ def get_best_matches(self, key): def __getitem__(self, key): try: return super().__getitem__(key) - except KeyError: + except KeyError as error: + if "electrode diffusivity" in key or "particle diffusivity" in key: + old_term, new_term = ( + ("electrode", "particle") + if "electrode diffusivity" in key + else ("particle", "electrode") + ) + alternative_key = key.replace(old_term, new_term) + + if old_term == "electrode": + warn( + f"The parameter '{alternative_key}' has been renamed to '{key}' and will be removed in a future release. Using '{key}'", + DeprecationWarning, + stacklevel=2, + ) + + return super().__getitem__(alternative_key) if key in ["Negative electrode SOC", "Positive electrode SOC"]: domain = key.split(" ")[0] raise KeyError( f"Variable '{domain} electrode SOC' has been renamed to " f"'{domain} electrode stoichiometry' to avoid confusion " "with cell SOC" - ) + ) from error if "Measured open circuit voltage" in key: raise KeyError( "The variable that used to be called " @@ -73,26 +88,28 @@ def __getitem__(self, key): "variable called 'Bulk open-circuit voltage [V]' which is the" "open-circuit voltage evaluated at the average particle " "concentrations." - ) + ) from error if "Open-circuit voltage at 0% SOC [V]" in key: raise KeyError( "Parameter 'Open-circuit voltage at 0% SOC [V]' not found." "In most cases this should be set to be equal to " "'Lower voltage cut-off [V]'" - ) + ) from error if "Open-circuit voltage at 100% SOC [V]" in key: raise KeyError( "Parameter 'Open-circuit voltage at 100% SOC [V]' not found." "In most cases this should be set to be equal to " "'Upper voltage cut-off [V]'" - ) + ) from error best_matches = self.get_best_matches(key) for k in best_matches: if key in k and k.endswith("]"): raise KeyError( f"'{key}' not found. Use the dimensional version '{k}' instead." - ) - raise KeyError(f"'{key}' not found. Best matches are {best_matches}") + ) from error + raise KeyError( + f"'{key}' not found. Best matches are {best_matches}" + ) from error def search(self, key, print_values=False): """ @@ -234,16 +251,6 @@ def __eq__(self, other): return self.value == other.value -def rmse(x, y): - """ - Calculate the root-mean-square-error between two vectors x and y, ignoring NaNs - """ - # Check lengths - if len(x) != len(y): - raise ValueError("Vectors must have the same length") - return np.sqrt(np.nanmean((x - y) ** 2)) - - def load(filename): """Load a saved object""" with open(filename, "rb") as f: @@ -261,7 +268,15 @@ def get_parameters_filepath(path): def have_jax(): - """Check if jax and jaxlib are installed with the correct versions""" + """ + Check if jax and jaxlib are installed with the correct versions + + Returns + ------- + bool + True if jax and jaxlib are installed with the correct versions, False if otherwise + + """ return ( (importlib.util.find_spec("jax") is not None) and (importlib.util.find_spec("jaxlib") is not None) @@ -270,7 +285,14 @@ def have_jax(): def is_jax_compatible(): - """Check if the available version of jax and jaxlib are compatible with PyBaMM""" + """ + Check if the available versions of jax and jaxlib are compatible with PyBaMM + + Returns + ------- + bool + True if jax and jaxlib are compatible with PyBaMM, False if otherwise + """ return importlib.metadata.distribution("jax").version.startswith( JAX_VERSION ) and importlib.metadata.distribution("jaxlib").version.startswith(JAXLIB_VERSION) @@ -333,7 +355,7 @@ def install_jax(arguments=None): # pragma: no cover "pybamm_install_jax is deprecated," " use 'pip install pybamm[jax]' to install jax & jaxlib" ) - warn(msg, DeprecationWarning) + warn(msg, DeprecationWarning, stacklevel=2) subprocess.check_call( [ sys.executable, @@ -347,24 +369,21 @@ def install_jax(arguments=None): # pragma: no cover # https://docs.pybamm.org/en/latest/source/user_guide/contributing.html#managing-optional-dependencies-and-their-imports -def have_optional_dependency(module_name, attribute=None): +def import_optional_dependency(module_name, attribute=None): err_msg = f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details." try: - # Attempt to import the specified module module = importlib.import_module(module_name) - if attribute: - # If an attribute is specified, check if it's available if hasattr(module, attribute): imported_attribute = getattr(module, attribute) - return imported_attribute # Return the imported attribute + # Return the imported attribute + return imported_attribute else: - # Raise an ModuleNotFoundError if the attribute is not available raise ModuleNotFoundError(err_msg) # pragma: no cover else: # Return the entire module if no attribute is specified return module - except ModuleNotFoundError: + except ModuleNotFoundError as error: # Raise an ModuleNotFoundError if the module or attribute is not available - raise ModuleNotFoundError(err_msg) + raise ModuleNotFoundError(err_msg) from error diff --git a/pybamm/version.py b/pybamm/version.py index 61641b1fbe..edeca1094b 100644 --- a/pybamm/version.py +++ b/pybamm/version.py @@ -1 +1 @@ -__version__ = "24.1" +__version__ = "24.5" diff --git a/pyproject.toml b/pyproject.toml index fca4de17ac..019ed63014 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ requires = [ "setuptools>=64", "wheel", # On Windows, use the CasADi vcpkg registry and CMake bundled from MSVC - "casadi>=3.6.3; platform_system!='Windows'", + "casadi>=3.6.5; platform_system!='Windows'", # Note: the version of CasADi as a build-time dependency should be matched # cross platforms, so updates to its minimum version here should be accompanied # by a version bump in https://github.com/pybamm-team/casadi-vcpkg-registry. @@ -13,12 +13,12 @@ build-backend = "setuptools.build_meta" [project] name = "pybamm" -version = "24.1" +version = "24.5" license = { file = "LICENSE.txt" } description = "Python Battery Mathematical Modelling" authors = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}] maintainers = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}] -requires-python = ">=3.8, <3.13" +requires-python = ">=3.9, <3.13" readme = {file = "README.md", content-type = "text/markdown"} classifiers = [ "Development Status :: 5 - Production/Stable", @@ -28,7 +28,6 @@ classifiers = [ "Programming Language :: Python", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3 :: Only", - "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", @@ -36,11 +35,15 @@ classifiers = [ "Topic :: Scientific/Engineering", ] dependencies = [ - "numpy>=1.23.5", - "scipy>=1.9.3", - "casadi>=3.6.3", + "numpy>=1.23.5,<2.0.0", + "scipy>=1.11.4", + "casadi>=3.6.5", "xarray>=2022.6.0", "anytree>=2.8.0", + "sympy>=1.12", + "typing-extensions>=4.10.0", + "pandas>=1.5.0", + "pooch>=1.8.1", ] [project.urls] @@ -51,7 +54,6 @@ Releases = "https://github.com/pybamm-team/PyBaMM/releases" Changelog = "https://github.com/pybamm-team/PyBaMM/blob/develop/CHANGELOG.md" [project.optional-dependencies] -# For the generation of documentation docs = [ "sphinx>=6", "sphinx_rtd_theme>=0.5", @@ -74,7 +76,6 @@ docs = [ examples = [ "jupyter", ] -# Plotting functionality plot = [ "imageio>=2.3.0", # Note: matplotlib is loaded for debug plots, but to ensure PyBaMM runs @@ -82,23 +83,17 @@ plot = [ # outside of plot() methods. "matplotlib>=3.6.0", ] -# For the Citations class cite = [ "pybtex>=0.24.0", ] -# To generate LaTeX strings -latexify = [ - "sympy>=1.12", -] # Battery Parameter eXchange format bpx = [ - "bpx", + "bpx>=0.4.0", ] # Low-overhead progress bars tqdm = [ "tqdm", ] -# Dependencies intended for use by developers dev = [ # For working with pre-commit hooks "pre-commit", @@ -107,37 +102,30 @@ dev = [ # For running testing sessions "nox", # For coverage - "coverage[toml]", + "pytest-cov", + # For doctest + "pytest-doctestplus", + # For test parameterization + "parameterized>=0.9", # For testing Jupyter notebooks "pytest>=6", "pytest-xdist", "nbmake", -] -# Reading CSV files -pandas = [ - "pandas>=1.5.0", + # To access the metadata for python packages + "importlib-metadata; python_version < '3.10'", ] # For the Jax solver. Note: these must be kept in sync with the versions defined in pybamm/util.py. jax = [ - "jax==0.4.20; python_version >= '3.9'", - "jaxlib==0.4.20; python_version >= '3.9'", -] -# For the scikits.odes solver -odes = [ - "scikits.odes" + "jax==0.4.27", + "jaxlib==0.4.27", ] -# Contains all optional dependencies, except for odes, jax, and dev dependencies +# Contains all optional dependencies, except for jax and dev dependencies all = [ - "autograd>=1.6.2", "scikit-fem>=8.1.0", - "pybamm[examples,plot,cite,latexify,bpx,tqdm,pandas]", + "pybamm[examples,plot,cite,bpx,tqdm]", ] [project.scripts] -pybamm_edit_parameter = "pybamm.parameters_cli:edit_parameter" -pybamm_add_parameter = "pybamm.parameters_cli:add_parameter" -pybamm_rm_parameter = "pybamm.parameters_cli:remove_parameter" -pybamm_install_odes = "pybamm.install_odes:main" pybamm_install_jax = "pybamm.util:install_jax" [project.entry-points."pybamm_parameter_sets"] @@ -185,7 +173,7 @@ extend-exclude = ["__init__.py"] [tool.ruff.lint] extend-select = [ - # "B", # flake8-bugbear + "B", # flake8-bugbear # "I", # isort # "ARG", # flake8-unused-arguments # "C4", # flake8-comprehensions @@ -197,11 +185,12 @@ extend-select = [ # "PT", # flake8-pytest-style # "PTH", # flake8-use-pathlib # "RET", # flake8-return - "RUF", # Ruff-specific + "RUF", # Ruff-specific # "SIM", # flake8-simplify # "T20", # flake8-print - "UP", # pyupgrade - "YTT", # flake8-2020 + "UP", # pyupgrade + "YTT", # flake8-2020 + "TID252", # relative-imports ] ignore = [ "E741", # Ambiguous variable name @@ -253,6 +242,10 @@ filterwarnings = [ # ignore internal nbmake warnings 'ignore:unclosed \ "/usr/local/" then + # KLU_INCLUDE_DIR -> "/usr/local/include" + # KLU_LIBRARY_DIR -> "/usr/local/lib" + KLU_INCLUDE_DIR = os.path.join(install_dir, "include") + KLU_LIBRARY_DIR = os.path.join(install_dir, "lib") + cmake_args = [ + "-DENABLE_LAPACK=ON", + "-DSUNDIALS_INDEX_SIZE=32", + "-DEXAMPLES_ENABLE_C=OFF", + "-DEXAMPLES_ENABLE_CXX=OFF", + "-DEXAMPLES_INSTALL=OFF", + "-DENABLE_KLU=ON", + "-DENABLE_OPENMP=ON", + f"-DKLU_INCLUDE_DIR={KLU_INCLUDE_DIR}", + f"-DKLU_LIBRARY_DIR={KLU_LIBRARY_DIR}", + "-DCMAKE_INSTALL_PREFIX=" + install_dir, + # on macOS use fixed paths rather than rpath + "-DCMAKE_INSTALL_NAME_DIR=" + KLU_LIBRARY_DIR, + ] + + # try to find OpenMP on mac + if platform.system() == "Darwin": + # flags to find OpenMP on mac + if platform.processor() == "arm": + OpenMP_C_FLAGS = ( + "-Xpreprocessor -fopenmp -I/opt/homebrew/opt/libomp/include" + ) + OpenMP_C_LIB_NAMES = "omp" + OpenMP_omp_LIBRARY = "/opt/homebrew/opt/libomp/lib/libomp.dylib" + elif platform.processor() == "i386": + OpenMP_C_FLAGS = "-Xpreprocessor -fopenmp -I/usr/local/opt/libomp/include" + OpenMP_C_LIB_NAMES = "omp" + OpenMP_omp_LIBRARY = "/usr/local/opt/libomp/lib/libomp.dylib" + else: + raise NotImplementedError( + f"Unsupported processor architecture: {platform.processor()}. " + "Only 'arm' and 'i386' architectures are supported." + ) + + # Don't pass the following args to CMake when building wheels. We set a custom + # OpenMP installation for macOS wheels in the wheel build script. + # This is because we can't use Homebrew's OpenMP dylib due to the wheel + # repair process, where Homebrew binaries are not built for distribution and + # break MACOSX_DEPLOYMENT_TARGET. We use a custom OpenMP binary as described + # in CIBW_BEFORE_ALL in the wheel builder CI job. + # Check for CI environment variable to determine if we are building a wheel + if os.environ.get("CIBUILDWHEEL") != "1": + print("Using Homebrew OpenMP for macOS build") + cmake_args += [ + "-DOpenMP_C_FLAGS=" + OpenMP_C_FLAGS, + "-DOpenMP_C_LIB_NAMES=" + OpenMP_C_LIB_NAMES, + "-DOpenMP_omp_LIBRARY=" + OpenMP_omp_LIBRARY, + ] + + # SUNDIALS are built within download_dir 'build_sundials' in the PyBaMM root + # download_dir + build_dir = os.path.abspath(os.path.join(download_dir, "build_sundials")) + if not os.path.exists(build_dir): + print("\n-" * 10, "Creating build dir", "-" * 40) + os.makedirs(build_dir) + + sundials_src = f"../sundials-{SUNDIALS_VERSION}" + print("-" * 10, "Running CMake prepare", "-" * 40) + subprocess.run(["cmake", sundials_src, *cmake_args], cwd=build_dir, check=True) + + print("-" * 10, "Building SUNDIALS", "-" * 40) + make_cmd = ["make", f"-j{cpu_count()}", "install"] + subprocess.run(make_cmd, cwd=build_dir, check=True) + + +def check_libraries_installed(install_dir): + # Define the directories to check for SUNDIALS and SuiteSparse libraries + lib_dirs = [install_dir] + + sundials_files = [ + "libsundials_idas", + "libsundials_sunlinsolklu", + "libsundials_sunlinsoldense", + "libsundials_sunlinsolspbcgs", + "libsundials_sunlinsollapackdense", + "libsundials_sunmatrixsparse", + "libsundials_nvecserial", + "libsundials_nvecopenmp", + ] + if platform.system() == "Linux": + sundials_files = [file + ".so" for file in sundials_files] + elif platform.system() == "Darwin": + sundials_files = [file + ".dylib" for file in sundials_files] + sundials_lib_found = True + # Check for SUNDIALS libraries in each directory + for lib_file in sundials_files: + file_found = False + for lib_dir in lib_dirs: + if isfile(join(lib_dir, "lib", lib_file)): + print(f"{lib_file} found in {lib_dir}.") + file_found = True + break + if not file_found: + print( + f"{lib_file} not found. Proceeding with SUNDIALS library installation." + ) + sundials_lib_found = False + break + + suitesparse_files = [ + "libsuitesparseconfig", + "libklu", + "libamd", + "libcolamd", + "libbtf", + ] + if platform.system() == "Linux": + suitesparse_files = [file + ".so" for file in suitesparse_files] + elif platform.system() == "Darwin": + suitesparse_files = [file + ".dylib" for file in suitesparse_files] + else: + raise NotImplementedError( + f"Unsupported operating system: {platform.system()}. This script currently supports only Linux and macOS." + ) + + suitesparse_lib_found = True + # Check for SuiteSparse libraries in each directory + for lib_file in suitesparse_files: + file_found = False + for lib_dir in lib_dirs: + if isfile(join(lib_dir, "lib", lib_file)): + print(f"{lib_file} found in {lib_dir}.") + file_found = True + break + if not file_found: + print( + f"{lib_file} not found. Proceeding with SuiteSparse library installation." + ) + suitesparse_lib_found = False + break + + return sundials_lib_found, suitesparse_lib_found + +def calculate_sha256(file_path): + sha256_hash = hashlib.sha256() + with open(file_path, "rb") as f: + # Read and update hash in chunks of 4K + for byte_block in iter(lambda: f.read(4096), b""): + sha256_hash.update(byte_block) + return sha256_hash.hexdigest() + + +def download_extract_library(url, expected_checksum, download_dir): + file_name = url.split("/")[-1] + file_path = os.path.join(download_dir, file_name) + + # Check if file already exists and validate checksum + if os.path.exists(file_path): + print(f"Validating checksum for {file_name}...") + actual_checksum = calculate_sha256(file_path) + print(f"Found {actual_checksum} against {expected_checksum}") + if actual_checksum == expected_checksum: + print(f"Checksum valid. Skipping download for {file_name}.") + # Extract the archive as the checksum is valid + with tarfile.open(file_path) as tar: + tar.extractall(download_dir) + return + else: + print(f"Checksum invalid. Redownloading {file_name}.") -def download_extract_library(url, download_dir): # Download and extract archive at url - if NO_WGET: - error_msg = ( - "Could not find wget module." - " Please install wget module (pip install wget)." + parsed_url = urlparse(url) + if parsed_url.scheme not in ["http", "https"]: + raise ValueError( + f"Invalid URL scheme: {parsed_url.scheme}. Only HTTP and HTTPS are allowed." ) - raise ModuleNotFoundError(error_msg) - archive = wget.download(url, out=download_dir) - tar = tarfile.open(archive) - tar.extractall(download_dir) + with urllib.request.urlopen(url) as response: + os.makedirs(download_dir, exist_ok=True) + with open(file_path, "wb") as out_file: + out_file.write(response.read()) + with tarfile.open(file_path) as tar: + tar.extractall(download_dir) + + +def parallel_download(urls, download_dir): + # Use 2 processes for parallel downloading + with ThreadPoolExecutor(max_workers=len(urls)) as executor: + futures = [ + executor.submit( + download_extract_library, url, expected_checksum, download_dir + ) + for (url, expected_checksum) in urls + ] + for future in futures: + future.result() # First check requirements: make and cmake try: subprocess.run(["make", "--version"]) -except OSError: - raise RuntimeError("Make must be installed.") +except OSError as error: + raise RuntimeError("Make must be installed.") from error try: subprocess.run(["cmake", "--version"]) -except OSError: - raise RuntimeError("CMake must be installed.") +except OSError as error: + raise RuntimeError("CMake must be installed.") from error # Build in parallel wherever possible os.environ["CMAKE_BUILD_PARALLEL_LEVEL"] = str(cpu_count()) @@ -47,11 +290,15 @@ def download_extract_library(url, download_dir): os.makedirs(download_dir) # Get installation location -default_install_dir = os.path.join(os.getenv("HOME"), ".local") parser = argparse.ArgumentParser( description="Download, compile and install Sundials and SuiteSparse." ) -parser.add_argument("--install-dir", type=str, default=default_install_dir) +parser.add_argument( + "--force", + action="store_true", + help="Force installation even if libraries are already found. This will overwrite the pre-existing files.", +) +parser.add_argument("--install-dir", type=str, default=DEFAULT_INSTALL_DIR) args = parser.parse_args() install_dir = ( args.install_dir @@ -59,120 +306,37 @@ def download_extract_library(url, download_dir): else os.path.join(pybamm_dir, args.install_dir) ) -# 1 --- Download SuiteSparse -suitesparse_version = "6.0.3" -suitesparse_url = ( - "https://github.com/DrTimothyAldenDavis/" - + f"SuiteSparse/archive/v{suitesparse_version}.tar.gz" -) -download_extract_library(suitesparse_url, download_dir) - -# The SuiteSparse KLU module has 4 dependencies: -# - suitesparseconfig -# - AMD -# - COLAMD -# - BTF -suitesparse_dir = f"SuiteSparse-{suitesparse_version}" -suitesparse_src = os.path.join(download_dir, suitesparse_dir) -print("-" * 10, "Building SuiteSparse_config", "-" * 40) -make_cmd = [ - "make", - "library", -] -install_cmd = [ - "make", - f"-j{cpu_count()}", - "install", -] -print("-" * 10, "Building SuiteSparse", "-" * 40) -# Set CMAKE_OPTIONS as environment variables to pass to the GNU Make command -env = os.environ.copy() -for libdir in ["SuiteSparse_config", "AMD", "COLAMD", "BTF", "KLU"]: - build_dir = os.path.join(suitesparse_src, libdir) - # We want to ensure that libsuitesparseconfig.dylib is not repeated in - # multiple paths at the time of wheel repair. Therefore, it should not be - # built with an RPATH since it is copied to the install prefix. - if libdir == "SuiteSparse_config": - env["CMAKE_OPTIONS"] = f"-DCMAKE_INSTALL_PREFIX={install_dir}" - else: - # For AMD, COLAMD, BTF and KLU; do not set a BUILD RPATH but use an - # INSTALL RPATH in order to ensure that the dynamic libraries are found - # at runtime just once. Otherwise, delocate complains about multiple - # references to the SuiteSparse_config dynamic library (auditwheel does not). - env[ - "CMAKE_OPTIONS" - ] = f"-DCMAKE_INSTALL_PREFIX={install_dir} -DCMAKE_INSTALL_RPATH={install_dir}/lib -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=FALSE -DCMAKE_BUILD_WITH_INSTALL_RPATH=FALSE" - subprocess.run(make_cmd, cwd=build_dir, env=env, shell=True, check=True) - subprocess.run(install_cmd, cwd=build_dir, check=True) - -# 2 --- Download SUNDIALS -sundials_version = "6.5.0" -sundials_url = ( - "https://github.com/LLNL/sundials/" - + f"releases/download/v{sundials_version}/sundials-{sundials_version}.tar.gz" -) - -download_extract_library(sundials_url, download_dir) - -# Set install dir for SuiteSparse libs -# Ex: if install_dir -> "/usr/local/" then -# KLU_INCLUDE_DIR -> "/usr/local/include" -# KLU_LIBRARY_DIR -> "/usr/local/lib" -KLU_INCLUDE_DIR = os.path.join(install_dir, "include") -KLU_LIBRARY_DIR = os.path.join(install_dir, "lib") -cmake_args = [ - "-DENABLE_LAPACK=ON", - "-DSUNDIALS_INDEX_SIZE=32", - "-DEXAMPLES_ENABLE_C=OFF", - "-DEXAMPLES_ENABLE_CXX=OFF", - "-DEXAMPLES_INSTALL=OFF", - "-DENABLE_KLU=ON", - "-DENABLE_OPENMP=ON", - f"-DKLU_INCLUDE_DIR={KLU_INCLUDE_DIR}", - f"-DKLU_LIBRARY_DIR={KLU_LIBRARY_DIR}", - "-DCMAKE_INSTALL_PREFIX=" + install_dir, - # on macOS use fixed paths rather than rpath - "-DCMAKE_INSTALL_NAME_DIR=" + KLU_LIBRARY_DIR, -] - -# try to find OpenMP on mac -if platform.system() == "Darwin": - # flags to find OpenMP on mac - if platform.processor() == "arm": - LDFLAGS = "-L/opt/homebrew/opt/libomp/lib" - CPPFLAGS = "-I/opt/homebrew/opt/libomp/include" - OpenMP_C_FLAGS = "-Xpreprocessor -fopenmp -I/opt/homebrew/opt/libomp/include" - OpenMP_C_LIB_NAMES = "omp" - OpenMP_libomp_LIBRARY = "/opt/homebrew/opt/libomp/lib/libomp.dylib" - OpenMP_omp_LIBRARY = "/opt/homebrew/opt/libomp/lib/libomp.dylib" - elif platform.processor() == "i386": - LDFLAGS = "-L/usr/local/opt/libomp/lib" - CPPFLAGS = "-I/usr/local/opt/libomp/include" - OpenMP_C_FLAGS = "-Xpreprocessor -fopenmp -I/usr/local/opt/libomp/include" - OpenMP_CXX_FLAGS = "-Xpreprocessor -fopenmp -I/usr/local/opt/libomp/include" - OpenMP_C_LIB_NAMES = "omp" - OpenMP_CXX_LIB_NAMES = "omp" - OpenMP_omp_LIBRARY = "/usr/local/opt/libomp/lib/libomp.dylib" - - cmake_args += [ - "-DLDFLAGS=" + LDFLAGS, - "-DCPPFLAGS=" + CPPFLAGS, - "-DOpenMP_C_FLAGS=" + OpenMP_C_FLAGS, - "-DOpenMP_C_LIB_NAMES=" + OpenMP_C_LIB_NAMES, - "-DOpenMP_omp_LIBRARY=" + OpenMP_omp_LIBRARY, - ] - -# SUNDIALS are built within download_dir 'build_sundials' in the PyBaMM root -# download_dir -build_dir = os.path.abspath(os.path.join(download_dir, "build_sundials")) -if not os.path.exists(build_dir): - print("\n-" * 10, "Creating build dir", "-" * 40) - os.makedirs(build_dir) - -sundials_src = f"../sundials-{sundials_version}" -print("-" * 10, "Running CMake prepare", "-" * 40) -subprocess.run(["cmake", sundials_src, *cmake_args], cwd=build_dir, check=True) +if args.force: + print( + "The '--force' option is activated: installation will be forced, ignoring any existing libraries." + ) + safe_remove_dir(os.path.join(download_dir, "build_sundials")) + safe_remove_dir(os.path.join(download_dir, f"SuiteSparse-{SUITESPARSE_VERSION}")) + safe_remove_dir(os.path.join(download_dir, f"sundials-{SUNDIALS_VERSION}")) + sundials_found, suitesparse_found = False, False +else: + # Check whether the libraries are installed + sundials_found, suitesparse_found = check_libraries_installed(install_dir) -print("-" * 10, "Building the sundials", "-" * 40) -make_cmd = ["make", f"-j{cpu_count()}", "install"] -subprocess.run(make_cmd, cwd=build_dir, check=True) +if __name__ == "__main__": + # Determine which libraries to download based on whether they were found + if not sundials_found and not suitesparse_found: + # Both SUNDIALS and SuiteSparse are missing, download and install both + parallel_download( + [ + (SUITESPARSE_URL, SUITESPARSE_CHECKSUM), + (SUNDIALS_URL, SUNDIALS_CHECKSUM), + ], + download_dir, + ) + install_suitesparse(download_dir) + install_sundials(download_dir, install_dir) + else: + if not sundials_found: + # Only SUNDIALS is missing, download and install it + parallel_download([(SUNDIALS_URL, SUNDIALS_CHECKSUM)], download_dir) + install_sundials(download_dir, install_dir) + if not suitesparse_found: + # Only SuiteSparse is missing, download and install it + parallel_download([(SUITESPARSE_URL, SUITESPARSE_CHECKSUM)], download_dir) + install_suitesparse(download_dir) diff --git a/setup.py b/setup.py index 7d688dc883..6b97f73058 100644 --- a/setup.py +++ b/setup.py @@ -289,15 +289,30 @@ def compile_KLU(): idaklu_ext = Extension( name="pybamm.solvers.idaklu", + # The sources list should mirror the list in CMakeLists.txt sources=[ - "pybamm/solvers/c_solvers/idaklu.cpp" - "pybamm/solvers/c_solvers/idaklu.hpp" - "pybamm/solvers/c_solvers/idaklu_casadi.cpp" - "pybamm/solvers/c_solvers/idaklu_casadi.hpp" - "pybamm/solvers/c_solvers/idaklu_python.cpp" - "pybamm/solvers/c_solvers/idaklu_python.hpp" - "pybamm/solvers/c_solvers/solution.cpp" - "pybamm/solvers/c_solvers/solution.hpp" + "pybamm/solvers/c_solvers/idaklu/casadi_functions.cpp", + "pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp", + "pybamm/solvers/c_solvers/idaklu/casadi_solver.cpp", + "pybamm/solvers/c_solvers/idaklu/casadi_solver.hpp", + "pybamm/solvers/c_solvers/idaklu/CasadiSolver.cpp", + "pybamm/solvers/c_solvers/idaklu/CasadiSolver.hpp", + "pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP.cpp", + "pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP.hpp", + "pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP_solvers.cpp", + "pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP_solvers.hpp", + "pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp", + "pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.hpp", + "pybamm/solvers/c_solvers/idaklu/idaklu_jax.cpp", + "pybamm/solvers/c_solvers/idaklu/idaklu_jax.hpp", + "pybamm/solvers/c_solvers/idaklu/common.hpp", + "pybamm/solvers/c_solvers/idaklu/python.hpp", + "pybamm/solvers/c_solvers/idaklu/python.cpp", + "pybamm/solvers/c_solvers/idaklu/solution.cpp", + "pybamm/solvers/c_solvers/idaklu/solution.hpp", + "pybamm/solvers/c_solvers/idaklu/options.hpp", + "pybamm/solvers/c_solvers/idaklu/options.cpp", + "pybamm/solvers/c_solvers/idaklu.cpp", ], ) ext_modules = [idaklu_ext] if compile_KLU() else [] diff --git a/tests/__init__.py b/tests/__init__.py index 919605998e..f23a008ce0 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -32,10 +32,17 @@ get_discretisation_for_testing, get_p2d_discretisation_for_testing, get_size_distribution_disc_for_testing, + function_test, + multi_var_function_test, + multi_var_function_cube_test, get_1p1d_discretisation_for_testing, get_2p1d_discretisation_for_testing, get_unit_2p1D_mesh_for_testing, get_cylindrical_discretisation_for_testing, get_base_model_with_battery_geometry, + get_required_distribution_deps, + get_optional_distribution_deps, + get_present_optional_import_deps, + no_internet_connection, ) from .testcase import TestCase diff --git a/tests/integration/test_models/standard_model_tests.py b/tests/integration/test_models/standard_model_tests.py index 43eba8894e..7f0e9e6137 100644 --- a/tests/integration/test_models/standard_model_tests.py +++ b/tests/integration/test_models/standard_model_tests.py @@ -3,6 +3,7 @@ # import pybamm import tests +import uuid import numpy as np import os @@ -140,11 +141,14 @@ def test_sensitivities(self, param_name, param_value, output_name="Voltage [V]") ) def test_serialisation(self, solver=None, t_eval=None): + # Generating unique file names to avoid race conditions when run in parallel. + unique_id = uuid.uuid4() + file_name = f"test_model_{unique_id}" self.model.save_model( - "test_model", variables=self.model.variables, mesh=self.disc.mesh + file_name, variables=self.model.variables, mesh=self.disc.mesh ) - new_model = pybamm.load_model("test_model.json") + new_model = pybamm.load_model(file_name + ".json") # create new solver for re-created model if solver is not None: @@ -170,12 +174,12 @@ def test_serialisation(self, solver=None, t_eval=None): new_solution = new_solver.solve(new_model, t_eval) - for x, val in enumerate(self.solution.all_ys): + for x, _ in enumerate(self.solution.all_ys): np.testing.assert_array_almost_equal( new_solution.all_ys[x], self.solution.all_ys[x], decimal=accuracy ) - os.remove("test_model.json") + os.remove(file_name + ".json") def test_all( self, param=None, disc=None, solver=None, t_eval=None, skip_output_tests=False diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_basic_half_cell_models.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_basic_half_cell_models.py deleted file mode 100644 index 0d69fbae28..0000000000 --- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_basic_half_cell_models.py +++ /dev/null @@ -1,78 +0,0 @@ -# -# Test basic half-cell model with different parameter values -# -from tests import TestCase -import pybamm - -import numpy as np -import unittest - - -class TestBasicHalfCellModels(TestCase): - def test_runs_Xu2019(self): - options = {"working electrode": "positive"} - model = pybamm.lithium_ion.BasicDFNHalfCell(options=options) - - # create geometry - geometry = model.default_geometry - - # load parameter values - param = pybamm.ParameterValues("Xu2019") - - param["Current function [A]"] = 2.5e-3 - - # process model and geometry - param.process_model(model) - param.process_geometry(geometry) - - # set mesh - var_pts = model.default_var_pts - mesh = pybamm.Mesh(geometry, model.default_submesh_types, var_pts) - - # discretise model - disc = pybamm.Discretisation(mesh, model.default_spatial_methods) - disc.process_model(model) - - # solve model - t_eval = np.linspace(0, 7200, 1000) - solver = pybamm.CasadiSolver(mode="safe", atol=1e-6, rtol=1e-3) - solver.solve(model, t_eval) - - def test_runs_OKane2022_negative(self): - # load model - options = {"working electrode": "positive"} - model = pybamm.lithium_ion.BasicDFNHalfCell(options=options) - - # create geometry - geometry = model.default_geometry - - # load parameter values - param = pybamm.ParameterValues("OKane2022_graphite_SiOx_halfcell") - - param["Current function [A]"] = -2.5 # C/2 charge - - # process model and geometry - param.process_model(model) - param.process_geometry(geometry) - - # set mesh - var_pts = model.default_var_pts - mesh = pybamm.Mesh(geometry, model.default_submesh_types, var_pts) - - # discretise model - disc = pybamm.Discretisation(mesh, model.default_spatial_methods) - disc.process_model(model) - - # solve model - t_eval = np.linspace(0, 7200, 1000) - solver = pybamm.CasadiSolver(mode="safe", atol=1e-6, rtol=1e-3) - solver.solve(model, t_eval) - - -if __name__ == "__main__": - print("Add -v for more debug output") - import sys - - if "-v" in sys.argv: - debug = True - unittest.main() diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_basic_models.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_basic_models.py new file mode 100644 index 0000000000..932405dc67 --- /dev/null +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_basic_models.py @@ -0,0 +1,51 @@ +# +# Test basic model classes +# +from tests import TestCase +import pybamm + +import unittest + + +class BaseBasicModelTest: + def test_with_experiment(self): + model = self.model + experiment = pybamm.Experiment( + [ + "Discharge at C/3 until 3.5V", + "Hold at 3.5V for 1 hour", + "Rest for 10 min", + ] + ) + sim = pybamm.Simulation(model, experiment=experiment) + sim.solve(calc_esoh=False) + + +class TestBasicSPM(BaseBasicModelTest, TestCase): + def setUp(self): + self.model = pybamm.lithium_ion.BasicSPM() + + +class TestBasicDFN(BaseBasicModelTest, TestCase): + def setUp(self): + self.model = pybamm.lithium_ion.BasicDFN() + + +class TestBasicDFNComposite(BaseBasicModelTest, TestCase): + def setUp(self): + self.model = pybamm.lithium_ion.BasicDFNComposite() + + +class TestBasicDFNHalfCell(BaseBasicModelTest, TestCase): + def setUp(self): + options = {"working electrode": "positive"} + self.model = pybamm.lithium_ion.BasicDFNHalfCell(options) + + +if __name__ == "__main__": + print("Add -v for more debug output") + import sys + + if "-v" in sys.argv: + debug = True + unittest.main() diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_compare_outputs_two_phase.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_compare_outputs_two_phase.py index 48cda791b1..b0c0fe5898 100644 --- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_compare_outputs_two_phase.py +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_compare_outputs_two_phase.py @@ -31,7 +31,7 @@ def compare_outputs_two_phase_graphite_graphite(self, model_class): "Maximum concentration in negative electrode [mol.m-3]", "Initial concentration in negative electrode [mol.m-3]", "Negative particle radius [m]", - "Negative electrode diffusivity [m2.s-1]", + "Negative particle diffusivity [m2.s-1]", "Negative electrode exchange-current density [A.m-2]", ]: parameter_values_two_phase.update( @@ -144,9 +144,9 @@ def compare_outputs_two_phase_silicon_graphite(self, model_class): ) sim = pybamm.Simulation(model, parameter_values=param) - t_eval = np.linspace(0, 9000, 1000) - sol1 = sim.solve(t_eval, inputs={"x": 0.01}) - sol2 = sim.solve(t_eval, inputs={"x": 0.1}) + t_eval = np.linspace(0, 8000, 1000) + inputs = [{"x": 0.01}, {"x": 0.1}] + sol = sim.solve(t_eval, inputs=inputs) # Starting values should be close for var in [ @@ -155,11 +155,11 @@ def compare_outputs_two_phase_silicon_graphite(self, model_class): "Average negative secondary particle concentration", ]: np.testing.assert_allclose( - sol1[var].data[:20], sol2[var].data[:20], rtol=1e-2 + sol[0][var].data[:20], sol[1][var].data[:20], rtol=1e-2 ) # More silicon means longer sim - self.assertLess(sol1["Time [s]"].data[-1], sol2["Time [s]"].data[-1]) + self.assertLess(sol[0]["Time [s]"].data[-1], sol[1]["Time [s]"].data[-1]) def test_compare_SPM_silicon_graphite(self): model_class = pybamm.lithium_ion.SPM diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py index e217a11d75..82d228badb 100644 --- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py @@ -68,6 +68,28 @@ def test_current_sigmoid_ocp(self): modeltest = tests.StandardModelTest(model, parameter_values=parameter_values) modeltest.test_all(skip_output_tests=True) + def test_wycisk_ocp(self): + options = {"open-circuit potential": ("Wycisk", "single")} + model = pybamm.lithium_ion.MPM(options) + parameter_values = pybamm.ParameterValues("Chen2020") + parameter_values = pybamm.get_size_distribution_parameters(parameter_values) + parameter_values.update( + { + "Negative electrode lithiation OCP [V]" + "": lambda sto: parameter_values["Negative electrode OCP [V]"](sto) + - 0.1, + "Negative electrode delithiation OCP [V]" + "": lambda sto: parameter_values["Negative electrode OCP [V]"](sto) + + 0.1, + "Negative particle hysteresis decay rate": 1, + "Negative particle hysteresis switching factor": 1, + # "Negative electrode OCP hysteresis [V]": lambda sto: 1, + }, + check_already_exists=False, + ) + modeltest = tests.StandardModelTest(model, parameter_values=parameter_values) + modeltest.test_all(skip_output_tests=True) + def test_voltage_control(self): options = {"operating mode": "voltage"} model = pybamm.lithium_ion.MPM(options) diff --git a/tests/integration/test_spatial_methods/test_finite_volume.py b/tests/integration/test_spatial_methods/test_finite_volume.py index 2f9f6ef1b0..ce2d07c2de 100644 --- a/tests/integration/test_spatial_methods/test_finite_volume.py +++ b/tests/integration/test_spatial_methods/test_finite_volume.py @@ -339,6 +339,55 @@ def test_laplacian_spherical(self): ) +def solve_advection_equation(direction="upwind", source=1, bc=0): + model = pybamm.BaseModel() + x = pybamm.SpatialVariable("x", domain="domain", coord_sys="cartesian") + u = pybamm.Variable("u", domain="domain") + if direction == "upwind": + bc_side = "left" + y = x + v = pybamm.PrimaryBroadcastToEdges(1, ["domain"]) + rhs = -pybamm.div(pybamm.upwind(u) * v) + source + elif direction == "downwind": + bc_side = "right" + y = 1 - x + v = pybamm.PrimaryBroadcastToEdges(-1, ["domain"]) + rhs = -pybamm.div(pybamm.downwind(u) * v) + source + + u_an = (bc + source * y) - (bc + source * (y - pybamm.t)) * ((y - pybamm.t) > 0) + model.boundary_conditions = { + u: { + bc_side: (pybamm.Scalar(bc), "Dirichlet"), + } + } + model.rhs = {u: rhs} + model.initial_conditions = {u: pybamm.Scalar(0)} + model.variables = {"u": u, "x": x, "analytical": u_an} + geometry = {"domain": {x: {"min": pybamm.Scalar(0), "max": pybamm.Scalar(1)}}} + submesh_types = {"domain": pybamm.Uniform1DSubMesh} + var_pts = {x: 1000} + mesh = pybamm.Mesh(geometry, submesh_types, var_pts) + spatial_methods = {"domain": pybamm.FiniteVolume()} + disc = pybamm.Discretisation(mesh, spatial_methods) + disc.process_model(model) + solver = pybamm.CasadiSolver() + return solver.solve(model, [0, 1]) + + +class TestUpwindDownwind(TestCase): + def test_upwind(self): + solution = solve_advection_equation("upwind") + np.testing.assert_array_almost_equal( + solution["u"].entries, solution["analytical"].entries, decimal=2 + ) + + def test_downwind(self): + solution = solve_advection_equation("downwind") + np.testing.assert_array_almost_equal( + solution["u"].entries, solution["analytical"].entries, decimal=2 + ) + + if __name__ == "__main__": print("Add -v for more debug output") import sys diff --git a/tests/shared.py b/tests/shared.py index 1f0b033582..2fa9c24960 100644 --- a/tests/shared.py +++ b/tests/shared.py @@ -3,6 +3,14 @@ # import pybamm from scipy.sparse import eye +import sys +import re +import socket + +if sys.version_info < (3, 10): + import importlib_metadata +else: + import importlib.metadata as importlib_metadata class SpatialMethodForTesting(pybamm.SpatialMethod): @@ -153,8 +161,10 @@ def get_2p1d_mesh_for_testing( ypts=15, zpts=15, include_particles=True, - cc_submesh=pybamm.MeshGenerator(pybamm.ScikitUniform2DSubMesh), + cc_submesh=None, ): + if cc_submesh is None: + cc_submesh = pybamm.MeshGenerator(pybamm.ScikitUniform2DSubMesh) geometry = pybamm.battery_geometry( include_particles=include_particles, options={"dimensionality": 2} ) @@ -245,6 +255,18 @@ def get_size_distribution_disc_for_testing(xpts=None, rpts=10, Rpts=10, zpts=15) ) +def function_test(arg): + return arg + arg + + +def multi_var_function_test(arg1, arg2): + return arg1 + arg2 + + +def multi_var_function_cube_test(arg1, arg2): + return arg1 + arg2**3 + + def get_1p1d_discretisation_for_testing(xpts=None, rpts=10, zpts=15): return get_discretisation_for_testing( mesh=get_1p1d_mesh_for_testing(xpts, rpts, zpts), @@ -274,3 +296,45 @@ def get_base_model_with_battery_geometry(**kwargs): model = pybamm.BaseModel() model._geometry = pybamm.battery_geometry(**kwargs) return model + + +def get_required_distribution_deps(package_name): + pattern = re.compile(r"(?!.*extra\b)^([^<>=;\[]+)\b.*$") + if json_deps := importlib_metadata.metadata(package_name).json.get("requires_dist"): + return {m.group(1) for dep_name in json_deps if (m := pattern.match(dep_name))} + return set() + + +def get_optional_distribution_deps(package_name): + pattern = re.compile(rf"(?!.*{package_name}\b|.*docs\b|.*dev\b)^([^<>=;\[]+)\b.*$") + if json_deps := importlib_metadata.metadata(package_name).json.get("requires_dist"): + return { + m.group(1) + for dep_name in json_deps + if (m := pattern.match(dep_name)) and "extra" in m.group(0) + } + return set() + + +def get_present_optional_import_deps(package_name, optional_distribution_deps=None): + if optional_distribution_deps is None: + optional_distribution_deps = get_optional_distribution_deps(package_name) + + present_optional_import_deps = set() + for ( + import_pkg, + distribution_pkgs, + ) in importlib_metadata.packages_distributions().items(): + if any(dep in optional_distribution_deps for dep in distribution_pkgs): + present_optional_import_deps.add(import_pkg) + return present_optional_import_deps + + +def no_internet_connection(): + try: + host = socket.gethostbyname("www.github.com") + conn = socket.create_connection((host, 80), 2) + conn.close() + return False + except socket.gaierror: + return True diff --git a/tests/unit/test_batch_study.py b/tests/unit/test_batch_study.py index f8762133a6..ab7370a193 100644 --- a/tests/unit/test_batch_study.py +++ b/tests/unit/test_batch_study.py @@ -1,6 +1,7 @@ """ Tests for the batch_study.py """ + from tests import TestCase import os import pybamm @@ -52,7 +53,7 @@ def test_solve(self): # Tests for BatchStudy when permutations=False bs_false.solve() - bs_false.plot(testing=True) + bs_false.plot(show_plot=False) self.assertEqual(2, len(bs_false.sims)) for num in range(len(bs_false.sims)): output_model = bs_false.sims[num].model.name @@ -63,16 +64,15 @@ def test_solve(self): solvers_list = [solver.name for solver in bs_false.solvers.values()] self.assertIn(output_solver, solvers_list) - output_experiment = bs_false.sims[num].experiment.operating_conditions_steps + output_experiment = bs_false.sims[num].experiment.steps experiments_list = [ - experiment.operating_conditions_steps - for experiment in bs_false.experiments.values() + experiment.steps for experiment in bs_false.experiments.values() ] self.assertIn(output_experiment, experiments_list) # Tests for BatchStudy when permutations=True bs_true.solve() - bs_true.plot(testing=True) + bs_true.plot(show_plot=False) self.assertEqual(4, len(bs_true.sims)) for num in range(len(bs_true.sims)): output_model = bs_true.sims[num].model.name @@ -83,16 +83,21 @@ def test_solve(self): solvers_list = [solver.name for solver in bs_true.solvers.values()] self.assertIn(output_solver, solvers_list) - output_experiment = bs_true.sims[num].experiment.operating_conditions_steps + output_experiment = bs_true.sims[num].experiment.steps experiments_list = [ - experiment.operating_conditions_steps - for experiment in bs_true.experiments.values() + experiment.steps for experiment in bs_true.experiments.values() ] self.assertIn(output_experiment, experiments_list) def test_create_gif(self): with TemporaryDirectory() as dir_name: bs = pybamm.BatchStudy({"spm": pybamm.lithium_ion.SPM()}) + with self.assertRaisesRegex( + ValueError, "The simulations have not been solved yet." + ): + pybamm.BatchStudy( + models={"SPM": spm, "SPM uniform": spm_uniform} + ).create_gif() bs.solve([0, 10]) # Create a temporary file name @@ -102,7 +107,7 @@ def test_create_gif(self): bs.create_gif(number_of_images=3, duration=1, output_filename=test_file) # create a GIF after calling the plot method - bs.plot(testing=True) + bs.plot(show_plot=False) bs.create_gif(number_of_images=3, duration=1, output_filename=test_file) diff --git a/tests/unit/test_callbacks.py b/tests/unit/test_callbacks.py index b36fef9ec6..649c7d9ec8 100644 --- a/tests/unit/test_callbacks.py +++ b/tests/unit/test_callbacks.py @@ -81,6 +81,7 @@ def test_logging_callback(self): "cycle number": (5, 12), "step number": (1, 4), "elapsed time": 0.45, + "step duration": 1, "step operating conditions": "Charge", "termination": "event", } @@ -96,10 +97,14 @@ def test_logging_callback(self): with open("test_callback.log") as f: self.assertIn("Cycle 5/12, step 1/4", f.read()) - callback.on_experiment_infeasible(logs) + callback.on_experiment_infeasible_event(logs) with open("test_callback.log") as f: self.assertIn("Experiment is infeasible: 'event'", f.read()) + callback.on_experiment_infeasible_time(logs) + with open("test_callback.log") as f: + self.assertIn("Experiment is infeasible: default duration", f.read()) + callback.on_experiment_end(logs) with open("test_callback.log") as f: self.assertIn("took 0.45", f.read()) diff --git a/tests/unit/test_citations.py b/tests/unit/test_citations.py index d8c1de3718..ba216e62ff 100644 --- a/tests/unit/test_citations.py +++ b/tests/unit/test_citations.py @@ -70,11 +70,6 @@ def test_print_citations(self): with self.assertRaisesRegex(pybamm.OptionError, "'text' or 'bibtex'"): pybamm.print_citations("test_citations.txt", "bad format") - pybamm.citations._citation_err_msg = "Error" - with self.assertRaisesRegex(ImportError, "Error"): - pybamm.print_citations() - pybamm.citations._citation_err_msg = None - # Test that unknown citation raises warning message on printing pybamm.citations._reset() pybamm.citations.register("not a citation") @@ -414,19 +409,6 @@ def test_solver_citations(self): self.assertIn("Virtanen2020", citations._papers_to_cite) self.assertIn("Virtanen2020", citations._citation_tags.keys()) - if pybamm.have_scikits_odes(): - citations._reset() - self.assertNotIn("Malengier2018", citations._papers_to_cite) - pybamm.ScikitsOdeSolver() - self.assertIn("Malengier2018", citations._papers_to_cite) - self.assertIn("Malengier2018", citations._citation_tags.keys()) - - citations._reset() - self.assertNotIn("Malengier2018", citations._papers_to_cite) - pybamm.ScikitsDaeSolver() - self.assertIn("Malengier2018", citations._papers_to_cite) - self.assertIn("Malengier2018", citations._citation_tags.keys()) - if pybamm.have_idaklu(): citations._reset() self.assertNotIn("Hindmarsh2005", citations._papers_to_cite) diff --git a/tests/unit/test_discretisations/test_discretisation.py b/tests/unit/test_discretisations/test_discretisation.py index 48795c9318..61e3c2bc6e 100644 --- a/tests/unit/test_discretisations/test_discretisation.py +++ b/tests/unit/test_discretisations/test_discretisation.py @@ -1074,23 +1074,6 @@ def test_check_tab_bcs_error(self): with self.assertRaisesRegex(pybamm.ModelError, "Boundary conditions"): disc.check_tab_conditions(b, bcs) - def test_process_with_no_check(self): - # create model - whole_cell = ["negative electrode", "separator", "positive electrode"] - c = pybamm.Variable("c", domain=whole_cell) - N = pybamm.grad(c) - model = pybamm.BaseModel() - model.rhs = {c: pybamm.div(N)} - model.initial_conditions = {c: pybamm.Scalar(3)} - model.boundary_conditions = { - c: {"left": (0, "Neumann"), "right": (0, "Neumann")} - } - model.variables = {"c": c, "N": N} - - # create discretisation - disc = get_discretisation_for_testing() - disc.process_model(model, check_model=False) - def test_mass_matrix_inverse(self): # get mesh mesh = get_2p1d_mesh_for_testing(ypts=5, zpts=5) @@ -1227,6 +1210,38 @@ def test_length_scale_errors(self): disc.process_model(model) def test_independent_rhs(self): + a = pybamm.Variable("a") + b = pybamm.Variable("b") + # Include a concatenation for the test + c_n = pybamm.Variable("c_n", ["negative electrode"]) + c_s = pybamm.Variable("c_s", ["separator"]) + c = pybamm.concatenation(c_n, c_s) + + model = pybamm.BaseModel() + model.rhs = {a: b, b: 0, c: -c} + model.initial_conditions = {a: 0, b: 1, c: 1} + # test edge case where variable appears twice with different names + model.variables = {"a": a, "a again": a} + mesh = get_mesh_for_testing() + spatial_methods = {"macroscale": SpatialMethodForTesting()} + disc = pybamm.Discretisation( + mesh, spatial_methods, remove_independent_variables_from_rhs=True + ) + disc.process_model(model) + self.assertEqual(len(model.rhs), 2) + self.assertEqual(model.variables["a"], model.variables["a again"]) + + def test_independent_rhs_one_equation(self): + # Test that if there is only one equation, it is not removed + a = pybamm.Variable("a") + model = pybamm.BaseModel() + model.rhs = {a: 0} + model.initial_conditions = {a: 0} + disc = pybamm.Discretisation(remove_independent_variables_from_rhs=True) + disc.process_model(model) + self.assertEqual(len(model.rhs), 1) + + def test_independent_rhs_with_event(self): a = pybamm.Variable("a") b = pybamm.Variable("b") c = pybamm.Variable("c") @@ -1237,9 +1252,10 @@ def test_independent_rhs(self): b: pybamm.Scalar(1), c: pybamm.Scalar(1), } - disc = pybamm.Discretisation() + model.events = [pybamm.Event("a=1", a - 1)] + disc = pybamm.Discretisation(remove_independent_variables_from_rhs=True) disc.process_model(model) - self.assertEqual(len(model.rhs), 2) + self.assertEqual(len(model.rhs), 3) if __name__ == "__main__": diff --git a/tests/unit/test_experiments/test_experiment.py b/tests/unit/test_experiments/test_experiment.py index 78ca39e6e8..6c342bd269 100644 --- a/tests/unit/test_experiments/test_experiment.py +++ b/tests/unit/test_experiments/test_experiment.py @@ -17,11 +17,11 @@ def test_cycle_unpacking(self): ] ) self.assertEqual( - [step.to_dict() for step in experiment.operating_conditions_steps], + [step.to_dict() for step in experiment.steps], [ { "value": 0.05, - "type": "C-rate", + "type": "CRate", "duration": 1800.0, "period": 60.0, "temperature": None, @@ -32,7 +32,7 @@ def test_cycle_unpacking(self): }, { "value": -0.2, - "type": "C-rate", + "type": "CRate", "duration": 2700.0, "period": 60.0, "temperature": None, @@ -43,7 +43,7 @@ def test_cycle_unpacking(self): }, { "value": 0.05, - "type": "C-rate", + "type": "CRate", "duration": 1800.0, "period": 60.0, "temperature": None, @@ -54,7 +54,7 @@ def test_cycle_unpacking(self): }, { "value": -0.2, - "type": "C-rate", + "type": "CRate", "duration": 2700.0, "period": 60.0, "temperature": None, @@ -67,6 +67,15 @@ def test_cycle_unpacking(self): ) self.assertEqual(experiment.cycle_lengths, [2, 1, 1]) + def test_invalid_step_type(self): + unprocessed = {1.0} + period = 1 + temperature = 300.0 + with self.assertRaisesRegex( + TypeError, "Operating conditions must be a Step object or string." + ): + pybamm.Experiment.process_steps(unprocessed, period, temperature) + def test_str_repr(self): conds = ["Discharge at 1 C for 20 seconds", "Charge at 0.5 W for 10 minutes"] experiment = pybamm.Experiment(conds) @@ -83,56 +92,38 @@ def test_str_repr(self): def test_bad_strings(self): with self.assertRaisesRegex( - TypeError, "Operating conditions should be strings or _Step objects" + TypeError, "Operating conditions must be a Step object or string." ): pybamm.Experiment([1, 2, 3]) with self.assertRaisesRegex( - TypeError, "Operating conditions should be strings or _Step objects" + TypeError, "Operating conditions must be a Step object or string." ): pybamm.Experiment([(1, 2, 3)]) - def test_deprecations(self): - with self.assertRaisesRegex(ValueError, "cccv_handling"): - pybamm.Experiment([], cccv_handling="something") - with self.assertRaisesRegex(ValueError, "drive_cycles"): - pybamm.Experiment([], drive_cycles="something") - def test_termination(self): experiment = pybamm.Experiment(["Discharge at 1 C for 20 seconds"]) self.assertEqual(experiment.termination, {}) experiment = pybamm.Experiment( - ["Discharge at 1 C for 20 seconds"], termination="80.7% capacity" + ["Discharge at 1 C for 20 seconds"], termination=["80.7% capacity"] ) self.assertEqual(experiment.termination, {"capacity": (80.7, "%")}) experiment = pybamm.Experiment( - ["Discharge at 1 C for 20 seconds"], termination="80.7 % capacity" + ["Discharge at 1 C for 20 seconds"], termination=["80.7 % capacity"] ) self.assertEqual(experiment.termination, {"capacity": (80.7, "%")}) experiment = pybamm.Experiment( - ["Discharge at 1 C for 20 seconds"], termination="4.1Ah capacity" + ["Discharge at 1 C for 20 seconds"], termination=["4.1Ah capacity"] ) self.assertEqual(experiment.termination, {"capacity": (4.1, "Ah")}) experiment = pybamm.Experiment( - ["Discharge at 1 C for 20 seconds"], termination="4.1 A.h capacity" + ["Discharge at 1 C for 20 seconds"], termination=["4.1 A.h capacity"] ) self.assertEqual(experiment.termination, {"capacity": (4.1, "Ah")}) - experiment = pybamm.Experiment( - ["Discharge at 1 C for 20 seconds"], termination="3V" - ) - self.assertEqual(experiment.termination, {"voltage": (3, "V")}) - - experiment = pybamm.Experiment( - ["Discharge at 1 C for 20 seconds"], termination=["3V", "4.1Ah capacity"] - ) - self.assertEqual( - experiment.termination, {"voltage": (3, "V"), "capacity": (4.1, "Ah")} - ) - with self.assertRaisesRegex(ValueError, "Only capacity"): experiment = pybamm.Experiment( ["Discharge at 1 C for 20 seconds"], termination="bla bla capacity bla" @@ -183,22 +174,22 @@ def test_no_initial_start_time(self): ) def test_set_next_start_time(self): - raw_op = [ - pybamm.step._Step( - "current", 1, duration=3600, start_time=datetime(2023, 1, 1, 8, 0) + raw_steps = [ + pybamm.step.Current( + 1, duration=3600, start_time=datetime(2023, 1, 1, 8, 0) ), - pybamm.step._Step("voltage", 2.5, duration=3600, start_time=None), - pybamm.step._Step( - "current", 1, duration=3600, start_time=datetime(2023, 1, 1, 12, 0) + pybamm.step.Voltage(2.5, duration=3600, start_time=None), + pybamm.step.Current( + 1, duration=3600, start_time=datetime(2023, 1, 1, 12, 0) ), - pybamm.step._Step("current", 1, duration=3600, start_time=None), - pybamm.step._Step("voltage", 2.5, duration=3600, start_time=None), - pybamm.step._Step( - "current", 1, duration=3600, start_time=datetime(2023, 1, 1, 15, 0) + pybamm.step.Current(1, duration=3600, start_time=None), + pybamm.step.Voltage(2.5, duration=3600, start_time=None), + pybamm.step.Current( + 1, duration=3600, start_time=datetime(2023, 1, 1, 15, 0) ), ] - experiment = pybamm.Experiment(raw_op) - processed_op = experiment._set_next_start_time(raw_op) + experiment = pybamm.Experiment(raw_steps) + processed_steps = experiment._set_next_start_time(raw_steps) expected_next = [ None, @@ -219,10 +210,10 @@ def test_set_next_start_time(self): ] # Test method directly - for next, end, op in zip(expected_next, expected_end, processed_op): + for next, end, steps in zip(expected_next, expected_end, processed_steps): # useful form for debugging - self.assertEqual(op.next_start_time, next) - self.assertEqual(op.end_time, end) + self.assertEqual(steps.next_start_time, next) + self.assertEqual(steps.end_time, end) # TODO: once #3176 is completed, the test should pass for # operating_conditions_steps (or equivalent) as well diff --git a/tests/unit/test_experiments/test_experiment_step_termination.py b/tests/unit/test_experiments/test_experiment_step_termination.py index ee45bcc9f8..650babf50a 100644 --- a/tests/unit/test_experiments/test_experiment_step_termination.py +++ b/tests/unit/test_experiments/test_experiment_step_termination.py @@ -1,18 +1,17 @@ # # Test the experiment step termination classes # + import pybamm -import unittest +import pytest -class TestExperimentStepTermination(unittest.TestCase): +class TestExperimentStepTermination: def test_base_termination(self): term = pybamm.step.BaseTermination(1) - self.assertEqual(term.value, 1) - - with self.assertRaises(NotImplementedError): + assert term.value == 1 + with pytest.raises(NotImplementedError): term.get_event(None, None) - - self.assertEqual(term, pybamm.step.BaseTermination(1)) - self.assertNotEqual(term, pybamm.step.BaseTermination(2)) - self.assertNotEqual(term, pybamm.step.CurrentTermination(1)) + assert term == pybamm.step.BaseTermination(1) + assert term != pybamm.step.BaseTermination(2) + assert term != pybamm.step.CurrentTermination(1) diff --git a/tests/unit/test_experiments/test_experiment_steps.py b/tests/unit/test_experiments/test_experiment_steps.py index b99ae22395..4bb686986f 100644 --- a/tests/unit/test_experiments/test_experiment_steps.py +++ b/tests/unit/test_experiments/test_experiment_steps.py @@ -9,8 +9,7 @@ class TestExperimentSteps(unittest.TestCase): def test_step(self): - step = pybamm.step._Step("current", 1, duration=3600) - self.assertEqual(step.type, "current") + step = pybamm.step.current(1, duration=3600) self.assertEqual(step.value, 1) self.assertEqual(step.duration, 3600) self.assertEqual(step.termination, []) @@ -21,8 +20,7 @@ def test_step(self): self.assertEqual(step.end_time, None) self.assertEqual(step.next_start_time, None) - step = pybamm.step._Step( - "voltage", + step = pybamm.step.voltage( 1, duration="1h", termination="2.5V", @@ -31,7 +29,6 @@ def test_step(self): tags="test", start_time=datetime(2020, 1, 1, 0, 0, 0), ) - self.assertEqual(step.type, "voltage") self.assertEqual(step.value, 1) self.assertEqual(step.duration, 3600) self.assertEqual(step.termination, [pybamm.step.VoltageTermination(2.5)]) @@ -40,42 +37,41 @@ def test_step(self): self.assertEqual(step.tags, ["test"]) self.assertEqual(step.start_time, datetime(2020, 1, 1, 0, 0, 0)) - step = pybamm.step._Step("current", 1, temperature="298K") + step = pybamm.step.current(1, temperature="298K") self.assertEqual(step.temperature, 298) with self.assertRaisesRegex(ValueError, "temperature units"): - step = pybamm.step._Step("current", 1, temperature="298T") + step = pybamm.step.current(1, temperature="298T") + + with self.assertRaisesRegex(ValueError, "time must be positive"): + pybamm.step.current(1, duration=0) def test_specific_steps(self): current = pybamm.step.current(1) - self.assertIsInstance(current, pybamm.step._Step) - self.assertEqual(current.type, "current") + self.assertIsInstance(current, pybamm.step.Current) self.assertEqual(current.value, 1) self.assertEqual(str(current), repr(current)) + self.assertEqual(current.duration, 24 * 3600) c_rate = pybamm.step.c_rate(1) - self.assertIsInstance(c_rate, pybamm.step._Step) - self.assertEqual(c_rate.type, "C-rate") + self.assertIsInstance(c_rate, pybamm.step.CRate) self.assertEqual(c_rate.value, 1) + self.assertEqual(c_rate.duration, 3600 * 2) voltage = pybamm.step.voltage(1) - self.assertIsInstance(voltage, pybamm.step._Step) - self.assertEqual(voltage.type, "voltage") + self.assertIsInstance(voltage, pybamm.step.Voltage) self.assertEqual(voltage.value, 1) rest = pybamm.step.rest() - self.assertIsInstance(rest, pybamm.step._Step) - self.assertEqual(rest.type, "current") + self.assertIsInstance(rest, pybamm.step.Current) self.assertEqual(rest.value, 0) power = pybamm.step.power(1) - self.assertIsInstance(power, pybamm.step._Step) - self.assertEqual(power.type, "power") + self.assertIsInstance(power, pybamm.step.Power) self.assertEqual(power.value, 1) resistance = pybamm.step.resistance(1) - self.assertIsInstance(resistance, pybamm.step._Step) - self.assertEqual(resistance.type, "resistance") + self.assertIsInstance(resistance, pybamm.step.Resistance) self.assertEqual(resistance.value, 1) def test_step_string(self): @@ -97,80 +93,80 @@ def test_step_string(self): expected_result = [ { - "type": "C-rate", + "type": "CRate", "value": 1.0, "duration": 1800.0, "termination": [], }, { - "type": "C-rate", + "type": "CRate", "value": 0.05, "duration": 3600.0, "termination": [], "period": 120, }, { - "type": "C-rate", + "type": "CRate", "value": -0.5, "duration": 2700.0, "termination": [], }, { "value": 1.0, - "type": "current", + "type": "Current", "duration": 1800.0, "termination": [], }, { "value": -0.2, - "type": "current", + "type": "Current", "duration": 2700.0, "termination": [], }, { "value": 1.0, - "type": "power", + "type": "Power", "duration": 1800.0, "termination": [], }, { "value": -0.2, - "type": "power", + "type": "Power", "duration": 2700.0, "termination": [], }, { "value": 0, - "type": "current", + "type": "Current", "duration": 600.0, "termination": [], }, { "value": 1, - "type": "voltage", + "type": "Voltage", "duration": 20.0, "termination": [], }, { - "type": "C-rate", + "type": "CRate", "value": -1, - "duration": None, + "duration": 7200, "termination": [pybamm.step.VoltageTermination(4.1)], }, { "value": 4.1, - "type": "voltage", - "duration": None, + "type": "Voltage", + "duration": 3600 * 24, "termination": [pybamm.step.CurrentTermination(0.05)], }, { "value": 3, - "type": "voltage", - "duration": None, + "type": "Voltage", + "duration": 3600 * 24, "termination": [pybamm.step.CrateTermination(0.02)], }, { - "type": "C-rate", + "type": "CRate", "value": 1 / 3, "duration": 7200.0, "termination": [pybamm.step.VoltageTermination(2.5)], @@ -198,7 +194,6 @@ def test_drive_cycle(self): # Create steps drive_cycle_step = pybamm.step.current(drive_cycle, temperature="-5oC") # Check drive cycle operating conditions - self.assertEqual(drive_cycle_step.type, "current") self.assertEqual(drive_cycle_step.duration, 9) self.assertEqual(drive_cycle_step.period, 1) self.assertEqual(drive_cycle_step.temperature, 273.15 - 5) @@ -217,7 +212,6 @@ def test_drive_cycle_duration(self): drive_cycle, duration=20, temperature="-5oC" ) # Check drive cycle operating conditions - self.assertEqual(drive_cycle_step.type, "current") self.assertEqual(drive_cycle_step.duration, 20) self.assertEqual(drive_cycle_step.period, 1) self.assertEqual(drive_cycle_step.temperature, 273.15 - 5) @@ -228,7 +222,6 @@ def test_drive_cycle_duration(self): drive_cycle, duration=5, temperature="-5oC" ) # Check drive cycle operating conditions - self.assertEqual(drive_cycle_step.type, "current") self.assertEqual(drive_cycle_step.duration, 5) self.assertEqual(drive_cycle_step.period, 1) self.assertEqual(drive_cycle_step.temperature, 273.15 - 5) @@ -255,14 +248,14 @@ def test_bad_strings(self): def test_start_times(self): # Test start_times - step = pybamm.step._Step( - "current", 1, duration=3600, start_time=datetime(2020, 1, 1, 0, 0, 0) + step = pybamm.step.current( + 1, duration=3600, start_time=datetime(2020, 1, 1, 0, 0, 0) ) self.assertEqual(step.start_time, datetime(2020, 1, 1, 0, 0, 0)) # Test bad start_times with self.assertRaisesRegex(TypeError, "`start_time` should be"): - pybamm.step._Step("current", 1, duration=3600, start_time="bad start_time") + pybamm.step.current(1, duration=3600, start_time="bad start_time") def test_custom_termination(self): def neg_stoich_cutoff(variables): @@ -276,6 +269,45 @@ def neg_stoich_cutoff(variables): self.assertEqual(event.name, "Negative stoichiometry cut-off [experiment]") self.assertEqual(event.expression, 2) + def test_drive_cycle_start_time(self): + # An example where start_time t>0 + t = np.array([[1, 1], [2, 2], [3, 3]]) + + with self.assertRaisesRegex(ValueError, "Drive cycle must start at t=0"): + pybamm.step.current(t) + + def test_base_custom_steps(self): + with self.assertRaises(NotImplementedError): + pybamm.step.BaseStepExplicit(None).current_value(None) + with self.assertRaises(NotImplementedError): + pybamm.step.BaseStepImplicit(None).get_submodel(None) + + def test_custom_steps(self): + def custom_step_constant(variables): + return 1 + + custom_constant = pybamm.step.CustomStepExplicit(custom_step_constant) + + self.assertEqual(custom_constant.current_value_function({}), 1) + + def custom_step_voltage(variables): + return variables["Voltage [V]"] - 4.1 + + custom_step_alg = pybamm.step.CustomStepImplicit(custom_step_voltage) + + self.assertEqual(custom_step_alg.control, "algebraic") + self.assertAlmostEqual( + custom_step_alg.current_rhs_function({"Voltage [V]": 4.2}), 0.1 + ) + + custom_step_diff = pybamm.step.CustomStepImplicit( + custom_step_voltage, control="differential" + ) + self.assertEqual(custom_step_diff.control, "differential") + + with self.assertRaisesRegex(ValueError, "control must be"): + pybamm.step.CustomStepImplicit(custom_step_voltage, control="bla") + if __name__ == "__main__": print("Add -v for more debug output") diff --git a/tests/unit/test_experiments/test_simulation_with_experiment.py b/tests/unit/test_experiments/test_simulation_with_experiment.py index 36475081c3..4b3fa3366a 100644 --- a/tests/unit/test_experiments/test_simulation_with_experiment.py +++ b/tests/unit/test_experiments/test_simulation_with_experiment.py @@ -10,6 +10,12 @@ from datetime import datetime +class ShortDurationCRate(pybamm.step.CRate): + def default_duration(self, value): + # Set a short default duration for testing early stopping due to infeasible time + return 1 + + class TestSimulationExperiment(TestCase): def test_set_up(self): experiment = pybamm.Experiment( @@ -23,27 +29,14 @@ def test_set_up(self): model = pybamm.lithium_ion.SPM() sim = pybamm.Simulation(model, experiment=experiment) sim.build_for_experiment() - C = model.default_parameter_values["Nominal cell capacity [A.h]"] self.assertEqual(sim.experiment.args, experiment.args) - op_conds = sim.experiment.operating_conditions_steps - self.assertEqual(op_conds[0].value, C / 20) - self.assertEqual(op_conds[1].value, -1) - self.assertEqual(op_conds[2].value, 4.1) - self.assertEqual(op_conds[3].value, 2) - - Crate = 1 / C - self.assertEqual( - [op.duration for op in op_conds], - [3600, 3 / Crate * 3600, 24 * 3600, 24 * 3600], - ) + steps = sim.experiment.steps model_I = sim.experiment_unique_steps_to_model[ - op_conds[1].basic_repr() + steps[1].basic_repr() ] # CC charge - model_V = sim.experiment_unique_steps_to_model[ - op_conds[2].basic_repr() - ] # CV hold + model_V = sim.experiment_unique_steps_to_model[steps[2].basic_repr()] # CV hold self.assertIn( "Current cut-off [A] [experiment]", [event.name for event in model_V.events], @@ -62,9 +55,9 @@ def test_setup_experiment_string_or_list(self): sim = pybamm.Simulation(model, experiment="Discharge at C/20 for 1 hour") sim.build_for_experiment() - self.assertEqual(len(sim.experiment.operating_conditions_steps), 1) + self.assertEqual(len(sim.experiment.steps), 1) self.assertEqual( - sim.experiment.operating_conditions_steps[0].description, + sim.experiment.steps[0].description, "Discharge at C/20 for 1 hour", ) sim = pybamm.Simulation( @@ -72,7 +65,7 @@ def test_setup_experiment_string_or_list(self): experiment=["Discharge at C/20 for 1 hour", pybamm.step.rest(60)], ) sim.build_for_experiment() - self.assertEqual(len(sim.experiment.operating_conditions_steps), 2) + self.assertEqual(len(sim.experiment.steps), 2) def test_run_experiment(self): s = pybamm.step.string @@ -82,7 +75,8 @@ def test_run_experiment(self): s("Discharge at C/20 for 1 hour", temperature="30.5oC"), s("Charge at 1 A until 4.1 V", temperature="24oC"), s("Hold at 4.1 V until C/2", temperature="24oC"), - "Discharge at 2 W for 1 hour", + "Discharge at 2 W for 10 minutes", + "Discharge at 4 Ohm for 10 minutes", ) ], temperature="-14oC", @@ -103,6 +97,9 @@ def test_run_experiment(self): np.testing.assert_array_almost_equal( sol.cycles[0].steps[3]["Power [W]"].data, 2, decimal=5 ) + np.testing.assert_array_almost_equal( + sol.cycles[0].steps[4]["Resistance [Ohm]"].data, 4, decimal=5 + ) np.testing.assert_array_equal( sol.cycles[0].steps[0]["Ambient temperature [C]"].data[0], 30.5 @@ -220,7 +217,7 @@ def test_run_experiment_drive_cycle(self): sim = pybamm.Simulation(model, experiment=experiment) sim.build_for_experiment() self.assertEqual( - sorted([repr(step) for step in experiment.operating_conditions_steps]), + sorted([step.basic_repr() for step in experiment.steps]), sorted(list(sim.experiment_unique_steps_to_model.keys())), ) @@ -281,6 +278,19 @@ def test_run_experiment_breaks_early_error(self): # Different callback - this is for coverage on the `Callback` class sol = sim.solve(callbacks=pybamm.callbacks.Callback()) + def test_run_experiment_infeasible_time(self): + experiment = pybamm.Experiment( + [ShortDurationCRate(1, termination="2.5V"), "Rest for 1 hour"] + ) + model = pybamm.lithium_ion.SPM() + parameter_values = pybamm.ParameterValues("Chen2020") + sim = pybamm.Simulation( + model, parameter_values=parameter_values, experiment=experiment + ) + sol = sim.solve() + self.assertEqual(len(sol.cycles), 1) + self.assertEqual(len(sol.cycles[0].steps), 1) + def test_run_experiment_termination_capacity(self): # with percent experiment = pybamm.Experiment( @@ -358,6 +368,60 @@ def test_run_experiment_termination_voltage(self): np.testing.assert_array_less(np.min(sol.cycles[1]["Voltage [V]"].data), 4) self.assertEqual(len(sol.cycles), 2) + def test_run_experiment_termination_time_min(self): + experiment = pybamm.Experiment( + [ + ("Discharge at 0.5C for 10 minutes", "Rest for 10 minutes"), + ] + * 5, + termination="25 min", + ) + model = pybamm.lithium_ion.SPM() + param = pybamm.ParameterValues("Chen2020") + sim = pybamm.Simulation(model, experiment=experiment, parameter_values=param) + # Test with calc_esoh=False here + sol = sim.solve(calc_esoh=False) + # Only two cycles should be completed, only 2nd cycle should go below 4V + np.testing.assert_array_less(np.max(sol.cycles[0]["Time [s]"].data), 1500) + np.testing.assert_array_equal(np.max(sol.cycles[1]["Time [s]"].data), 1500) + self.assertEqual(len(sol.cycles), 2) + + def test_run_experiment_termination_time_s(self): + experiment = pybamm.Experiment( + [ + ("Discharge at 0.5C for 10 minutes", "Rest for 10 minutes"), + ] + * 5, + termination="1500 s", + ) + model = pybamm.lithium_ion.SPM() + param = pybamm.ParameterValues("Chen2020") + sim = pybamm.Simulation(model, experiment=experiment, parameter_values=param) + # Test with calc_esoh=False here + sol = sim.solve(calc_esoh=False) + # Only two cycles should be completed, only 2nd cycle should go below 4V + np.testing.assert_array_less(np.max(sol.cycles[0]["Time [s]"].data), 1500) + np.testing.assert_array_equal(np.max(sol.cycles[1]["Time [s]"].data), 1500) + self.assertEqual(len(sol.cycles), 2) + + def test_run_experiment_termination_time_h(self): + experiment = pybamm.Experiment( + [ + ("Discharge at 0.5C for 10 minutes", "Rest for 10 minutes"), + ] + * 5, + termination="0.5 h", + ) + model = pybamm.lithium_ion.SPM() + param = pybamm.ParameterValues("Chen2020") + sim = pybamm.Simulation(model, experiment=experiment, parameter_values=param) + # Test with calc_esoh=False here + sol = sim.solve(calc_esoh=False) + # Only two cycles should be completed, only 2nd cycle should go below 4V + np.testing.assert_array_less(np.max(sol.cycles[0]["Time [s]"].data), 1800) + np.testing.assert_array_equal(np.max(sol.cycles[1]["Time [s]"].data), 1800) + self.assertEqual(len(sol.cycles), 2) + def test_save_at_cycles(self): experiment = pybamm.Experiment( [ @@ -458,7 +522,7 @@ def test_inputs(self): # Change a parameter to an input param = pybamm.ParameterValues("Marquis2019") - param["Negative electrode diffusivity [m2.s-1]"] = ( + param["Negative particle diffusivity [m2.s-1]"] = ( pybamm.InputParameter("Dsn") * 3.9e-14 ) @@ -776,13 +840,47 @@ def test_experiment_start_time_identical_steps(self): sim.solve(calc_esoh=False) # Check that there are 4 steps - self.assertEqual(len(experiment.operating_conditions_steps), 4) + self.assertEqual(len(experiment.steps), 4) # Check that there are only 2 unique steps self.assertEqual(len(sim.experiment.unique_steps), 2) # Check that there are only 3 built models (unique steps + padding rest) - self.assertEqual(len(sim.op_conds_to_built_models), 3) + self.assertEqual(len(sim.steps_to_built_models), 3) + + def test_experiment_custom_steps(self): + model = pybamm.lithium_ion.SPM() + + # Explicit control + def custom_step_constant(variables): + return 1 + + custom_constant = pybamm.step.CustomStepExplicit( + custom_step_constant, duration=1, period=0.1 + ) + + experiment = pybamm.Experiment([custom_constant]) + sim = pybamm.Simulation(model, experiment=experiment) + sol = sim.solve() + np.testing.assert_array_equal(sol["Current [A]"].data, 1) + + # Implicit control (algebraic) + def custom_step_voltage(variables): + return 100 * (variables["Voltage [V]"] - 4.2) + + for control in ["differential"]: + with self.subTest(control=control): + custom_step_alg = pybamm.step.CustomStepImplicit( + custom_step_voltage, control=control, duration=100, period=10 + ) + + experiment = pybamm.Experiment([custom_step_alg]) + sim = pybamm.Simulation(model, experiment=experiment) + sol = sim.solve() + # sol.plot() + np.testing.assert_array_almost_equal( + sol["Voltage [V]"].data[2:], 4.2, decimal=3 + ) def test_experiment_custom_termination(self): def neg_stoich_cutoff(variables): diff --git a/tests/unit/test_expression_tree/test_binary_operators.py b/tests/unit/test_expression_tree/test_binary_operators.py index b6cbe093eb..9efcbb90f2 100644 --- a/tests/unit/test_expression_tree/test_binary_operators.py +++ b/tests/unit/test_expression_tree/test_binary_operators.py @@ -9,7 +9,7 @@ from scipy.sparse import coo_matrix import pybamm -from pybamm.util import have_optional_dependency +import sympy EMPTY_DOMAINS = { "primary": [], @@ -790,7 +790,6 @@ def test_inner_simplifications(self): self.assertEqual(pybamm.inner(a3, a3).evaluate(), 9) def test_to_equation(self): - sympy = have_optional_dependency("sympy") # Test print_name pybamm.Addition.print_name = "test" self.assertEqual(pybamm.Addition(1, 2).to_equation(), sympy.Symbol("test")) diff --git a/tests/unit/test_expression_tree/test_concatenations.py b/tests/unit/test_expression_tree/test_concatenations.py index 691b6a7ee2..03a4b5a894 100644 --- a/tests/unit/test_expression_tree/test_concatenations.py +++ b/tests/unit/test_expression_tree/test_concatenations.py @@ -8,7 +8,7 @@ import numpy as np import pybamm -from pybamm.util import have_optional_dependency +import sympy from tests import get_discretisation_for_testing, get_mesh_for_testing @@ -371,7 +371,6 @@ def test_numpy_concatenation(self): ) def test_to_equation(self): - sympy = have_optional_dependency("sympy") a = pybamm.Symbol("a", domain="test a") b = pybamm.Symbol("b", domain="test b") func_symbol = sympy.Symbol(r"\begin{cases}a\\b\end{cases}") diff --git a/tests/unit/test_expression_tree/test_functions.py b/tests/unit/test_expression_tree/test_functions.py index 33e11459ab..c4b5fb4368 100644 --- a/tests/unit/test_expression_tree/test_functions.py +++ b/tests/unit/test_expression_tree/test_functions.py @@ -9,19 +9,11 @@ from scipy import special import pybamm -from pybamm.util import have_optional_dependency - - -def test_function(arg): - return arg + arg - - -def test_multi_var_function(arg1, arg2): - return arg1 + arg2 - - -def test_multi_var_function_cube(arg1, arg2): - return arg1 + arg2**3 +import sympy +from tests import ( + function_test, + multi_var_function_test, +) class TestFunction(TestCase): @@ -31,16 +23,16 @@ def test_number_input(self): self.assertIsInstance(log.children[0], pybamm.Scalar) self.assertEqual(log.evaluate(), np.log(10)) - summ = pybamm.Function(test_multi_var_function, 1, 2) + summ = pybamm.Function(multi_var_function_test, 1, 2) self.assertIsInstance(summ.children[0], pybamm.Scalar) self.assertIsInstance(summ.children[1], pybamm.Scalar) self.assertEqual(summ.evaluate(), 3) def test_function_of_one_variable(self): a = pybamm.Symbol("a") - funca = pybamm.Function(test_function, a) - self.assertEqual(funca.name, "function (test_function)") - self.assertEqual(str(funca), "test_function(a)") + funca = pybamm.Function(function_test, a) + self.assertEqual(funca.name, "function (function_test)") + self.assertEqual(str(funca), "function_test(a)") self.assertEqual(funca.children[0].name, a.name) b = pybamm.Scalar(1) @@ -59,69 +51,23 @@ def test_function_of_one_variable(self): def test_diff(self): a = pybamm.StateVector(slice(0, 1)) - b = pybamm.StateVector(slice(1, 2)) - y = np.array([5]) - func = pybamm.Function(test_function, a) - self.assertEqual(func.diff(a).evaluate(y=y), 2) - self.assertEqual(func.diff(func).evaluate(), 1) - func = pybamm.sin(a) - self.assertEqual(func.evaluate(y=y), np.sin(a.evaluate(y=y))) - self.assertEqual(func.diff(a).evaluate(y=y), np.cos(a.evaluate(y=y))) - func = pybamm.exp(a) - self.assertEqual(func.evaluate(y=y), np.exp(a.evaluate(y=y))) - self.assertEqual(func.diff(a).evaluate(y=y), np.exp(a.evaluate(y=y))) - - # multiple variables - func = pybamm.Function(test_multi_var_function, 4 * a, 3 * a) - self.assertEqual(func.diff(a).evaluate(y=y), 7) - func = pybamm.Function(test_multi_var_function, 4 * a, 3 * b) - self.assertEqual(func.diff(a).evaluate(y=np.array([5, 6])), 4) - self.assertEqual(func.diff(b).evaluate(y=np.array([5, 6])), 3) - func = pybamm.Function(test_multi_var_function_cube, 4 * a, 3 * b) - self.assertEqual(func.diff(a).evaluate(y=np.array([5, 6])), 4) - self.assertEqual( - func.diff(b).evaluate(y=np.array([5, 6])), 3 * 3 * (3 * 6) ** 2 - ) - - # exceptions - func = pybamm.Function( - test_multi_var_function_cube, 4 * a, 3 * b, derivative="derivative" - ) - with self.assertRaises(ValueError): + func = pybamm.Function(function_test, a) + with self.assertRaisesRegex( + NotImplementedError, "Derivative of base Function class is not implemented" + ): func.diff(a) - def test_function_of_multiple_variables(self): - a = pybamm.Variable("a") - b = pybamm.Parameter("b") - func = pybamm.Function(test_multi_var_function, a, b) - self.assertEqual(func.name, "function (test_multi_var_function)") - self.assertEqual(str(func), "test_multi_var_function(a, b)") - self.assertEqual(func.children[0].name, a.name) - self.assertEqual(func.children[1].name, b.name) - - # test eval and diff - a = pybamm.StateVector(slice(0, 1)) - b = pybamm.StateVector(slice(1, 2)) - y = np.array([5, 2]) - func = pybamm.Function(test_multi_var_function, a, b) - - self.assertEqual(func.evaluate(y=y), 7) - self.assertEqual(func.diff(a).evaluate(y=y), 1) - self.assertEqual(func.diff(b).evaluate(y=y), 1) - self.assertEqual(func.diff(func).evaluate(), 1) - def test_exceptions(self): a = pybamm.Variable("a", domain="something") b = pybamm.Variable("b", domain="something else") with self.assertRaises(pybamm.DomainError): - pybamm.Function(test_multi_var_function, a, b) + pybamm.Function(multi_var_function_test, a, b) def test_function_unnamed(self): fun = pybamm.Function(np.cos, pybamm.t) self.assertEqual(fun.name, "function (cos)") def test_to_equation(self): - sympy = have_optional_dependency("sympy") a = pybamm.Symbol("a", domain="test") # Test print_name @@ -130,26 +76,28 @@ def test_to_equation(self): self.assertEqual(func.to_equation(), sympy.Symbol("test")) # Test Arcsinh - self.assertEqual(pybamm.Arcsinh(a).to_equation(), sympy.asinh(a)) + self.assertEqual(pybamm.Arcsinh(a).to_equation(), sympy.asinh("a")) # Test Arctan - self.assertEqual(pybamm.Arctan(a).to_equation(), sympy.atan(a)) + self.assertEqual(pybamm.Arctan(a).to_equation(), sympy.atan("a")) # Test Exp - self.assertEqual(pybamm.Exp(a).to_equation(), sympy.exp(a)) + self.assertEqual(pybamm.Exp(a).to_equation(), sympy.exp("a")) # Test log - self.assertEqual(pybamm.Log(54.0).to_equation(), sympy.log(54.0)) + value = 54.0 + self.assertEqual(pybamm.Log(value).to_equation(), sympy.log(value)) # Test sinh - self.assertEqual(pybamm.Sinh(a).to_equation(), sympy.sinh(a)) + self.assertEqual(pybamm.Sinh(a).to_equation(), sympy.sinh("a")) # Test Function - self.assertEqual(pybamm.Function(np.log, 10).to_equation(), 10.0) + value = 10 + self.assertEqual(pybamm.Function(np.log, value).to_equation(), value) def test_to_from_json_error(self): a = pybamm.Symbol("a") - funca = pybamm.Function(test_function, a) + funca = pybamm.Function(function_test, a) with self.assertRaises(NotImplementedError): funca.to_json() @@ -504,6 +452,49 @@ def test_erfc(self): ) +class TestNonObjectFunctions(TestCase): + def test_normal_pdf(self): + x = pybamm.InputParameter("x") + mu = pybamm.InputParameter("mu") + sigma = pybamm.InputParameter("sigma") + fun = pybamm.normal_pdf(x, mu, sigma) + self.assertEqual( + fun.evaluate(inputs={"x": 0, "mu": 0, "sigma": 1}), 1 / np.sqrt(2 * np.pi) + ) + self.assertEqual( + fun.evaluate(inputs={"x": 2, "mu": 2, "sigma": 10}), + 1 / np.sqrt(2 * np.pi) / 10, + ) + self.assertAlmostEqual(fun.evaluate(inputs={"x": 100, "mu": 0, "sigma": 1}), 0) + self.assertAlmostEqual(fun.evaluate(inputs={"x": -100, "mu": 0, "sigma": 1}), 0) + self.assertGreater( + fun.evaluate(inputs={"x": 1, "mu": 0, "sigma": 1}), + fun.evaluate(inputs={"x": 1, "mu": 0, "sigma": 2}), + ) + self.assertGreater( + fun.evaluate(inputs={"x": -1, "mu": 0, "sigma": 1}), + fun.evaluate(inputs={"x": -1, "mu": 0, "sigma": 2}), + ) + + def test_normal_cdf(self): + x = pybamm.InputParameter("x") + mu = pybamm.InputParameter("mu") + sigma = pybamm.InputParameter("sigma") + fun = pybamm.normal_cdf(x, mu, sigma) + self.assertEqual(fun.evaluate(inputs={"x": 0, "mu": 0, "sigma": 1}), 0.5) + self.assertEqual(fun.evaluate(inputs={"x": 2, "mu": 2, "sigma": 10}), 0.5) + self.assertAlmostEqual(fun.evaluate(inputs={"x": 100, "mu": 0, "sigma": 1}), 1) + self.assertAlmostEqual(fun.evaluate(inputs={"x": -100, "mu": 0, "sigma": 1}), 0) + self.assertGreater( + fun.evaluate(inputs={"x": 1, "mu": 0, "sigma": 1}), + fun.evaluate(inputs={"x": 1, "mu": 0, "sigma": 2}), + ) + self.assertLess( + fun.evaluate(inputs={"x": -1, "mu": 0, "sigma": 1}), + fun.evaluate(inputs={"x": -1, "mu": 0, "sigma": 2}), + ) + + if __name__ == "__main__": print("Add -v for more debug output") import sys diff --git a/tests/unit/test_expression_tree/test_independent_variable.py b/tests/unit/test_expression_tree/test_independent_variable.py index b748a6fbe9..f747b60d40 100644 --- a/tests/unit/test_expression_tree/test_independent_variable.py +++ b/tests/unit/test_expression_tree/test_independent_variable.py @@ -6,7 +6,7 @@ import pybamm -from pybamm.util import have_optional_dependency +import sympy class TestIndependentVariable(TestCase): @@ -64,7 +64,6 @@ def test_spatial_variable_edge(self): self.assertTrue(x.evaluates_on_edges("primary")) def test_to_equation(self): - sympy = have_optional_dependency("sympy") # Test print_name func = pybamm.IndependentVariable("a") func.print_name = "test" diff --git a/tests/unit/test_expression_tree/test_interpolant.py b/tests/unit/test_expression_tree/test_interpolant.py index 1d1a55e85c..f5ded9cf8e 100644 --- a/tests/unit/test_expression_tree/test_interpolant.py +++ b/tests/unit/test_expression_tree/test_interpolant.py @@ -45,15 +45,6 @@ def test_errors(self): (np.ones(10), np.ones(12)), np.ones((10, 12)), pybamm.Symbol("a") ) - def test_warnings(self): - with self.assertWarnsRegex(Warning, "cubic spline"): - pybamm.Interpolant( - np.linspace(0, 1, 10), - np.ones(10), - pybamm.Symbol("a"), - interpolator="cubic spline", - ) - def test_interpolation(self): x = np.linspace(0, 1, 200) y = pybamm.StateVector(slice(0, 2)) @@ -80,6 +71,11 @@ def test_interpolation(self): interp.evaluate(y=np.array([2]))[:, 0], np.array([np.nan]) ) + def test_interpolation_float(self): + x = np.linspace(0, 1, 200) + interp = pybamm.Interpolant(x, 2 * x, 0.5) + self.assertEqual(interp.evaluate(), 1) + def test_interpolation_1_x_2d_y(self): x = np.linspace(0, 1, 200) y = np.tile(2 * x, (10, 1)).T @@ -137,7 +133,7 @@ def f(x, y): # check also works for cubic interp = pybamm.Interpolant(x_in, data, (var1, var2), interpolator="cubic") value = interp.evaluate(y=np.array([1, 5])) - np.testing.assert_equal(value, f(1, 5)) + np.testing.assert_almost_equal(value, f(1, 5), decimal=3) # Test raising error if data is not 2D data_3d = np.zeros((11, 22, 33)) @@ -235,7 +231,7 @@ def f(x, y, z): x_in, data, (var1, var2, var3), interpolator="cubic" ) value = interp.evaluate(y=np.array([1, 5, 8])) - np.testing.assert_equal(value, f(1, 5, 8)) + np.testing.assert_almost_equal(value, f(1, 5, 8), decimal=3) # Test raising error if data is not 3D data_4d = np.zeros((11, 22, 33, 5)) @@ -330,12 +326,26 @@ def test_diff(self): decimal=3, ) + # test 2D interpolation diff fails + x = (np.arange(-5.01, 5.01, 0.05), np.arange(-5.01, 5.01, 0.01)) + xx, yy = np.meshgrid(x[0], x[1], indexing="ij") + z = np.sin(xx**2 + yy**2) + var1 = pybamm.StateVector(slice(0, 1)) + var2 = pybamm.StateVector(slice(1, 2)) + # linear + interp = pybamm.Interpolant(x, z, (var1, var2), interpolator="linear") + with self.assertRaisesRegex( + NotImplementedError, + "differentiation not implemented for functions with more than one child", + ): + interp.diff(var1) + def test_processing(self): x = np.linspace(0, 1, 200) y = pybamm.StateVector(slice(0, 2)) interp = pybamm.Interpolant(x, 2 * x, y) - self.assertEqual(interp, interp.new_copy()) + self.assertEqual(interp, interp.create_copy()) def test_to_from_json(self): x = np.linspace(0, 1, 10) @@ -373,6 +383,7 @@ def test_to_from_json(self): ], "interpolator": "linear", "extrapolate": True, + "_num_derivatives": 0, } # check correct writing to json diff --git a/tests/unit/test_expression_tree/test_operations/test_convert_to_casadi.py b/tests/unit/test_expression_tree/test_operations/test_convert_to_casadi.py index db50ac8c92..20c2e40db0 100644 --- a/tests/unit/test_expression_tree/test_operations/test_convert_to_casadi.py +++ b/tests/unit/test_expression_tree/test_operations/test_convert_to_casadi.py @@ -219,7 +219,7 @@ def test_interpolation_2d(self): # linear y_test = np.array([0.4, 0.6]) Y = (2 * x).sum(axis=1).reshape(*[len(el) for el in x_]) - for interpolator in ["linear"]: + for interpolator in ["linear", "cubic"]: interp = pybamm.Interpolant(x_, Y, y, interpolator=interpolator) interp_casadi = interp.to_casadi(y=casadi_y) f = casadi.Function("f", [casadi_y], [interp_casadi]) @@ -314,22 +314,6 @@ def test_concatenations(self): y_eval = np.linspace(0, 1, expr.size) self.assert_casadi_equal(f(y_eval), casadi.SX(expr.evaluate(y=y_eval))) - def test_convert_differentiated_function(self): - a = pybamm.InputParameter("a") - b = pybamm.InputParameter("b") - - def myfunction(x, y): - return x + y**3 - - f = pybamm.Function(myfunction, a, b).diff(a) - self.assert_casadi_equal( - f.to_casadi(inputs={"a": 1, "b": 2}), casadi.DM(1), evalf=True - ) - f = pybamm.Function(myfunction, a, b).diff(b) - self.assert_casadi_equal( - f.to_casadi(inputs={"a": 1, "b": 2}), casadi.DM(12), evalf=True - ) - def test_convert_input_parameter(self): casadi_t = casadi.MX.sym("t") casadi_y = casadi.MX.sym("y", 10) diff --git a/tests/unit/test_expression_tree/test_operations/test_copy.py b/tests/unit/test_expression_tree/test_operations/test_copy.py index 6800f9092f..ca6ac6c448 100644 --- a/tests/unit/test_expression_tree/test_operations/test_copy.py +++ b/tests/unit/test_expression_tree/test_operations/test_copy.py @@ -9,9 +9,10 @@ class TestCopy(TestCase): - def test_symbol_new_copy(self): + def test_symbol_create_copy(self): a = pybamm.Parameter("a") b = pybamm.Parameter("b") + c = pybamm.IndependentVariable("Variable_c") v_n = pybamm.Variable("v", "negative electrode") v_n_2D = pybamm.Variable( "v", @@ -32,6 +33,7 @@ def test_symbol_new_copy(self): a**b, -a, abs(a), + c, pybamm.Function(np.sin, a), pybamm.FunctionParameter("function", {"a": a}), pybamm.grad(v_n), @@ -62,7 +64,337 @@ def test_symbol_new_copy(self): pybamm.Equality(a, b), pybamm.EvaluateAt(a, 0), ]: - self.assertEqual(symbol, symbol.new_copy()) + self.assertEqual(symbol, symbol.create_copy()) + self.assertEqual(symbol.print_name, symbol.create_copy().print_name) + + def test_symbol_create_copy_new_children(self): + a = pybamm.Parameter("a") + b = pybamm.Parameter("b") + + # binary operations + for symbol_ab, symbol_ba in zip( + [ + a + b, + a - b, + a * b, + a / b, + a**b, + pybamm.minimum(a, b), + pybamm.maximum(a, b), + pybamm.Equality(a, b), + ], + [ + b + a, + b - a, + b * a, + b / a, + b**a, + pybamm.minimum(b, a), + pybamm.maximum(b, a), + pybamm.Equality(b, a), + ], + ): + new_symbol = symbol_ab.create_copy(new_children=[b, a]) + self.assertEqual(new_symbol, symbol_ba) + self.assertEqual(new_symbol.print_name, symbol_ba.print_name) + + # unary operations + for symbol_a, symbol_b in zip( + [ + -a, + abs(a), + pybamm.Function(np.sin, a), + pybamm.PrimaryBroadcast(a, "domain"), + pybamm.FullBroadcast(a, "domain", {"secondary": "other domain"}), + pybamm.NotConstant(a), + pybamm.EvaluateAt(a, 0), + ], + [ + -b, + abs(b), + pybamm.Function(np.sin, b), + pybamm.PrimaryBroadcast(b, "domain"), + pybamm.FullBroadcast(b, "domain", {"secondary": "other domain"}), + pybamm.NotConstant(b), + pybamm.EvaluateAt(b, 0), + ], + ): + new_symbol = symbol_a.create_copy(new_children=[b]) + self.assertEqual(new_symbol, symbol_b) + self.assertEqual(new_symbol.print_name, symbol_b.print_name) + + v_n = pybamm.Variable("v", "negative electrode") + w_n = pybamm.Variable("w", "negative electrode") + x_n = pybamm.standard_spatial_vars.x_n + + for symbol_v, symbol_w in zip( + [ + pybamm.grad(v_n), + pybamm.upwind(v_n), + pybamm.IndefiniteIntegral(v_n, x_n), + pybamm.BackwardIndefiniteIntegral(v_n, x_n), + pybamm.BoundaryValue(v_n, "right"), + pybamm.BoundaryGradient(v_n, "right"), + pybamm.SecondaryBroadcast(v_n, "current collector"), + ], + [ + pybamm.grad(w_n), + pybamm.upwind(w_n), + pybamm.IndefiniteIntegral(w_n, x_n), + pybamm.BackwardIndefiniteIntegral(w_n, x_n), + pybamm.BoundaryValue(w_n, "right"), + pybamm.BoundaryGradient(w_n, "right"), + pybamm.SecondaryBroadcast(w_n, "current collector"), + ], + ): + new_symbol = symbol_v.create_copy(new_children=[w_n]) + self.assertEqual(new_symbol, symbol_w) + self.assertEqual(new_symbol.print_name, symbol_w.print_name) + + self.assertEqual( + pybamm.div(pybamm.grad(v_n)).create_copy(new_children=[pybamm.grad(w_n)]), + pybamm.div(pybamm.grad(w_n)), + ) + + v_s = pybamm.Variable("v", "separator") + mesh = get_mesh_for_testing() + + for symbol_n, symbol_s in zip( + [ + pybamm.concatenation(v_n, v_s), + pybamm.DomainConcatenation([v_n, v_s], mesh), + ], + [ + pybamm.concatenation(v_s, v_n), + pybamm.DomainConcatenation([v_s, v_n], mesh), + ], + ): + new_symbol = symbol_n.create_copy(new_children=[v_s, v_n]) + self.assertEqual(new_symbol, symbol_s) + self.assertEqual(new_symbol.print_name, symbol_s.print_name) + + self.assertEqual( + pybamm.NumpyConcatenation(a, b, v_s).create_copy(new_children=[b, a, v_n]), + pybamm.NumpyConcatenation(b, a, v_n), + ) + + v_n_2D = pybamm.Variable( + "v", + domain="negative particle", + auxiliary_domains={"secondary": "negative electrode"}, + ) + w_n_2D = pybamm.Variable( + "w", + domain="negative particle", + auxiliary_domains={"secondary": "negative electrode"}, + ) + vec = pybamm.Vector([1, 2, 3, 4, 5]) + vec_b = pybamm.Vector([6, 7, 8, 9, 10]) + mat = pybamm.Matrix([[1, 2], [3, 4]]) + mat_b = pybamm.Matrix([[5, 6], [7, 8]]) + + self.assertEqual( + pybamm.TertiaryBroadcast(v_n_2D, "current collector").create_copy( + new_children=[w_n_2D] + ), + pybamm.TertiaryBroadcast(w_n_2D, "current collector"), + ) + self.assertEqual( + pybamm.Index(vec, 1).create_copy(new_children=[vec_b]), + pybamm.Index(vec_b, 1), + ) + self.assertEqual( + pybamm.SparseStack(mat, mat).create_copy(new_children=[mat_b, mat_b]), + pybamm.SparseStack(mat_b, mat_b), + ) + + def test_create_copy_new_children_binary_error(self): + a = pybamm.Parameter("a") + b = pybamm.Parameter("b") + + with self.assertRaisesRegex(ValueError, "must have exactly two children"): + (a + b).create_copy(new_children=[a]) + + def test_create_copy_new_children_scalars(self): + a = pybamm.Scalar(2) + b = pybamm.Scalar(5) + + self.assertEqual((a + b).create_copy(), a + b) + # a+b produces a scalar, not an addition object. + with self.assertRaisesRegex( + ValueError, "Cannot create a copy of a scalar with new children" + ): + (a + b).create_copy(new_children=[a, b]) + + self.assertEqual(pybamm.Addition(a, b).create_copy(), pybamm.Scalar(7)) + self.assertEqual( + pybamm.Addition(a, b).create_copy(perform_simplifications=False), + pybamm.Addition(a, b), + ) + + c = pybamm.Scalar(4) + d = pybamm.Scalar(8) + + self.assertEqual( + pybamm.Addition(a, b).create_copy(new_children=[c, d]), pybamm.Scalar(12) + ) + self.assertEqual( + pybamm.Addition(a, b).create_copy( + new_children=[c, d], perform_simplifications=False + ), + pybamm.Addition(c, d), + ) + + def test_create_copy_new_children_unary_error(self): + vec = pybamm.Vector([1, 2, 3, 4, 5]) + vec_b = pybamm.Vector([6, 7, 8, 9, 10]) + + I = pybamm.Index(vec, 1) + + with self.assertRaisesRegex(ValueError, "must have exactly one child"): + I.create_copy(new_children=[vec, vec_b]) + + def test_unary_create_copy_no_simplification(self): + a = pybamm.Parameter("a") + b = pybamm.Parameter("b") + + for symbol_a, symbol_b in zip( + [ + pybamm.Negate(a), + pybamm.AbsoluteValue(a), + pybamm.sign(a), + # boundaryvalue + ], + [ + pybamm.Negate(b), + pybamm.AbsoluteValue(b), + pybamm.Sign(b), + ], + ): + self.assertEqual( + symbol_a.create_copy(new_children=[b], perform_simplifications=False), + symbol_b, + ) + + v_n = pybamm.Variable("v", "negative electrode") + w_n = pybamm.Variable("w", "negative electrode") + + self.assertEqual( + pybamm.grad(v_n).create_copy( + new_children=[w_n], perform_simplifications=False + ), + pybamm.Gradient(w_n), + ) + + self.assertEqual( + pybamm.div(pybamm.grad(v_n)).create_copy( + new_children=[pybamm.grad(w_n)], perform_simplifications=False + ), + pybamm.Divergence(pybamm.grad(w_n)), + ) + + var = pybamm.Variable("var", domain="test") + ible = pybamm.Variable("ible", domain="test") + left_extrap = pybamm.BoundaryValue(var, "left") + + self.assertEqual( + left_extrap.create_copy(new_children=[ible], perform_simplifications=False), + pybamm.BoundaryValue(ible, "left"), + ) + + def test_unary_create_copy_no_simplification_averages(self): + a_v = pybamm.Variable("a", domain=["negative electrode"]) + c = pybamm.Variable("a", domain=["current collector"]) + + for average, var in zip( + [ + pybamm.XAverage, + pybamm.RAverage, + pybamm.ZAverage, + pybamm.YZAverage, + ], + [a_v, a_v, c, c], + ): + self.assertEqual( + average(var).create_copy( + new_children=[var], perform_simplifications=False + ), + average(var), + ) + + d = pybamm.Symbol("d", domain=["negative particle size"]) + R = pybamm.SpatialVariable("R", ["negative particle size"]) + geo = pybamm.geometric_parameters + f_a_dist = geo.n.prim.f_a_dist(R) + + s_a = pybamm.SizeAverage(d, f_a_dist=f_a_dist) + + self.assertEqual( + s_a.create_copy(new_children=[d], perform_simplifications=False), + pybamm.SizeAverage(d, f_a_dist=f_a_dist), + ) + + def test_concatenation_create_copy_no_simplification(self): + a = pybamm.Parameter("a") + b = pybamm.Parameter("b") + v_n = pybamm.Variable("v", "negative electrode") + v_s = pybamm.Variable("v", "separator") + mesh = get_mesh_for_testing() + + for symbol_n, symbol_s in zip( + [ + pybamm.concatenation(v_n, v_s), + pybamm.DomainConcatenation([v_n, v_s], mesh), + ], + [ + pybamm.ConcatenationVariable(v_s, v_n), + pybamm.DomainConcatenation([v_s, v_n], mesh), + ], + ): + self.assertEqual( + symbol_n.create_copy( + new_children=[v_s, v_n], perform_simplifications=False + ), + symbol_s, + ) + + with self.assertRaisesRegex( + NotImplementedError, "should always be copied using simplification checks" + ): + pybamm.NumpyConcatenation(a, b, v_s).create_copy( + new_children=[a, b], perform_simplifications=False + ) + + def test_function_create_copy_no_simplification(self): + a = pybamm.Parameter("a") + b = pybamm.Parameter("b") + + self.assertEqual( + pybamm.Function(np.sin, a).create_copy( + new_children=[b], perform_simplifications=False + ), + pybamm.Function(np.sin, b), + ) + + def test_symbol_new_copy_warning(self): + with self.assertWarns(DeprecationWarning): + pybamm.Symbol("a").new_copy() + + def test_symbol_copy_tree(self): + model = pybamm.lithium_ion.DFN() + geometry = model.default_geometry + param = model.default_parameter_values + param.process_model(model) + param.process_geometry(geometry) + mesh = pybamm.Mesh(geometry, model.default_submesh_types, model.default_var_pts) + disc = pybamm.Discretisation(mesh, model.default_spatial_methods) + disc.process_model(model) + + y = model.concatenated_initial_conditions.evaluate() + copied_rhs = model.concatenated_rhs.create_copy() + np.testing.assert_array_equal( + model.concatenated_rhs.evaluate(None, y), copied_rhs.evaluate(None, y) + ) if __name__ == "__main__": diff --git a/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py b/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py index 552e79bc7e..b02c75f386 100644 --- a/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py +++ b/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py @@ -12,14 +12,10 @@ if pybamm.have_jax(): import jax - - -def test_function(arg): - return arg + arg - - -def test_function2(arg1, arg2): - return arg1 + arg2 +from tests import ( + function_test, + multi_var_function_test, +) class TestEvaluate(TestCase): @@ -93,10 +89,10 @@ def test_find_symbols(self): # test function constant_symbols = OrderedDict() variable_symbols = OrderedDict() - expr = pybamm.Function(test_function, a) + expr = pybamm.Function(function_test, a) pybamm.find_symbols(expr, constant_symbols, variable_symbols) self.assertEqual(next(iter(constant_symbols.keys())), expr.id) - self.assertEqual(next(iter(constant_symbols.values())), test_function) + self.assertEqual(next(iter(constant_symbols.values())), function_test) self.assertEqual(next(iter(variable_symbols.keys())), a.id) self.assertEqual(list(variable_symbols.keys())[1], expr.id) self.assertEqual(next(iter(variable_symbols.values())), "y[0:1]") @@ -283,9 +279,9 @@ def test_to_python(self): expr = a + b constant_str, variable_str = pybamm.to_python(expr) expected_str = ( - "var_[0-9m]+ = y\[0:1\].*\\n" - "var_[0-9m]+ = y\[1:2\].*\\n" - "var_[0-9m]+ = var_[0-9m]+ \+ var_[0-9m]+" + r"var_[0-9m]+ = y\[0:1\].*\n" + r"var_[0-9m]+ = y\[1:2\].*\n" + r"var_[0-9m]+ = var_[0-9m]+ \+ var_[0-9m]+" ) self.assertRegex(variable_str, expected_str) @@ -306,12 +302,12 @@ def test_evaluator_python(self): self.assertEqual(result, 3) # test function(a*b) - expr = pybamm.Function(test_function, a * b) + expr = pybamm.Function(function_test, a * b) evaluator = pybamm.EvaluatorPython(expr) result = evaluator(t=None, y=np.array([[2], [3]])) self.assertEqual(result, 12) - expr = pybamm.Function(test_function2, a, b) + expr = pybamm.Function(multi_var_function_test, a, b) evaluator = pybamm.EvaluatorPython(expr) result = evaluator(t=None, y=np.array([[2], [3]])) self.assertEqual(result, 5) @@ -486,7 +482,7 @@ def test_evaluator_jax(self): self.assertEqual(result, 3) # test function(a*b) - expr = pybamm.Function(test_function, a * b) + expr = pybamm.Function(function_test, a * b) evaluator = pybamm.EvaluatorJax(expr) result = evaluator(t=None, y=np.array([[2], [3]])) self.assertEqual(result, 12) diff --git a/tests/unit/test_expression_tree/test_operations/test_jac.py b/tests/unit/test_expression_tree/test_operations/test_jac.py index d3572cafdc..bec38d7243 100644 --- a/tests/unit/test_expression_tree/test_operations/test_jac.py +++ b/tests/unit/test_expression_tree/test_operations/test_jac.py @@ -10,10 +10,6 @@ from tests import get_mesh_for_testing -def test_multi_var_function(arg1, arg2): - return arg1 + arg2 - - class TestJacobian(TestCase): def test_variable_is_statevector(self): a = pybamm.Symbol("a") @@ -216,12 +212,6 @@ def test_functions(self): dfunc_dy = func.jac(y).evaluate(y=y0) np.testing.assert_array_equal(0, dfunc_dy) - # several children - func = pybamm.Function(test_multi_var_function, 2 * y, 3 * y) - jacobian = np.diag(5 * np.ones(4)) - dfunc_dy = func.jac(y).evaluate(y=y0) - np.testing.assert_array_equal(jacobian, dfunc_dy.toarray()) - def test_index(self): vec = pybamm.StateVector(slice(0, 5)) ind = pybamm.Index(vec, 3) diff --git a/tests/unit/test_expression_tree/test_operations/test_jac_2D.py b/tests/unit/test_expression_tree/test_operations/test_jac_2D.py index 5dafef1309..2be71d85e4 100644 --- a/tests/unit/test_expression_tree/test_operations/test_jac_2D.py +++ b/tests/unit/test_expression_tree/test_operations/test_jac_2D.py @@ -7,11 +7,9 @@ import numpy as np import unittest from scipy.sparse import eye -from tests import get_1p1d_discretisation_for_testing - - -def test_multi_var_function(arg1, arg2): - return arg1 + arg2 +from tests import ( + get_1p1d_discretisation_for_testing, +) class TestJacobian(TestCase): @@ -201,12 +199,6 @@ def test_functions(self): dfunc_dy = func.jac(y).evaluate(y=y0) np.testing.assert_array_equal(0, dfunc_dy) - # several children - func = pybamm.Function(test_multi_var_function, 2 * y, 3 * y) - jacobian = np.diag(5 * np.ones(8)) - dfunc_dy = func.jac(y).evaluate(y=y0) - np.testing.assert_array_equal(jacobian, dfunc_dy.toarray()) - def test_jac_of_domain_concatenation(self): # create mesh disc = get_1p1d_discretisation_for_testing() diff --git a/tests/unit/test_expression_tree/test_operations/test_latexify.py b/tests/unit/test_expression_tree/test_operations/test_latexify.py index 7e0703534e..0340a1d53f 100644 --- a/tests/unit/test_expression_tree/test_operations/test_latexify.py +++ b/tests/unit/test_expression_tree/test_operations/test_latexify.py @@ -1,6 +1,7 @@ """ Tests for the latexify.py """ + from tests import TestCase import os import platform diff --git a/tests/unit/test_expression_tree/test_parameter.py b/tests/unit/test_expression_tree/test_parameter.py index 6940ac38fe..efd9dcbfba 100644 --- a/tests/unit/test_expression_tree/test_parameter.py +++ b/tests/unit/test_expression_tree/test_parameter.py @@ -6,7 +6,7 @@ import unittest import pybamm -from pybamm.util import have_optional_dependency +import sympy class TestParameter(TestCase): @@ -20,7 +20,6 @@ def test_evaluate_for_shape(self): self.assertIsInstance(a.evaluate_for_shape(), numbers.Number) def test_to_equation(self): - sympy = have_optional_dependency("sympy") func = pybamm.Parameter("test_string") func1 = pybamm.Parameter("test_name") @@ -64,7 +63,7 @@ def test_copy(self): a = pybamm.Parameter("a") func = pybamm.FunctionParameter("func", {"2a": 2 * a}) - new_func = func.new_copy() + new_func = func.create_copy() self.assertEqual(func.input_names, new_func.input_names) def test_print_input_names(self): @@ -107,7 +106,6 @@ def _myfun(x): self.assertEqual(_myfun(x).print_name, None) def test_function_parameter_to_equation(self): - sympy = have_optional_dependency("sympy") func = pybamm.FunctionParameter("test", {"x": pybamm.Scalar(1)}) func1 = pybamm.FunctionParameter("func", {"var": pybamm.Variable("var")}) diff --git a/tests/unit/test_expression_tree/test_printing/test_print_name.py b/tests/unit/test_expression_tree/test_printing/test_print_name.py index 554e6567f1..9d74d6f1ab 100644 --- a/tests/unit/test_expression_tree/test_printing/test_print_name.py +++ b/tests/unit/test_expression_tree/test_printing/test_print_name.py @@ -1,6 +1,7 @@ """ Tests for the print_name.py """ + from tests import TestCase import unittest @@ -31,9 +32,9 @@ def test_prettify_print_name(self): # Test greek letters self.assertEqual(param2.delta.print_name, r"\delta") - # Test new_copy() + # Test create_copy() a_n = param2.n.prim.a - a_n.new_copy() + self.assertEqual(a_n.create_copy().print_name, r"a_{\mathrm{n}}") # Test eps eps_n = pybamm.Variable("eps_n") diff --git a/tests/unit/test_expression_tree/test_printing/test_sympy_overrides.py b/tests/unit/test_expression_tree/test_printing/test_sympy_overrides.py index de3ff08c43..4b19c7d822 100644 --- a/tests/unit/test_expression_tree/test_printing/test_sympy_overrides.py +++ b/tests/unit/test_expression_tree/test_printing/test_sympy_overrides.py @@ -1,17 +1,17 @@ """ Tests for the sympy_overrides.py """ + from tests import TestCase import unittest import pybamm from pybamm.expression_tree.printing.sympy_overrides import custom_print_func -from pybamm.util import have_optional_dependency +import sympy class TestCustomPrint(TestCase): def test_print_Derivative(self): - sympy = have_optional_dependency("sympy") # Test force_partial der1 = sympy.Derivative("y", "x") der1.force_partial = True diff --git a/tests/unit/test_expression_tree/test_symbol.py b/tests/unit/test_expression_tree/test_symbol.py index 9a7939c66d..668c076907 100644 --- a/tests/unit/test_expression_tree/test_symbol.py +++ b/tests/unit/test_expression_tree/test_symbol.py @@ -12,7 +12,7 @@ import pybamm from pybamm.expression_tree.binary_operators import _Heaviside -from pybamm.util import have_optional_dependency +import sympy class TestSymbol(TestCase): @@ -122,7 +122,7 @@ def test_symbol_methods(self): self.assertIsInstance(-a, pybamm.Negate) self.assertIsInstance(abs(a), pybamm.AbsoluteValue) # special cases - self.assertEqual(-(-a), a) + self.assertEqual(-(-a), a) # noqa: B002 self.assertEqual(-(a - b), b - a) self.assertEqual(abs(abs(a)), abs(a)) @@ -169,8 +169,12 @@ def test_symbol_methods(self): def test_symbol_create_copy(self): a = pybamm.Symbol("a") - with self.assertRaisesRegex(NotImplementedError, "method self.new_copy()"): - a.create_copy() + new_a = a.create_copy() + self.assertEqual(new_a, a) + + b = pybamm.Symbol("b") + new_b = b.create_copy(new_children=[a]) + self.assertEqual(new_b, pybamm.Symbol("b", children=[a])) def test_sigmoid(self): # Test that smooth heaviside is used when the setting is changed @@ -485,7 +489,6 @@ def test_test_shape(self): (y1 + y2).test_shape() def test_to_equation(self): - sympy = have_optional_dependency("sympy") self.assertEqual(pybamm.Symbol("test").to_equation(), sympy.Symbol("test")) def test_numpy_array_ufunc(self): diff --git a/tests/unit/test_expression_tree/test_unary_operators.py b/tests/unit/test_expression_tree/test_unary_operators.py index 6ae6b62d05..2f476e3d09 100644 --- a/tests/unit/test_expression_tree/test_unary_operators.py +++ b/tests/unit/test_expression_tree/test_unary_operators.py @@ -7,9 +7,11 @@ import numpy as np from scipy.sparse import diags +import sympy +from sympy.vector.operators import Divergence as sympy_Divergence +from sympy.vector.operators import Gradient as sympy_Gradient import pybamm -from pybamm.util import have_optional_dependency class TestUnaryOperators(TestCase): @@ -221,6 +223,14 @@ def test_gradient(self): grad = pybamm.grad(a) self.assertEqual(grad, pybamm.PrimaryBroadcastToEdges(0, "test domain")) + # gradient of a secondary broadcast moves the secondary out of the gradient + a = pybamm.Symbol("a", domain="test domain") + a_broad = pybamm.SecondaryBroadcast(a, "another domain") + grad = pybamm.grad(a_broad) + self.assertEqual( + grad, pybamm.SecondaryBroadcast(pybamm.grad(a), "another domain") + ) + # otherwise gradient should work a = pybamm.Symbol("a", domain="test domain") grad = pybamm.Gradient(a) @@ -678,12 +688,6 @@ def test_not_constant(self): self.assertFalse((2 * a).is_constant()) def test_to_equation(self): - sympy = have_optional_dependency("sympy") - sympy_Divergence = have_optional_dependency( - "sympy.vector.operators", "Divergence" - ) - sympy_Gradient = have_optional_dependency("sympy.vector.operators", "Gradient") - a = pybamm.Symbol("a", domain="negative particle") b = pybamm.Symbol("b", domain="current collector") c = pybamm.Symbol("c", domain="test") @@ -695,10 +699,11 @@ def test_to_equation(self): self.assertEqual(pybamm.Floor(-2.5).to_equation(), sympy.Symbol("test")) # Test Negate - self.assertEqual(pybamm.Negate(4).to_equation(), -4.0) + value = 4 + self.assertEqual(pybamm.Negate(value).to_equation(), -value) # Test AbsoluteValue - self.assertEqual(pybamm.AbsoluteValue(-4).to_equation(), 4.0) + self.assertEqual(pybamm.AbsoluteValue(-value).to_equation(), value) # Test Gradient self.assertEqual(pybamm.Gradient(a).to_equation(), sympy_Gradient("a")) @@ -706,7 +711,7 @@ def test_to_equation(self): # Test Divergence self.assertEqual( pybamm.Divergence(pybamm.Gradient(a)).to_equation(), - sympy_Divergence(sympy_Gradient(a)), + sympy_Divergence(sympy_Gradient("a")), ) # Test BoundaryValue @@ -736,7 +741,7 @@ def test_explicit_time_integral(self): self.assertEqual(expr.child, pybamm.Parameter("param")) self.assertEqual(expr.initial_condition, pybamm.Scalar(1)) self.assertEqual(expr.name, "explicit time integral") - self.assertEqual(expr.new_copy(), expr) + self.assertEqual(expr.create_copy(), expr) self.assertFalse(expr.is_constant()) def test_to_from_json(self): diff --git a/tests/unit/test_expression_tree/test_variable.py b/tests/unit/test_expression_tree/test_variable.py index 0d5aa251d2..42ab2c0e22 100644 --- a/tests/unit/test_expression_tree/test_variable.py +++ b/tests/unit/test_expression_tree/test_variable.py @@ -7,7 +7,7 @@ import numpy as np import pybamm -from pybamm.util import have_optional_dependency +import sympy class TestVariable(TestCase): @@ -55,7 +55,6 @@ def test_variable_bounds(self): pybamm.Variable("var", bounds=(1, 1)) def test_to_equation(self): - sympy = have_optional_dependency("sympy") # Test print_name func = pybamm.Variable("test_string") func.print_name = "test" diff --git a/tests/unit/test_models/test_base_model.py b/tests/unit/test_models/test_base_model.py index 538765c48d..7caa4c94b8 100644 --- a/tests/unit/test_models/test_base_model.py +++ b/tests/unit/test_models/test_base_model.py @@ -6,6 +6,8 @@ import platform import subprocess # nosec import unittest +from io import StringIO +import sys import casadi import numpy as np @@ -160,6 +162,242 @@ def test_read_parameters(self): } model.print_parameter_info() + def test_get_parameter_info(self): + model = pybamm.BaseModel() + a = pybamm.InputParameter("a") + b = pybamm.InputParameter("b", "test") + c = pybamm.InputParameter("c") + d = pybamm.InputParameter("d") + e = pybamm.InputParameter("e") + f = pybamm.InputParameter("f") + g = pybamm.Parameter("g") + h = pybamm.Parameter("h") + i = pybamm.Parameter("i") + + u = pybamm.Variable("u") + v = pybamm.Variable("v") + model.rhs = {u: -u * a} + model.algebraic = {v: v - b} + model.initial_conditions = {u: c, v: d} + model.events = [pybamm.Event("u=e", u - e)] + model.variables = {"v+f+i": v + f + i} + model.boundary_conditions = { + u: {"left": (g, "Dirichlet"), "right": (0, "Neumann")}, + v: {"left": (0, "Dirichlet"), "right": (h, "Neumann")}, + } + + parameter_info = model.get_parameter_info() + self.assertEqual(parameter_info["a"][1], "InputParameter") + self.assertEqual(parameter_info["b"][1], "InputParameter in ['test']") + self.assertIn("c", parameter_info) + self.assertIn("d", parameter_info) + self.assertIn("e", parameter_info) + self.assertIn("f", parameter_info) + self.assertEqual(parameter_info["g"][1], "Parameter") + self.assertIn("h", parameter_info) + self.assertIn("i", parameter_info) + + def test_get_parameter_info_submodel(self): + submodel = pybamm.lithium_ion.SPM().submodels["electrolyte diffusion"] + + class SubModel1(pybamm.BaseSubModel): + def get_fundamental_variables(self): + u = pybamm.Variable("u") + + variables = {"u": u} + return variables + + def get_coupled_variables(self, variables): + x = pybamm.Parameter("x") + w = pybamm.InputParameter("w") + f = pybamm.InputParameter("f", "test") + variables.update({"w": w, "x": x, "f": f}) + return variables + + def set_rhs(self, variables): + a = pybamm.InputParameter("a") + u = variables["u"] + self.rhs = {u: -u * a} + + def set_boundary_conditions(self, variables): + g = pybamm.Parameter("g") + u = variables["u"] + self.boundary_conditions = { + u: {"left": (g, "Dirichlet"), "right": (0, "Neumann")}, + } + + def set_initial_conditions(self, variables): + c = pybamm.FunctionParameter("c", {}) + u = variables["u"] + self.initial_conditions = {u: c} + + def set_events(self, variables): + e = pybamm.InputParameter("e") + u = variables["u"] + self.events = [pybamm.Event("u=e", u - e)] + + class SubModel2(pybamm.BaseSubModel): + def get_fundamental_variables(self): + v = pybamm.Variable("v") + i = pybamm.FunctionParameter("i", {}) + variables = {"v": v, "i": i} + return variables + + def set_rhs(self, variables): + b = pybamm.InputParameter("b", "test") + v = variables["v"] + self.rhs = {v: v - b} + + def set_boundary_conditions(self, variables): + h = pybamm.Parameter("h") + v = variables["v"] + self.boundary_conditions = { + v: {"left": (0, "Dirichlet"), "right": (h, "Neumann")}, + } + + def set_initial_conditions(self, variables): + d = pybamm.FunctionParameter("d", {}) + v = variables["v"] + self.initial_conditions = {v: d} + + sub1 = SubModel1(None) + sub2 = SubModel2(None) + model = pybamm.BaseModel() + model.submodels = {"sub1": sub1, "sub2": sub2} + model.build_model() + + parameter_info = model.get_parameter_info(by_submodel=True) + + expected_error_message = "Cannot use get_parameter_info" + + with self.assertRaisesRegex(NotImplementedError, expected_error_message): + submodel.get_parameter_info(by_submodel=True) + + with self.assertRaisesRegex(NotImplementedError, expected_error_message): + submodel.get_parameter_info(by_submodel=False) + + self.assertIn("a", parameter_info["sub1"]) + self.assertIn("b", parameter_info["sub2"]) + self.assertEqual(parameter_info["sub1"]["a"][1], "InputParameter") + self.assertEqual(parameter_info["sub1"]["w"][1], "InputParameter") + self.assertEqual(parameter_info["sub1"]["e"][1], "InputParameter") + self.assertEqual(parameter_info["sub1"]["g"][1], "Parameter") + self.assertEqual(parameter_info["sub1"]["x"][1], "Parameter") + self.assertEqual(parameter_info["sub1"]["f"][1], "InputParameter in ['test']") + self.assertEqual(parameter_info["sub2"]["b"][1], "InputParameter in ['test']") + self.assertEqual(parameter_info["sub2"]["h"][1], "Parameter") + self.assertEqual( + parameter_info["sub1"]["c"][1], + "FunctionParameter with inputs(s) ''", + ) + self.assertEqual( + parameter_info["sub2"]["d"][1], + "FunctionParameter with inputs(s) ''", + ) + self.assertEqual( + parameter_info["sub2"]["i"][1], + "FunctionParameter with inputs(s) ''", + ) + + def test_print_parameter_info(self): + model = pybamm.BaseModel() + a = pybamm.InputParameter("a") + b = pybamm.InputParameter("b", "test") + c = pybamm.FunctionParameter("c", {}) + d = pybamm.FunctionParameter("d", {}) + e = pybamm.InputParameter("e") + f = pybamm.InputParameter("f") + g = pybamm.Parameter("g") + h = pybamm.Parameter("h") + i = pybamm.Parameter("i") + + u = pybamm.Variable("u") + v = pybamm.Variable("v") + + sub1 = pybamm.BaseSubModel(None) + sub1.rhs = {u: -u * a} + sub1.initial_conditions = {u: c} + sub1.variables = {"u": u} + sub1.boundary_conditions = { + u: {"left": (g, "Dirichlet"), "right": (0, "Neumann")}, + } + sub2 = pybamm.BaseSubModel(None) + sub2.algebraic = {v: v - b} + sub2.variables = {"v": v, "v+f+i": v + f + i} + sub2.initial_conditions = {v: d} + sub2.boundary_conditions = { + v: {"left": (0, "Dirichlet"), "right": (h, "Neumann")}, + } + sub3 = pybamm.BaseSubModel(None) + model.submodels = {"sub1": sub1, "sub2": sub2, "sub3": sub3} + model.events = [pybamm.Event("u=e", u - e)] + model.build_model() + captured_output = StringIO() + sys.stdout = captured_output + + model.print_parameter_info() + sys.stdout = sys.__stdout__ + + result = captured_output.getvalue().strip() + self.assertIn("a", result) + self.assertIn("b", result) + self.assertIn("InputParameter", result) + self.assertIn("InputParameter in ['test']", result) + self.assertIn("Parameter", result) + self.assertIn("FunctionParameter with inputs(s) ''", result) + + def test_print_parameter_info_submodel(self): + model = pybamm.BaseModel() + a = pybamm.InputParameter("a") + b = pybamm.InputParameter("b", "test") + c = pybamm.FunctionParameter("c", {}) + d = pybamm.FunctionParameter("d", {}) + e = pybamm.InputParameter("e") + f = pybamm.InputParameter("f") + g = pybamm.Parameter("g") + h = pybamm.Parameter("h") + i = pybamm.Parameter("i") + u = pybamm.Variable("u") + v = pybamm.Variable("v") + + sub1 = pybamm.BaseSubModel(None) + sub1.rhs = {u: -u * a} + sub1.initial_conditions = {u: c} + sub1.variables = {"u": u} + sub1.boundary_conditions = { + u: {"left": (g, "Dirichlet"), "right": (0, "Neumann")}, + } + sub2 = pybamm.BaseSubModel(None) + sub2.algebraic = {v: v - b} + sub2.variables = {"v": v, "v+f+i": v + f + i} + sub2.initial_conditions = {v: d} + sub2.boundary_conditions = { + v: {"left": (0, "Dirichlet"), "right": (h, "Neumann")}, + } + sub3 = pybamm.BaseSubModel(None) + model.submodels = {"sub1": sub1, "sub2": sub2, "sub3": sub3} + model.events = [pybamm.Event("u=e", u - e)] + model.build_model() + captured_output = StringIO() + sys.stdout = captured_output + + model.print_parameter_info(by_submodel=True) + sys.stdout = sys.__stdout__ + + result = captured_output.getvalue().strip() + self.assertIn("'sub1' submodel parameters:", result) + self.assertIn("'sub2' submodel parameters:", result) + self.assertIn("Parameter", result) + self.assertIn("InputParameter", result) + self.assertIn("FunctionParameter with inputs(s) ''", result) + self.assertIn("InputParameter in ['test']", result) + self.assertIn("g", result) + self.assertIn("a", result) + self.assertIn("c", result) + self.assertIn("h", result) + self.assertIn("b", result) + self.assertIn("d", result) + def test_read_input_parameters(self): # Read input parameters from different parts of the model model = pybamm.BaseModel() diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py index c56cd2304c..caff0cda5d 100644 --- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py +++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py @@ -26,6 +26,7 @@ 'dimensionality': 0 (possible: [0, 1, 2]) 'electrolyte conductivity': 'default' (possible: ['default', 'full', 'leading order', 'composite', 'integrated']) 'exchange-current density': 'single' (possible: ['single', 'current sigmoid']) +'heat of mixing': 'false' (possible: ['false', 'true']) 'hydrolysis': 'false' (possible: ['false', 'true']) 'intercalation kinetics': 'symmetric Butler-Volmer' (possible: ['symmetric Butler-Volmer', 'asymmetric Butler-Volmer', 'linear', 'Marcus', 'Marcus-Hush-Chidsey', 'MSMR']) 'interface utilisation': 'full' (possible: ['full', 'constant', 'current-driven']) @@ -33,7 +34,7 @@ 'lithium plating porosity change': 'false' (possible: ['false', 'true']) 'loss of active material': 'stress-driven' (possible: ['none', 'stress-driven', 'reaction-driven', 'current-driven', 'stress and reaction-driven']) 'number of MSMR reactions': 'none' (possible: ['none']) -'open-circuit potential': 'single' (possible: ['single', 'current sigmoid', 'MSMR']) +'open-circuit potential': 'single' (possible: ['single', 'current sigmoid', 'MSMR', 'Wycisk']) 'operating mode': 'current' (possible: ['current', 'voltage', 'power', 'differential power', 'explicit power', 'resistance', 'differential resistance', 'explicit resistance', 'CCCV']) 'particle': 'Fickian diffusion' (possible: ['Fickian diffusion', 'fast diffusion', 'uniform profile', 'quadratic profile', 'quartic profile', 'MSMR']) 'particle mechanics': 'swelling only' (possible: ['none', 'swelling only', 'swelling and cracking']) @@ -48,6 +49,7 @@ 'surface form': 'differential' (possible: ['false', 'differential', 'algebraic']) 'thermal': 'x-full' (possible: ['isothermal', 'lumped', 'x-lumped', 'x-full']) 'total interfacial current density as a state': 'false' (possible: ['false', 'true']) +'transport efficiency': 'Bruggeman' (possible: ['Bruggeman', 'ordered packing', 'hyperbola of revolution', 'overlapping spheres', 'tortuosity factor', 'random overlapping cylinders', 'heterogeneous catalyst', 'cation-exchange membrane']) 'working electrode': 'both' (possible: ['both', 'positive']) 'x-average side reactions': 'false' (possible: ['false', 'true']) """ @@ -372,6 +374,15 @@ def test_options(self): } ) + # thermal heat of mixing + with self.assertRaisesRegex(NotImplementedError, "Heat of mixing"): + pybamm.BaseBatteryModel( + { + "heat of mixing": "true", + "particle size": "distribution", + } + ) + # phases with self.assertRaisesRegex(pybamm.OptionError, "multiple particle phases"): pybamm.BaseBatteryModel({"particle phases": "2", "surface form": "false"}) diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py index 24ccd55d79..50fac7a04e 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py @@ -82,3 +82,7 @@ def test_well_posed_asymmetric_ec_reaction_limited_sei(self): def test_well_posed_lumped_thermal(self): options = {"thermal": "lumped"} self.check_well_posedness(options) + + def test_well_posed_lumped_thermal_hom(self): + options = {"thermal": "lumped", "heat of mixing": "true"} + self.check_well_posedness(options) diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py index f4e3c3cceb..7d190875a6 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py @@ -67,6 +67,76 @@ def test_well_posed_thermal_2plus1D(self): } self.check_well_posedness(options) + def test_well_posed_isothermal_heat_source_hom(self): + options = { + "calculate heat source for isothermal models": "true", + "thermal": "isothermal", + "heat of mixing": "true", + } + self.check_well_posedness(options) + + def test_well_posed_2plus1D_hom(self): + options = { + "current collector": "potential pair", + "dimensionality": 1, + "heat of mixing": "true", + } + self.check_well_posedness(options) + + options = { + "current collector": "potential pair", + "dimensionality": 2, + "heat of mixing": "true", + } + self.check_well_posedness(options) + + def test_well_posed_lumped_thermal_model_1D_hom(self): + options = {"thermal": "lumped", "heat of mixing": "true"} + self.check_well_posedness(options) + + def test_well_posed_x_full_thermal_model_hom(self): + options = { + "thermal": "x-full", + "heat of mixing": "true", + } + self.check_well_posedness(options) + + def test_well_posed_lumped_thermal_1plus1D_hom(self): + options = { + "current collector": "potential pair", + "dimensionality": 1, + "thermal": "lumped", + "heat of mixing": "true", + } + self.check_well_posedness(options) + + def test_well_posed_lumped_thermal_2plus1D_hom(self): + options = { + "current collector": "potential pair", + "dimensionality": 2, + "thermal": "lumped", + "heat of mixing": "true", + } + self.check_well_posedness(options) + + def test_well_posed_thermal_1plus1D_hom(self): + options = { + "current collector": "potential pair", + "dimensionality": 1, + "thermal": "x-lumped", + "heat of mixing": "true", + } + self.check_well_posedness(options) + + def test_well_posed_thermal_2plus1D_hom(self): + options = { + "current collector": "potential pair", + "dimensionality": 2, + "thermal": "x-lumped", + "heat of mixing": "true", + } + self.check_well_posedness(options) + def test_well_posed_contact_resistance(self): options = {"contact resistance": "true"} self.check_well_posedness(options) @@ -350,6 +420,44 @@ def external_circuit_function(variables): options = {"operating mode": external_circuit_function} self.check_well_posedness(options) + def test_well_posed_external_circuit_function_1plus1D(self): + def external_circuit_function(variables): + I = variables["Current [A]"] + V = variables["Voltage [V]"] + return ( + V + + I + - pybamm.FunctionParameter( + "Function", {"Time [s]": pybamm.t}, print_name="test_fun" + ) + ) + + options = { + "current collector": "potential pair", + "dimensionality": 1, + "operating mode": external_circuit_function, + } + self.check_well_posedness(options) + + def test_well_posed_external_circuit_function_2plus1D(self): + def external_circuit_function(variables): + I = variables["Current [A]"] + V = variables["Voltage [V]"] + return ( + V + + I + - pybamm.FunctionParameter( + "Function", {"Time [s]": pybamm.t}, print_name="test_fun" + ) + ) + + options = { + "current collector": "potential pair", + "dimensionality": 2, + "operating mode": external_circuit_function, + } + self.check_well_posedness(options) + def test_well_posed_particle_phases(self): options = {"particle phases": "2"} self.check_well_posedness(options) @@ -360,6 +468,10 @@ def test_well_posed_particle_phases(self): options = {"particle phases": ("1", "2")} self.check_well_posedness(options) + def test_well_posed_particle_phases_thermal(self): + options = {"particle phases": "2", "thermal": "lumped"} + self.check_well_posedness(options) + def test_well_posed_particle_phases_sei(self): options = {"particle phases": "2", "SEI": "ec reaction limited"} self.check_well_posedness(options) @@ -368,6 +480,10 @@ def test_well_posed_current_sigmoid_ocp(self): options = {"open-circuit potential": "current sigmoid"} self.check_well_posedness(options) + def test_well_posed_wycisk_ocp(self): + options = {"open-circuit potential": "Wycisk"} + self.check_well_posedness(options) + def test_well_posed_msmr(self): options = { "open-circuit potential": "MSMR", @@ -390,6 +506,38 @@ def test_well_posed_psd(self): options = {"particle size": "distribution", "surface form": "algebraic"} self.check_well_posedness(options) + def test_well_posed_transport_efficiency_Bruggeman(self): + options = {"transport efficiency": "Bruggeman"} + self.check_well_posedness(options) + + def test_well_posed_transport_efficiency_ordered_packing(self): + options = {"transport efficiency": "ordered packing"} + self.check_well_posedness(options) + + def test_well_posed_transport_efficiency_overlapping_spheres(self): + options = {"transport efficiency": "overlapping spheres"} + self.check_well_posedness(options) + + def test_well_posed_transport_efficiency_random_overlapping_cylinders(self): + options = {"transport efficiency": "random overlapping cylinders"} + self.check_well_posedness(options) + + def test_well_posed_transport_efficiency_heterogeneous_catalyst(self): + options = {"transport efficiency": "heterogeneous catalyst"} + self.check_well_posedness(options) + + def test_well_posed_transport_efficiency_cation_exchange_membrane(self): + options = {"transport efficiency": "cation-exchange membrane"} + self.check_well_posedness(options) + + def test_well_posed_transport_efficiency_hyperbola(self): + options = {"transport efficiency": "hyperbola of revolution"} + self.check_well_posedness(options) + + def test_well_posed_transport_efficiency_tortuosity_factor(self): + options = {"transport efficiency": "tortuosity factor"} + self.check_well_posedness(options) + def test_well_posed_composite_kinetic_hysteresis(self): options = { "particle phases": ("2", "1"), diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_basic_models.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_basic_models.py index f2f5a5ef40..2f00bb260c 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_basic_models.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_basic_models.py @@ -11,9 +11,6 @@ def test_dfn_well_posed(self): model = pybamm.lithium_ion.BasicDFN() model.check_well_posedness() - copy = model.new_copy() - copy.check_well_posedness() - def test_spm_well_posed(self): model = pybamm.lithium_ion.BasicSPM() model.check_well_posedness() @@ -23,26 +20,6 @@ def test_dfn_half_cell_well_posed(self): model = pybamm.lithium_ion.BasicDFNHalfCell(options=options) model.check_well_posedness() - def test_dfn_half_cell_simulation_with_experiment_error(self): - options = {"working electrode": "positive"} - model = pybamm.lithium_ion.BasicDFNHalfCell(options=options) - experiment = pybamm.Experiment( - [("Discharge at C/10 for 10 hours or until 3.5 V")] - ) - with self.assertRaisesRegex( - NotImplementedError, - "BasicDFNHalfCell is not compatible with experiment simulations.", - ): - pybamm.Simulation(model, experiment=experiment) - - def test_basic_dfn_half_cell_simulation(self): - model = pybamm.lithium_ion.BasicDFNHalfCell( - options={"working electrode": "positive"} - ) - sim = pybamm.Simulation(model=model) - sim.solve([0, 100]) - self.assertTrue(isinstance(sim.solution, pybamm.solvers.solution.Solution)) - def test_dfn_composite_well_posed(self): model = pybamm.lithium_ion.BasicDFNComposite() model.check_well_posedness() diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py index d7e95247e0..20fc69e541 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py @@ -35,6 +35,20 @@ def test_well_posed_current_sigmoid_ocp_with_psd(self): } self.check_well_posedness(options) + def test_well_posed_wycisk_ocp_with_psd(self): + options = { + "open-circuit potential": "Wycisk", + "particle size": "distribution", + } + self.check_well_posedness(options) + + def test_well_posed_wycisk_ocp_with_composite(self): + options = { + "open-circuit potential": (("Wycisk", "single"), "single"), + "particle phases": ("2", "1"), + } + self.check_well_posedness(options) + def test_well_posed_external_circuit_explicit_power(self): options = {"operating mode": "explicit power"} self.check_well_posedness(options) diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py index e5e79a6ae4..dd7d35b683 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py @@ -43,14 +43,6 @@ def test_known_solution(self): energy = esoh_solver.theoretical_energy_integral(inputs) self.assertAlmostEqual(sol[key], energy, places=5) - # should still work with old inputs - n_Li = parameter_values.evaluate(param.n_Li_particles_init) - inputs = {"V_min": 3, "V_max": 4.2, "n_Li": n_Li, "C_n": Q_n, "C_p": Q_p} - - # Solve the model and check outputs - sol = esoh_solver.solve(inputs) - self.assertAlmostEqual(sol["Q_Li"], Q_Li, places=5) - def test_known_solution_cell_capacity(self): param = pybamm.LithiumIonParameters() parameter_values = pybamm.ParameterValues("Mohtat2020") @@ -243,15 +235,12 @@ def test_error(self): class TestElectrodeSOHHalfCell(TestCase): def test_known_solution(self): model = pybamm.lithium_ion.ElectrodeSOHHalfCell() - param = pybamm.LithiumIonParameters({"working electrode": "positive"}) parameter_values = pybamm.ParameterValues("Xu2019") + Q_w = parameter_values.evaluate(param.p.Q_init) sim = pybamm.Simulation(model, parameter_values=parameter_values) - V_min = 3.5 V_max = 4.2 - Q_w = parameter_values.evaluate(param.p.Q_init) - # Solve the model and check outputs sol = sim.solve([0], inputs={"Q_w": Q_w}) self.assertAlmostEqual(sol["Uw(x_100)"].data[0], V_max, places=5) @@ -376,6 +365,30 @@ def test_error(self): 2, parameter_values_half_cell ) + with self.assertRaisesRegex( + ValueError, "Known value must be cell capacity or cyclable lithium capacity" + ): + pybamm.lithium_ion.ElectrodeSOHSolver( + parameter_values, known_value="something else" + ) + + with self.assertRaisesRegex( + ValueError, "Known value must be cell capacity or cyclable lithium capacity" + ): + param_MSMR = pybamm.lithium_ion.MSMR( + {"number of MSMR reactions": "3"} + ).param + pybamm.models.full_battery_models.lithium_ion.electrode_soh._ElectrodeSOHMSMR( + param=param_MSMR, known_value="something else" + ) + + with self.assertRaisesRegex( + ValueError, "Known value must be cell capacity or cyclable lithium capacity" + ): + pybamm.models.full_battery_models.lithium_ion.electrode_soh._ElectrodeSOH( + known_value="something else" + ) + class TestGetInitialOCP(TestCase): def test_get_initial_ocp(self): diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py index 88049c0c63..389aa55849 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py @@ -116,6 +116,13 @@ def test_msmr(self): model = pybamm.lithium_ion.MPM(options) model.check_well_posedness() + def test_wycisk_ocp(self): + options = { + "open-circuit potential": "Wycisk", + } + model = pybamm.lithium_ion.MPM(options) + model.check_well_posedness() + class TestMPMExternalCircuits(TestCase): def test_well_posed_voltage(self): diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_newman_tobias.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_newman_tobias.py index 4d65804156..5369d94b29 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_newman_tobias.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_newman_tobias.py @@ -19,6 +19,9 @@ def test_electrolyte_options(self): def test_well_posed_particle_phases(self): pass # skip this test + def test_well_posed_particle_phases_thermal(self): + pass # Skip this test + def test_well_posed_particle_phases_sei(self): pass # skip this test diff --git a/tests/unit/test_models/test_model_info.py b/tests/unit/test_models/test_model_info.py index 936c9d8449..144d763bf1 100644 --- a/tests/unit/test_models/test_model_info.py +++ b/tests/unit/test_models/test_model_info.py @@ -9,11 +9,11 @@ class TestModelInfo(TestCase): def test_find_parameter_info(self): model = pybamm.lithium_ion.SPM() - model.info("Negative electrode diffusivity [m2.s-1]") + model.info("Negative particle diffusivity [m2.s-1]") model = pybamm.lithium_ion.SPMe() - model.info("Negative electrode diffusivity [m2.s-1]") + model.info("Negative particle diffusivity [m2.s-1]") model = pybamm.lithium_ion.DFN() - model.info("Negative electrode diffusivity [m2.s-1]") + model.info("Negative particle diffusivity [m2.s-1]") model.info("Not a parameter") diff --git a/tests/unit/test_parameters/test_base_parameters.py b/tests/unit/test_parameters/test_base_parameters.py index 11879f01ce..6c87cdcd88 100644 --- a/tests/unit/test_parameters/test_base_parameters.py +++ b/tests/unit/test_parameters/test_base_parameters.py @@ -1,6 +1,7 @@ """ Tests for the base_parameters.py """ + from tests import TestCase import pybamm import unittest @@ -11,23 +12,23 @@ def test_getattr__(self): param = pybamm.LithiumIonParameters() # ending in _n / _s / _p with self.assertRaisesRegex(AttributeError, "param.n.L"): - getattr(param, "L_n") + param.L_n with self.assertRaisesRegex(AttributeError, "param.s.L"): - getattr(param, "L_s") + param.L_s with self.assertRaisesRegex(AttributeError, "param.p.L"): - getattr(param, "L_p") + param.L_p # _n_ in the name with self.assertRaisesRegex(AttributeError, "param.n.prim.c_max"): - getattr(param, "c_n_max") + param.c_n_max # _n_ or _p_ not in name with self.assertRaisesRegex( AttributeError, "has no attribute 'c_n_not_a_parameter" ): - getattr(param, "c_n_not_a_parameter") + param.c_n_not_a_parameter with self.assertRaisesRegex(AttributeError, "has no attribute 'c_s_test"): - getattr(pybamm.electrical_parameters, "c_s_test") + pybamm.electrical_parameters.c_s_test self.assertEqual(param.n.cap_init, param.n.Q_init) self.assertEqual(param.p.prim.cap_init, param.p.prim.Q_init) diff --git a/tests/unit/test_parameters/test_bpx.py b/tests/unit/test_parameters/test_bpx.py index e131e906c4..916eb8d161 100644 --- a/tests/unit/test_parameters/test_bpx.py +++ b/tests/unit/test_parameters/test_bpx.py @@ -8,6 +8,8 @@ import json import pybamm import copy +import numpy as np +import pytest class TestBPX(TestCase): @@ -107,28 +109,51 @@ def setUp(self): } def test_bpx(self): - bpx_obj = copy.copy(self.base) + bpx_objs = [ + { + **copy.deepcopy(self.base), + "Parameterisation": { + **copy.deepcopy(self.base["Parameterisation"]), + "Negative electrode": { + **copy.deepcopy( + self.base["Parameterisation"]["Negative electrode"] + ), + "Diffusivity [m2.s-1]": "8.3e-13 * exp(-13.4 * x) + 9.6e-15", # new diffusivity + }, + }, + }, + copy.copy(self.base), + ] + + model = pybamm.lithium_ion.DFN() + experiment = pybamm.Experiment( + [ + "Discharge at C/5 for 1 hour", + ] + ) filename = "tmp.json" - with tempfile.NamedTemporaryFile( - suffix=filename, delete=False, mode="w" - ) as tmp: - # write to a tempory file so we can - # get the source later on using inspect.getsource - # (as long as the file still exists) - json.dump(bpx_obj, tmp) - tmp.flush() + sols = [] + for obj in bpx_objs: + with tempfile.NamedTemporaryFile( + suffix=filename, delete=False, mode="w" + ) as tmp: + # write to a temporary file so we can + # get the source later on using inspect.getsource + # (as long as the file still exists) + json.dump(obj, tmp) + tmp.flush() - pv = pybamm.ParameterValues.create_from_bpx(tmp.name) + pv = pybamm.ParameterValues.create_from_bpx(tmp.name) + sim = pybamm.Simulation( + model, parameter_values=pv, experiment=experiment + ) + sols.append(sim.solve()) - model = pybamm.lithium_ion.DFN() - experiment = pybamm.Experiment( - [ - "Discharge at C/5 for 1 hour", - ] + with pytest.raises(AssertionError): + np.testing.assert_allclose( + sols[0]["Voltage [V]"].data, sols[1]["Voltage [V]"].data, atol=1e-7 ) - sim = pybamm.Simulation(model, parameter_values=pv, experiment=experiment) - sim.solve() def test_constant_functions(self): bpx_obj = copy.copy(self.base) @@ -171,7 +196,7 @@ def check_constant_output(func): self.assertEqual(p_vals[0], p_vals[1]) for electrode in ["Negative", "Positive"]: - D = param[f"{electrode} electrode diffusivity [m2.s-1]"] + D = param[f"{electrode} particle diffusivity [m2.s-1]"] dUdT = param[f"{electrode} electrode OCP entropic change [V.K-1]"] check_constant_output(D) check_constant_output(dUdT) @@ -227,7 +252,7 @@ def test_table_data(self): D = param["Electrolyte diffusivity [m2.s-1]"](c, 298.15) self.assertIsInstance(D, pybamm.Interpolant) for electrode in ["Negative", "Positive"]: - D = param[f"{electrode} electrode diffusivity [m2.s-1]"](c, 298.15) + D = param[f"{electrode} particle diffusivity [m2.s-1]"](c, 298.15) self.assertIsInstance(D, pybamm.Interpolant) OCP = param[f"{electrode} electrode OCP [V]"](c) self.assertIsInstance(OCP, pybamm.Interpolant) @@ -281,8 +306,8 @@ def arrhenius_assertion(pv, param_key, Ea_key): param_keys = [ "Electrolyte conductivity [S.m-1]", "Electrolyte diffusivity [m2.s-1]", - "Negative electrode diffusivity [m2.s-1]", - "Positive electrode diffusivity [m2.s-1]", + "Negative particle diffusivity [m2.s-1]", + "Positive particle diffusivity [m2.s-1]", "Positive electrode exchange-current density [A.m-2]", "Negative electrode exchange-current density [A.m-2]", ] @@ -290,8 +315,8 @@ def arrhenius_assertion(pv, param_key, Ea_key): Ea_keys = [ "Electrolyte conductivity activation energy [J.mol-1]", "Electrolyte diffusivity activation energy [J.mol-1]", - "Negative electrode diffusivity activation energy [J.mol-1]", - "Positive electrode diffusivity activation energy [J.mol-1]", + "Negative particle diffusivity activation energy [J.mol-1]", + "Positive particle diffusivity activation energy [J.mol-1]", "Positive electrode reaction rate constant activation energy [J.mol-1]", "Negative electrode reaction rate constant activation energy [J.mol-1]", ] @@ -299,6 +324,165 @@ def arrhenius_assertion(pv, param_key, Ea_key): for param_key, Ea_key in zip(param_keys, Ea_keys): arrhenius_assertion(pv, param_key, Ea_key) + def test_bpx_blended(self): + bpx_obj = copy.copy(self.base) + bpx_obj["Parameterisation"]["Positive electrode"] = { + "Thickness [m]": 5.23e-05, + "Conductivity [S.m-1]": 0.789, + "Porosity": 0.277493, + "Transport efficiency": 0.1462, + "Particle": { + "Large Particles": { + "Diffusivity [m2.s-1]": 3.2e-14, + "Particle radius [m]": 8e-06, + "OCP [V]": "-3.04420906 * x + 10.04892207 - 0.65637536 * tanh(-4.02134095 * (x - 0.80063948)) + 4.24678547 * tanh(12.17805062 * (x - 7.57659337)) - 0.3757068 * tanh(59.33067782 * (x - 0.99784492))", + "Entropic change coefficient [V.K-1]": -1e-4, + "Surface area per unit volume [m-1]": 186331, + "Reaction rate constant [mol.m-2.s-1]": 2.305e-05, + "Minimum stoichiometry": 0.42424, + "Maximum stoichiometry": 0.96210, + "Maximum concentration [mol.m-3]": 46200, + "Diffusivity activation energy [J.mol-1]": 15000, + "Reaction rate constant activation energy [J.mol-1]": 3500, + }, + "Small Particles": { + "Diffusivity [m2.s-1]": 3.2e-14, + "Particle radius [m]": 1e-06, + "OCP [V]": "-3.04420906 * x + 10.04892207 - 0.65637536 * tanh(-4.02134095 * (x - 0.80063948)) + 4.24678547 * tanh(12.17805062 * (x - 7.57659337)) - 0.3757068 * tanh(59.33067782 * (x - 0.99784492))", + "Entropic change coefficient [V.K-1]": -1e-4, + "Surface area per unit volume [m-1]": 496883, + "Reaction rate constant [mol.m-2.s-1]": 2.305e-05, + "Minimum stoichiometry": 0.42424, + "Maximum stoichiometry": 0.96210, + "Maximum concentration [mol.m-3]": 46200, + "Diffusivity activation energy [J.mol-1]": 15000, + "Reaction rate constant activation energy [J.mol-1]": 3500, + }, + }, + } + + filename = "tmp.json" + with tempfile.NamedTemporaryFile( + suffix=filename, delete=False, mode="w" + ) as tmp: + # write to a tempory file so we can + # get the source later on using inspect.getsource + # (as long as the file still exists) + json.dump(bpx_obj, tmp) + tmp.flush() + + pv = pybamm.ParameterValues.create_from_bpx(tmp.name) + # initial concentration must be set manually for blended models (for now) + pv.update( + { + "Initial concentration in negative electrode [mol.m-3]": 22000, + "Primary: Initial concentration in positive electrode [mol.m-3]": 19404, + "Secondary: Initial concentration in positive electrode [mol.m-3]": 19404, + }, + check_already_exists=False, + ) + model = pybamm.lithium_ion.SPM({"particle phases": ("1", "2")}) + experiment = pybamm.Experiment( + [ + "Discharge at C/5 for 1 hour", + ] + ) + sim = pybamm.Simulation(model, parameter_values=pv, experiment=experiment) + sim.solve(calc_esoh=False) + + def test_bpx_blended_error(self): + bpx_obj = copy.copy(self.base) + bpx_obj["Parameterisation"]["Positive electrode"] = { + "Thickness [m]": 5.23e-05, + "Conductivity [S.m-1]": 0.789, + "Porosity": 0.277493, + "Transport efficiency": 0.1462, + "Particle": { + "Large Particles": { + "Diffusivity [m2.s-1]": 3.2e-14, + "Particle radius [m]": 8e-06, + "OCP [V]": "-3.04420906 * x + 10.04892207 - 0.65637536 * tanh(-4.02134095 * (x - 0.80063948)) + 4.24678547 * tanh(12.17805062 * (x - 7.57659337)) - 0.3757068 * tanh(59.33067782 * (x - 0.99784492))", + "Entropic change coefficient [V.K-1]": -1e-4, + "Surface area per unit volume [m-1]": 186331, + "Reaction rate constant [mol.m-2.s-1]": 2.305e-05, + "Minimum stoichiometry": 0.42424, + "Maximum stoichiometry": 0.96210, + "Maximum concentration [mol.m-3]": 46200, + "Diffusivity activation energy [J.mol-1]": 15000, + "Reaction rate constant activation energy [J.mol-1]": 3500, + }, + "Medium Particles": { + "Diffusivity [m2.s-1]": 3.2e-14, + "Particle radius [m]": 4e-06, + "OCP [V]": "-3.04420906 * x + 10.04892207 - 0.65637536 * tanh(-4.02134095 * (x - 0.80063948)) + 4.24678547 * tanh(12.17805062 * (x - 7.57659337)) - 0.3757068 * tanh(59.33067782 * (x - 0.99784492))", + "Entropic change coefficient [V.K-1]": -1e-4, + "Surface area per unit volume [m-1]": 186331, + "Reaction rate constant [mol.m-2.s-1]": 2.305e-05, + "Minimum stoichiometry": 0.42424, + "Maximum stoichiometry": 0.96210, + "Maximum concentration [mol.m-3]": 46200, + "Diffusivity activation energy [J.mol-1]": 15000, + "Reaction rate constant activation energy [J.mol-1]": 3500, + }, + "Small Particles": { + "Diffusivity [m2.s-1]": 3.2e-14, + "Particle radius [m]": 1e-06, + "OCP [V]": "-3.04420906 * x + 10.04892207 - 0.65637536 * tanh(-4.02134095 * (x - 0.80063948)) + 4.24678547 * tanh(12.17805062 * (x - 7.57659337)) - 0.3757068 * tanh(59.33067782 * (x - 0.99784492))", + "Entropic change coefficient [V.K-1]": -1e-4, + "Surface area per unit volume [m-1]": 186331, + "Reaction rate constant [mol.m-2.s-1]": 2.305e-05, + "Minimum stoichiometry": 0.42424, + "Maximum stoichiometry": 0.96210, + "Maximum concentration [mol.m-3]": 46200, + "Diffusivity activation energy [J.mol-1]": 15000, + "Reaction rate constant activation energy [J.mol-1]": 3500, + }, + }, + } + + filename = "tmp.json" + with tempfile.NamedTemporaryFile( + suffix=filename, delete=False, mode="w" + ) as tmp: + # write to a tempory file so we can + # get the source later on using inspect.getsource + # (as long as the file still exists) + json.dump(bpx_obj, tmp) + tmp.flush() + + with self.assertRaisesRegex(NotImplementedError, "PyBaMM does not support"): + pybamm.ParameterValues.create_from_bpx(tmp.name) + + def test_bpx_user_defined(self): + bpx_obj = copy.copy(self.base) + data = {"x": [0, 1], "y": [0, 1]} + bpx_obj["Parameterisation"]["User-defined"] = { + "User-defined scalar parameter": 1.0, + "User-defined parameter data": data, + "User-defined parameter data function": "x**2", + } + + filename = "tmp.json" + with tempfile.NamedTemporaryFile( + suffix=filename, delete=False, mode="w" + ) as tmp: + # write to a tempory file so we can + # get the source later on using inspect.getsource + # (as long as the file still exists) + json.dump(bpx_obj, tmp) + tmp.flush() + + param = pybamm.ParameterValues.create_from_bpx(tmp.name) + + self.assertEqual(param["User-defined scalar parameter"], 1.0) + var = pybamm.Variable("var") + self.assertIsInstance( + param["User-defined parameter data"](var), pybamm.Interpolant + ) + self.assertIsInstance( + param["User-defined parameter data function"](var), pybamm.Power + ) + if __name__ == "__main__": print("Add -v for more debug output") diff --git a/tests/unit/test_parameters/test_current_functions.py b/tests/unit/test_parameters/test_current_functions.py index 10a311fc2c..8a8cc266ce 100644 --- a/tests/unit/test_parameters/test_current_functions.py +++ b/tests/unit/test_parameters/test_current_functions.py @@ -6,8 +6,9 @@ import numbers import unittest import numpy as np -import os import pandas as pd +import pytest +from tests import no_internet_connection class TestCurrentFunctions(TestCase): @@ -19,10 +20,15 @@ def test_constant_current(self): processed_current = parameter_values.process_symbol(current) self.assertIsInstance(processed_current, pybamm.Scalar) + @pytest.mark.skipif( + no_internet_connection(), + reason="Network not available to download files from registry", + ) def test_get_current_data(self): # test process parameters + data_loader = pybamm.DataLoader() current_data = pd.read_csv( - os.path.join(pybamm.__path__[0], "input", "drive_cycles", "US06.csv"), + data_loader.get_data("US06.csv"), comment="#", names=["Time [s]", "Current [A]"], ) diff --git a/tests/unit/test_parameters/test_parameter_sets/test_Ai2020.py b/tests/unit/test_parameters/test_parameter_sets/test_Ai2020.py index 43eee82175..8816551ab6 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_Ai2020.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_Ai2020.py @@ -17,7 +17,7 @@ def test_functions(self): fun_test = { # Positive electrode "Positive electrode cracking rate": ([T], 3.9e-20), - "Positive electrode diffusivity [m2.s-1]": ([sto, T], 5.387e-15), + "Positive particle diffusivity [m2.s-1]": ([sto, T], 5.387e-15), "Positive electrode exchange-current density [A.m-2]": ( [1e3, 1e4, c_p_max, T], 0.6098, @@ -29,7 +29,7 @@ def test_functions(self): "Positive electrode volume change": ([sto, c_p_max], -1.8179e-2), # Negative electrode "Negative electrode cracking rate": ([T], 3.9e-20), - "Negative electrode diffusivity [m2.s-1]": ([sto, T], 3.9e-14), + "Negative particle diffusivity [m2.s-1]": ([sto, T], 3.9e-14), "Negative electrode exchange-current density [A.m-2]": ( [1e3, 1e4, c_n_max, T], 0.4172, diff --git a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py index 894213f92d..2dc73d4484 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py @@ -13,15 +13,22 @@ def test_functions(self): T = pybamm.Scalar(298.15) fun_test = { + # Lithium plating + "Exchange-current density for plating [A.m-2]": ([1e3, 1e4, T], 9.6485e-3), + "Exchange-current density for stripping [A.m-2]": ( + [1e3, 1e4, T], + 9.6485e-2, + ), + "Dead lithium decay rate [s-1]": ([1e-8], 5e-7), # Negative electrode - "Negative electrode diffusivity [m2.s-1]": ([sto, T], 1.219e-14), + "Negative particle diffusivity [m2.s-1]": ([sto, T], 1.219e-14), "Negative electrode exchange-current density [A.m-2]": ( [1000, 15960, 31920, T], 6.2517, ), "Negative electrode OCP [V]": ([sto], 0.124), # Positive electrode - "Positive electrode diffusivity [m2.s-1]": ([sto, T], 1.0457e-13), + "Positive particle diffusivity [m2.s-1]": ([sto, T], 1.0457e-13), "Positive electrode exchange-current density [A.m-2]": ( [1000, 24290, 48580, T], 2.5121, diff --git a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py index f548030f26..f435ef6d36 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py @@ -13,8 +13,15 @@ def test_functions(self): T = pybamm.Scalar(298.15) fun_test = { + # Lithium plating + "Exchange-current density for plating [A.m-2]": ([1e3, 1e4, T], 9.6485e-3), + "Exchange-current density for stripping [A.m-2]": ( + [1e3, 1e4, T], + 9.6485e-2, + ), + "Dead lithium decay rate [s-1]": ([1e-8], 5e-7), # Positive electrode - "Positive electrode diffusivity [m2.s-1]": ([sto, T], 1.219e-14), + "Positive particle diffusivity [m2.s-1]": ([sto, T], 1.219e-14), "Positive electrode exchange-current density [A.m-2]": ( [1000, 15960, 31920, T], 6.2517, diff --git a/tests/unit/test_parameters/test_parameter_sets/test_LCO_Ramadass2004.py b/tests/unit/test_parameters/test_parameter_sets/test_LCO_Ramadass2004.py index 1cb1477822..2de67b9e62 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_LCO_Ramadass2004.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_LCO_Ramadass2004.py @@ -16,7 +16,7 @@ def test_functions(self): c_n_max = param["Maximum concentration in negative electrode [mol.m-3]"] fun_test = { # Positive electrode - "Positive electrode diffusivity [m2.s-1]": ([sto, T], 1e-14), + "Positive particle diffusivity [m2.s-1]": ([sto, T], 1e-14), "Positive electrode exchange-current density [A.m-2]": ( [1e3, 1e4, c_p_max, T], 1.4517, @@ -27,7 +27,7 @@ def test_functions(self): ), "Positive electrode OCP [V]": ([sto], 4.1249), # Negative electrode - "Negative electrode diffusivity [m2.s-1]": ([sto, T], 3.9e-14), + "Negative particle diffusivity [m2.s-1]": ([sto, T], 3.9e-14), "Negative electrode exchange-current density [A.m-2]": ( [1e3, 1e4, c_n_max, T], 2.2007, diff --git a/tests/unit/test_parameters/test_parameter_sets/test_LGM50_ORegan2022.py b/tests/unit/test_parameters/test_parameter_sets/test_LGM50_ORegan2022.py index 037c2c87dd..f878b7d790 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_LGM50_ORegan2022.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_LGM50_ORegan2022.py @@ -23,7 +23,7 @@ def test_functions(self): [298.15], 902.6502, ), - "Positive electrode diffusivity [m2.s-1]": ([0.5, 298.15], 7.2627e-15), + "Positive particle diffusivity [m2.s-1]": ([0.5, 298.15], 7.2627e-15), "Positive electrode exchange-current density [A.m-2]": ( [1e3, 1e4, c_p_max, 298.15], 2.1939, @@ -40,7 +40,7 @@ def test_functions(self): [298.15], 847.7155, ), - "Negative electrode diffusivity [m2.s-1]": ([0.5, 298.15], 2.8655e-16), + "Negative particle diffusivity [m2.s-1]": ([0.5, 298.15], 2.8655e-16), "Negative electrode exchange-current density [A.m-2]": ( [1e3, 1e4, c_n_max, 298.15], 1.0372, diff --git a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py index 1abc5c3baf..1ab7e7930e 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py @@ -21,7 +21,7 @@ def test_functions(self): ), "Dead lithium decay rate [s-1]": ([1e-8], 5e-7), # Negative electrode - "Negative electrode diffusivity [m2.s-1]": ([sto, T], 3.3e-14), + "Negative particle diffusivity [m2.s-1]": ([sto, T], 3.3e-14), "Negative electrode exchange-current density [A.m-2]": ( [1000, 16566.5, 33133, T], 0.33947, @@ -29,7 +29,7 @@ def test_functions(self): "Negative electrode cracking rate": ([T], 3.9e-20), "Negative electrode volume change": ([sto, 33133], 0.0897), # Positive electrode - "Positive electrode diffusivity [m2.s-1]": ([sto, T], 4e-15), + "Positive particle diffusivity [m2.s-1]": ([sto, T], 4e-15), "Positive electrode exchange-current density [A.m-2]": ( [1000, 31552, 63104, T], 3.4123, diff --git a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022_negative_halfcell.py b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022_negative_halfcell.py index 5c6971a7d5..04a19e1002 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022_negative_halfcell.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022_negative_halfcell.py @@ -21,7 +21,7 @@ def test_functions(self): ), "Dead lithium decay rate [s-1]": ([1e-8], 5e-7), # Positive electrode - "Positive electrode diffusivity [m2.s-1]": ([sto, T], 3.3e-14), + "Positive particle diffusivity [m2.s-1]": ([sto, T], 3.3e-14), "Positive electrode exchange-current density [A.m-2]": ( [1000, 16566.5, 33133, T], 0.33947, diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py index 7b5fd38bf0..28b8aa2ef9 100644 --- a/tests/unit/test_parameters/test_parameter_values.py +++ b/tests/unit/test_parameters/test_parameter_values.py @@ -43,7 +43,7 @@ def test_init(self): pybamm.ParameterValues(None, chemistry="lithium-ion") # junk param values rejected - with self.assertRaisesRegex(ValueError, "Invalid Parameter Value"): + with self.assertRaisesRegex(ValueError, "'Junk' is not a valid parameter set."): pybamm.ParameterValues("Junk") def test_repr(self): diff --git a/tests/unit/test_parameters/test_parameters_cli.py b/tests/unit/test_parameters/test_parameters_cli.py deleted file mode 100644 index dbbdf18022..0000000000 --- a/tests/unit/test_parameters/test_parameters_cli.py +++ /dev/null @@ -1,28 +0,0 @@ -# -# Tests for the PyBaMM parameters management -# command line interface -# - -import pybamm -import unittest -from tests import TestCase - - -class TestParametersCLI(TestCase): - def test_error(self): - with self.assertRaisesRegex(NotImplementedError, "deprecated"): - pybamm.add_parameter() - with self.assertRaisesRegex(NotImplementedError, "deprecated"): - pybamm.edit_parameter() - with self.assertRaisesRegex(NotImplementedError, "deprecated"): - pybamm.remove_parameter() - - -if __name__ == "__main__": - print("Add -v for more debug output") - import sys - - if "-v" in sys.argv: - debug = True - pybamm.settings.debug_mode = True - unittest.main() diff --git a/tests/unit/test_plotting/test_plot.py b/tests/unit/test_plotting/test_plot.py index aaa75f957c..f36e20cd6f 100644 --- a/tests/unit/test_plotting/test_plot.py +++ b/tests/unit/test_plotting/test_plot.py @@ -3,16 +3,19 @@ import numpy as np from tests import TestCase import matplotlib.pyplot as plt +from matplotlib import use + +use("Agg") class TestPlot(TestCase): def test_plot(self): x = pybamm.Array(np.array([0, 3, 10])) y = pybamm.Array(np.array([6, 16, 78])) - pybamm.plot(x, y, testing=True) + pybamm.plot(x, y, show_plot=False) _, ax = plt.subplots() - ax_out = pybamm.plot(x, y, ax=ax, testing=True) + ax_out = pybamm.plot(x, y, ax=ax, show_plot=False) self.assertEqual(ax_out, ax) def test_plot_fail(self): @@ -28,13 +31,13 @@ def test_plot2D(self): X, Y = pybamm.meshgrid(x, y) # plot with array directly - pybamm.plot2D(x, y, Y, testing=True) + pybamm.plot2D(x, y, Y, show_plot=False) # plot with meshgrid - pybamm.plot2D(X, Y, Y, testing=True) + pybamm.plot2D(X, Y, Y, show_plot=False) _, ax = plt.subplots() - ax_out = pybamm.plot2D(X, Y, Y, ax=ax, testing=True) + ax_out = pybamm.plot2D(X, Y, Y, ax=ax, show_plot=False) self.assertEqual(ax_out, ax) def test_plot2D_fail(self): diff --git a/tests/unit/test_plotting/test_plot_summary_variables.py b/tests/unit/test_plotting/test_plot_summary_variables.py index 69e32eb023..e896b1f468 100644 --- a/tests/unit/test_plotting/test_plot_summary_variables.py +++ b/tests/unit/test_plotting/test_plot_summary_variables.py @@ -36,7 +36,7 @@ def test_plot(self): ) sol = sim.solve(initial_soc=1) - axes = pybamm.plot_summary_variables(sol, testing=True) + axes = pybamm.plot_summary_variables(sol, show_plot=False) axes = axes.flatten() self.assertEqual(len(axes), 9) @@ -52,7 +52,7 @@ def test_plot(self): np.testing.assert_array_equal(var, sol.summary_variables[output_var]) axes = pybamm.plot_summary_variables( - [sol, sol], labels=["SPM", "SPM"], testing=True + [sol, sol], labels=["SPM", "SPM"], show_plot=False ) axes = axes.flatten() diff --git a/tests/unit/test_plotting/test_plot_thermal_components.py b/tests/unit/test_plotting/test_plot_thermal_components.py new file mode 100644 index 0000000000..99b3d40cac --- /dev/null +++ b/tests/unit/test_plotting/test_plot_thermal_components.py @@ -0,0 +1,51 @@ +import pybamm +import unittest +import numpy as np +from tests import TestCase +import matplotlib.pyplot as plt +from matplotlib import use + +use("Agg") + + +class TestPlotThermalComponents(TestCase): + def test_plot_with_solution(self): + model = pybamm.lithium_ion.SPM({"thermal": "lumped"}) + sim = pybamm.Simulation( + model, parameter_values=pybamm.ParameterValues("ORegan2022") + ) + sol = sim.solve([0, 3600]) + for input_data in [sim, sol]: + _, ax = pybamm.plot_thermal_components(input_data, show_plot=False) + t, cumul_heat = ax[1].get_lines()[-1].get_data() + np.testing.assert_array_almost_equal(t, sol["Time [h]"].data) + t, cumul_heat = ax[1].get_lines()[-1].get_data() + T_sol = sol["X-averaged cell temperature [K]"].data + np.testing.assert_array_almost_equal(t, sol["Time [h]"].data) + rho_c_p_eff = sol[ + "Volume-averaged effective heat capacity [J.K-1.m-3]" + ].data + T_plot = T_sol[0] + cumul_heat / rho_c_p_eff + np.testing.assert_allclose(T_sol, T_plot, rtol=1e-2) + + _, ax = plt.subplots(1, 2) + _, ax_out = pybamm.plot_thermal_components(sol, ax=ax, show_legend=True) + self.assertEqual(ax_out[0], ax[0]) + self.assertEqual(ax_out[1], ax[1]) + + def test_not_implemented(self): + model = pybamm.lithium_ion.SPM({"thermal": "x-full"}) + sim = pybamm.Simulation(model) + sol = sim.solve([0, 3600]) + with self.assertRaises(NotImplementedError): + pybamm.plot_thermal_components(sol) + + +if __name__ == "__main__": + print("Add -v for more debug output") + import sys + + if "-v" in sys.argv: + debug = True + pybamm.settings.debug_mode = True + unittest.main() diff --git a/tests/unit/test_plotting/test_plot_voltage_components.py b/tests/unit/test_plotting/test_plot_voltage_components.py index b2fc201cec..1773d576d9 100644 --- a/tests/unit/test_plotting/test_plot_voltage_components.py +++ b/tests/unit/test_plotting/test_plot_voltage_components.py @@ -3,16 +3,19 @@ import numpy as np from tests import TestCase import matplotlib.pyplot as plt +from matplotlib import use + +use("Agg") class TestPlotVoltageComponents(TestCase): - def test_plot(self): + def test_plot_with_solution(self): model = pybamm.lithium_ion.SPM() sim = pybamm.Simulation(model) sol = sim.solve([0, 3600]) for split in [True, False]: _, ax = pybamm.plot_voltage_components( - sol, testing=True, split_by_electrode=split + sol, show_plot=False, split_by_electrode=split ) t, V = ax.get_lines()[0].get_data() np.testing.assert_array_equal(t, sol["Time [h]"].data) @@ -22,6 +25,67 @@ def test_plot(self): _, ax_out = pybamm.plot_voltage_components(sol, ax=ax, show_legend=True) self.assertEqual(ax_out, ax) + def test_plot_with_simulation(self): + model = pybamm.lithium_ion.SPM() + sim = pybamm.Simulation(model) + sim.solve([0, 3600]) + + for split in [True, False]: + _, ax = pybamm.plot_voltage_components( + sim, show_plot=False, split_by_electrode=split + ) + t, V = ax.get_lines()[0].get_data() + np.testing.assert_array_equal(t, sim.solution["Time [h]"].data) + np.testing.assert_array_equal(V, sim.solution["Battery voltage [V]"].data) + + _, ax = plt.subplots() + _, ax_out = pybamm.plot_voltage_components(sim, ax=ax, show_legend=True) + self.assertEqual(ax_out, ax) + + def test_plot_from_solution(self): + model = pybamm.lithium_ion.SPM() + sim = pybamm.Simulation(model) + sol = sim.solve([0, 3600]) + for split in [True, False]: + _, ax = sol.plot_voltage_components( + show_plot=False, split_by_electrode=split + ) + t, V = ax.get_lines()[0].get_data() + np.testing.assert_array_equal(t, sol["Time [h]"].data) + np.testing.assert_array_equal(V, sol["Battery voltage [V]"].data) + + _, ax = plt.subplots() + _, ax_out = sol.plot_voltage_components(ax=ax, show_legend=True) + self.assertEqual(ax_out, ax) + + def test_plot_from_simulation(self): + model = pybamm.lithium_ion.SPM() + sim = pybamm.Simulation(model) + sim.solve([0, 3600]) + + for split in [True, False]: + _, ax = sim.plot_voltage_components( + show_plot=False, split_by_electrode=split + ) + t, V = ax.get_lines()[0].get_data() + np.testing.assert_array_equal(t, sim.solution["Time [h]"].data) + np.testing.assert_array_equal(V, sim.solution["Battery voltage [V]"].data) + + _, ax = plt.subplots() + _, ax_out = sim.plot_voltage_components(ax=ax, show_legend=True) + self.assertEqual(ax_out, ax) + + def test_plot_without_solution(self): + model = pybamm.lithium_ion.SPM() + sim = pybamm.Simulation(model) + + with self.assertRaises(ValueError) as error: + sim.plot_voltage_components() + + self.assertEqual( + str(error.exception), "The simulation has not been solved yet." + ) + if __name__ == "__main__": print("Add -v for more debug output") diff --git a/tests/unit/test_plotting/test_quick_plot.py b/tests/unit/test_plotting/test_quick_plot.py index 7e2a088de6..e9e5dd810b 100644 --- a/tests/unit/test_plotting/test_quick_plot.py +++ b/tests/unit/test_plotting/test_quick_plot.py @@ -84,7 +84,7 @@ def test_simple_ode_model(self): self.assertNotEqual(quick_plot.axis_limits[("a",)], new_axis) # check dynamic plot loads - quick_plot.dynamic_plot(testing=True) + quick_plot.dynamic_plot(show_plot=False) quick_plot.slider_update(0.01) @@ -117,7 +117,7 @@ def test_simple_ode_model(self): self.assertNotEqual(quick_plot.axis_limits[var_key], new_axis) # check dynamic plot loads - quick_plot.dynamic_plot(testing=True) + quick_plot.dynamic_plot(show_plot=False) quick_plot.slider_update(0.01) @@ -181,20 +181,20 @@ def test_simple_ode_model(self): # Test different spatial units quick_plot = pybamm.QuickPlot(solution, ["a"]) - self.assertEqual(quick_plot.spatial_unit, "$\mu$m") + self.assertEqual(quick_plot.spatial_unit, r"$\mu$m") quick_plot = pybamm.QuickPlot(solution, ["a"], spatial_unit="m") self.assertEqual(quick_plot.spatial_unit, "m") quick_plot = pybamm.QuickPlot(solution, ["a"], spatial_unit="mm") self.assertEqual(quick_plot.spatial_unit, "mm") quick_plot = pybamm.QuickPlot(solution, ["a"], spatial_unit="um") - self.assertEqual(quick_plot.spatial_unit, "$\mu$m") + self.assertEqual(quick_plot.spatial_unit, r"$\mu$m") with self.assertRaisesRegex(ValueError, "spatial unit"): pybamm.QuickPlot(solution, ["a"], spatial_unit="bad unit") # Test 2D variables quick_plot = pybamm.QuickPlot(solution, ["2D variable"]) quick_plot.plot(0) - quick_plot.dynamic_plot(testing=True) + quick_plot.dynamic_plot(show_plot=False) quick_plot.slider_update(0.01) with self.assertRaisesRegex(NotImplementedError, "Cannot plot 2D variables"): @@ -440,7 +440,7 @@ def test_plot_2plus1D_spm(self): "Voltage [V]", ], ) - quick_plot.dynamic_plot(testing=True) + quick_plot.dynamic_plot(show_plot=False) quick_plot.slider_update(1) # check 2D (y,z space) variables update properly for different time units @@ -504,7 +504,7 @@ def test_model_with_inputs(self): quick_plot = pybamm.QuickPlot( solutions=[sol1, sol2], output_variables=output_variables ) - quick_plot.dynamic_plot(testing=True) + quick_plot.dynamic_plot(show_plot=False) quick_plot.slider_update(1) pybamm.close_plots() diff --git a/tests/unit/test_pybamm_data.py b/tests/unit/test_pybamm_data.py new file mode 100644 index 0000000000..6d73c633f8 --- /dev/null +++ b/tests/unit/test_pybamm_data.py @@ -0,0 +1,34 @@ +import pybamm +import pytest +from tests import no_internet_connection + +data_loader = pybamm.DataLoader() + + +@pytest.mark.skipif( + no_internet_connection(), + reason="Network not available to download files from registry", +) # Skip if no internet +def test_fetch(): + data_loader = pybamm.DataLoader() + test_file = next(iter(data_loader.files.keys())) + return data_loader.get_data(test_file).is_file() + + +@pytest.mark.skipif( + no_internet_connection(), + reason="Network not available to download files from registry", +) +def test_fetch_fake(): + # Try to fetch a fake file not present in the registry + with pytest.raises(ValueError): + data_loader.get_data("NotAfile.json") + + +@pytest.mark.skipif( + no_internet_connection(), + reason="Network not available to download files from registry", +) +def test_registry(): + # Checking if the file names returned are equal to the ones in the registry + return data_loader.show_registry() == list(data_loader.files) diff --git a/tests/unit/test_serialisation/test_serialisation.py b/tests/unit/test_serialisation/test_serialisation.py index 75ea33fe66..e7dcba6702 100644 --- a/tests/unit/test_serialisation/test_serialisation.py +++ b/tests/unit/test_serialisation/test_serialisation.py @@ -139,7 +139,7 @@ def test_user_defined_model_recreaction(self): new_solver = pybamm.ScipySolver() new_solution = new_solver.solve(new_model, t) - for x, val in enumerate(solution.all_ys): + for x, _ in enumerate(solution.all_ys): np.testing.assert_array_almost_equal( solution.all_ys[x], new_solution.all_ys[x] ) @@ -564,7 +564,7 @@ def test_serialised_model_plotting(self): new_solution = pybamm.ScipySolver().solve(new_model, np.linspace(0, 1)) # check dynamic plot loads - new_solution.plot(["c", "2c"], testing=True) + new_solution.plot(["c", "2c"], show_plot=False) # models with a mesh ---------------- model = pybamm.lithium_ion.SPM(name="test_spm_plotting") @@ -590,7 +590,7 @@ def test_serialised_model_plotting(self): new_solution = new_solver.solve(new_model, [0, 3600]) # check dynamic plot loads - new_solution.plot(testing=True) + new_solution.plot(show_plot=False) if __name__ == "__main__": diff --git a/tests/unit/test_simulation.py b/tests/unit/test_simulation.py index ef055fdc97..63c15a91fc 100644 --- a/tests/unit/test_simulation.py +++ b/tests/unit/test_simulation.py @@ -6,7 +6,10 @@ import sys import unittest import uuid +import pytest from tempfile import TemporaryDirectory +from scipy.integrate import cumulative_trapezoid +from tests import no_internet_connection class TestSimulation(TestCase): @@ -69,8 +72,10 @@ def test_solve(self): self.assertTrue(val.has_symbol_of_classes(pybamm.Matrix)) # test solve without check - sim = pybamm.Simulation(pybamm.lithium_ion.SPM()) - sol = sim.solve(t_eval=[0, 600], check_model=False) + sim = pybamm.Simulation( + pybamm.lithium_ion.SPM(), discretisation_kwargs={"check_model": False} + ) + sol = sim.solve(t_eval=[0, 600]) for val in list(sim.built_model.rhs.values()): self.assertFalse(val.has_symbol_of_classes(pybamm.Parameter)) # skip test for scalar variables (e.g. discharge capacity) @@ -83,6 +88,19 @@ def test_solve(self): with self.assertRaisesRegex(ValueError, "starting_solution"): sim.solve(starting_solution=sol) + def test_solve_remove_independent_variables_from_rhs(self): + sim = pybamm.Simulation( + pybamm.lithium_ion.SPM(), + discretisation_kwargs={"remove_independent_variables_from_rhs": True}, + ) + sol = sim.solve([0, 600]) + t = sol["Time [s]"].data + I = sol["Current [A]"].data + q = sol["Discharge capacity [A.h]"].data + np.testing.assert_array_almost_equal( + q, cumulative_trapezoid(I, t, initial=0) / 3600 + ) + def test_solve_non_battery_model(self): model = pybamm.BaseModel() v = pybamm.Variable("v") @@ -170,6 +188,10 @@ def test_step(self): sim.solution.t, np.array([0, dt, dt + 1e-9, 2 * dt]) ) + @pytest.mark.skipif( + no_internet_connection(), + reason="Network not available to download files from registry", + ) def test_solve_with_initial_soc(self): model = pybamm.lithium_ion.SPM() param = model.default_parameter_values @@ -186,8 +208,9 @@ def test_solve_with_initial_soc(self): self.assertEqual(sim._built_initial_soc, 0.8) # test with drive cycle + data_loader = pybamm.DataLoader() drive_cycle = pd.read_csv( - os.path.join("pybamm", "input", "drive_cycles", "US06.csv"), + data_loader.get_data("US06.csv"), comment="#", header=None, ).to_numpy() @@ -204,6 +227,38 @@ def test_solve_with_initial_soc(self): sim.build(initial_soc=0.5) self.assertEqual(sim._built_initial_soc, 0.5) + # Test that initial soc works with a relevant input parameter + model = pybamm.lithium_ion.DFN() + param = model.default_parameter_values + og_eps_p = param["Positive electrode active material volume fraction"] + param["Positive electrode active material volume fraction"] = ( + pybamm.InputParameter("eps_p") + ) + sim = pybamm.Simulation(model, parameter_values=param) + sim.solve(t_eval=[0, 1], initial_soc=0.8, inputs={"eps_p": og_eps_p}) + self.assertEqual(sim._built_initial_soc, 0.8) + + # test having an input parameter in the ocv function + model = pybamm.lithium_ion.SPM() + parameter_values = model.default_parameter_values + a = pybamm.Parameter("a") + + def ocv_with_parameter(sto): + u_eq = (4.2 - 2.5) * (1 - sto) + 2.5 + return a * u_eq + + parameter_values.update( + { + "Positive electrode OCP [V]": ocv_with_parameter, + } + ) + parameter_values.update({"a": "[input]"}, check_already_exists=False) + experiment = pybamm.Experiment(["Discharge at 1C until 2.5 V"]) + sim = pybamm.Simulation( + model, parameter_values=parameter_values, experiment=experiment + ) + sim.solve([0, 3600], inputs={"a": 1}) + # Test whether initial_soc works with half cell (solve) options = {"working electrode": "positive"} model = pybamm.lithium_ion.DFN(options) @@ -238,6 +293,41 @@ def test_solve_with_initial_soc(self): sim.build(initial_soc=0.5) self.assertEqual(sim._built_initial_soc, 0.5) + def test_solve_with_initial_soc_with_input_param_in_ocv(self): + # test having an input parameter in the ocv function + model = pybamm.lithium_ion.SPM() + parameter_values = model.default_parameter_values + a = pybamm.Parameter("a") + + def ocv_with_parameter(sto): + u_eq = (4.2 - 2.5) * (1 - sto) + 2.5 + return a * u_eq + + parameter_values.update( + { + "Positive electrode OCP [V]": ocv_with_parameter, + } + ) + parameter_values.update({"a": "[input]"}, check_already_exists=False) + experiment = pybamm.Experiment(["Discharge at 1C until 2.5 V"]) + sim = pybamm.Simulation( + model, parameter_values=parameter_values, experiment=experiment + ) + sim.solve([0, 3600], inputs={"a": 1}, initial_soc=0.8) + self.assertEqual(sim._built_initial_soc, 0.8) + + def test_esoh_with_input_param(self): + # Test that initial soc works with a relevant input parameter + model = pybamm.lithium_ion.DFN({"working electrode": "positive"}) + param = model.default_parameter_values + original_eps_p = param["Positive electrode active material volume fraction"] + param["Positive electrode active material volume fraction"] = ( + pybamm.InputParameter("eps_p") + ) + sim = pybamm.Simulation(model, parameter_values=param) + sim.solve(t_eval=[0, 1], initial_soc=0.8, inputs={"eps_p": original_eps_p}) + self.assertEqual(sim._built_initial_soc, 0.8) + def test_solve_with_inputs(self): model = pybamm.lithium_ion.SPM() param = model.default_parameter_values @@ -391,11 +481,15 @@ def test_plot(self): # now solve and plot t_eval = np.linspace(0, 100, 5) sim.solve(t_eval=t_eval) - sim.plot(testing=True) + sim.plot(show_plot=False) def test_create_gif(self): with TemporaryDirectory() as dir_name: sim = pybamm.Simulation(pybamm.lithium_ion.SPM()) + with self.assertRaisesRegex( + ValueError, "The simulation has not been solved yet." + ): + sim.create_gif() sim.solve(t_eval=[0, 10]) # Create a temporary file name @@ -405,17 +499,20 @@ def test_create_gif(self): sim.create_gif(number_of_images=3, duration=1, output_filename=test_file) # call the plot method before creating the GIF - sim.plot(testing=True) + sim.plot(show_plot=False) sim.create_gif(number_of_images=3, duration=1, output_filename=test_file) + @pytest.mark.skipif( + no_internet_connection(), + reason="Network not available to download files from registry", + ) def test_drive_cycle_interpolant(self): model = pybamm.lithium_ion.SPM() param = model.default_parameter_values # Import drive cycle from file + data_loader = pybamm.DataLoader() drive_cycle = pd.read_csv( - pybamm.get_parameters_filepath( - os.path.join("input", "drive_cycles", "US06.csv") - ), + pybamm.get_parameters_filepath(data_loader.get_data("US06.csv")), comment="#", skip_blank_lines=True, header=None, diff --git a/tests/unit/test_solvers/test_base_solver.py b/tests/unit/test_solvers/test_base_solver.py index 577e50e68b..9a6dec0eaf 100644 --- a/tests/unit/test_solvers/test_base_solver.py +++ b/tests/unit/test_solvers/test_base_solver.py @@ -75,6 +75,22 @@ def test_nonmonotonic_teval(self): ): solver.step(None, model, dt) + # Checking if array t_eval lies within range + dt = 2 + t_eval = np.array([0, 1]) + with self.assertRaisesRegex( + pybamm.SolverError, + "Elements inside array t_eval must lie in the closed interval 0 to dt", + ): + solver.step(None, model, dt, t_eval=t_eval) + + t_eval = np.array([1, dt]) + with self.assertRaisesRegex( + pybamm.SolverError, + "Elements inside array t_eval must lie in the closed interval 0 to dt", + ): + solver.step(None, model, dt, t_eval=t_eval) + def test_solution_time_length_fail(self): model = pybamm.BaseModel() v = pybamm.Scalar(1) @@ -332,6 +348,12 @@ def test_multiple_models_error(self): with self.assertRaisesRegex(RuntimeError, "already been initialised"): solver.solve(model2, t_eval=[0, 1]) + def test_multiprocess_context(self): + solver = pybamm.BaseSolver() + assert solver.get_platform_context("Win") == "spawn" + assert solver.get_platform_context("Linux") == "fork" + assert solver.get_platform_context("Darwin") == "fork" + @unittest.skipIf(not pybamm.have_idaklu(), "idaklu solver is not installed") def test_sensitivities(self): def exact_diff_a(y, a, b): diff --git a/tests/unit/test_solvers/test_casadi_algebraic_solver.py b/tests/unit/test_solvers/test_casadi_algebraic_solver.py index eaf873c5c7..5001b37d82 100644 --- a/tests/unit/test_solvers/test_casadi_algebraic_solver.py +++ b/tests/unit/test_solvers/test_casadi_algebraic_solver.py @@ -1,6 +1,3 @@ -# -# Tests for the Casadi Algebraic Solver class -# from tests import TestCase import casadi import pybamm diff --git a/tests/unit/test_solvers/test_casadi_solver.py b/tests/unit/test_solvers/test_casadi_solver.py index 3030f80af0..c798024579 100644 --- a/tests/unit/test_solvers/test_casadi_solver.py +++ b/tests/unit/test_solvers/test_casadi_solver.py @@ -1,6 +1,3 @@ -# -# Tests for the Casadi Solver class -# from tests import TestCase import pybamm import unittest @@ -539,6 +536,51 @@ def test_casadi_safe_no_termination(self): with self.assertRaisesRegex(pybamm.SolverError, "interpolation bounds"): solver.solve(model, t_eval=[0, 1]) + def test_modulo_non_smooth_events(self): + model = pybamm.BaseModel() + var1 = pybamm.Variable("var1") + var2 = pybamm.Variable("var2") + + a = 0.6 + discontinuities = (np.arange(3) + 1) * a + + model.rhs = {var1: pybamm.Modulo(pybamm.t, a)} + model.algebraic = {var2: 2 * var1 - var2} + model.initial_conditions = {var1: 0, var2: 0} + model.events = [ + pybamm.Event("var1 = 0.55", pybamm.min(0.55 - var1)), + pybamm.Event("var2 = 1.2", pybamm.min(1.2 - var2)), + ] + for discontinuity in discontinuities: + model.events.append( + pybamm.Event("nonsmooth rate", pybamm.Scalar(discontinuity)) + ) + disc = get_discretisation_for_testing() + disc.process_model(model) + + step_solver = pybamm.CasadiSolver(rtol=1e-8, atol=1e-8) + dt = 0.05 + time = 0 + end_time = 3 + step_solution = None + while time < end_time: + step_solution = step_solver.step(step_solution, model, dt=dt, npts=10) + time += dt + np.testing.assert_array_less(step_solution.y[0, :-1], 0.55) + np.testing.assert_array_less(step_solution.y[-1, :-1], 1.2) + np.testing.assert_equal(step_solution.t_event[0], step_solution.t[-1]) + np.testing.assert_array_equal( + step_solution.y_event[:, 0], step_solution.y.full()[:, -1] + ) + var1_soln = (step_solution.t % a) ** 2 / 2 + a**2 / 2 * (step_solution.t // a) + var2_soln = 2 * var1_soln + np.testing.assert_array_almost_equal( + step_solution.y.full()[0], var1_soln, decimal=4 + ) + np.testing.assert_array_almost_equal( + step_solution.y.full()[-1], var2_soln, decimal=4 + ) + class TestCasadiSolverODEsWithForwardSensitivityEquations(TestCase): def test_solve_sensitivity_scalar_var_scalar_input(self): diff --git a/tests/unit/test_solvers/test_idaklu_jax.py b/tests/unit/test_solvers/test_idaklu_jax.py new file mode 100644 index 0000000000..6d891010d6 --- /dev/null +++ b/tests/unit/test_solvers/test_idaklu_jax.py @@ -0,0 +1,891 @@ +# +# Tests for the KLU-Jax interface class +# +from tests import TestCase +from parameterized import parameterized + +import pybamm +import numpy as np +import unittest + +testcase = [] +if pybamm.have_idaklu() and pybamm.have_jax(): + from jax.tree_util import tree_flatten + import jax + import jax.numpy as jnp + + inputs = { + "a": 0.1, + "b": 0.2, + } + + model = pybamm.BaseModel() + v = pybamm.Variable("v") + u1 = pybamm.Variable("u1") + u2 = pybamm.Variable("u2") + a = pybamm.InputParameter("a") + b = pybamm.InputParameter("b") + model.rhs = {u1: a * v, u2: b * v} + model.algebraic = {v: 1 - v} + model.initial_conditions = {u1: 0, u2: 0, v: 1} + model.variables = {"v": v, "u1": u1, "u2": u2} + disc = pybamm.Discretisation() + disc.process_model(model) + t_eval = np.linspace(0, 1, 100) + idaklu_solver = pybamm.IDAKLUSolver(rtol=1e-6, atol=1e-6) + + # Create surrogate data (using base IDAKLU solver) + sim = idaklu_solver.solve( + model, + t_eval, + inputs=inputs, + calculate_sensitivities=True, + ) + + # Get jax expressions for IDAKLU solver + output_variables = [ + "v", + "u1", + "u2", + ] + # Single output variable + idaklu_jax_solver1 = idaklu_solver.jaxify( + model, + t_eval, + output_variables=output_variables[:1], + calculate_sensitivities=True, + ) + f1 = idaklu_jax_solver1.get_jaxpr() + # Multiple output variables + idaklu_jax_solver3 = idaklu_solver.jaxify( + model, + t_eval, + output_variables=output_variables, + calculate_sensitivities=True, + ) + f3 = idaklu_jax_solver3.get_jaxpr() + + # Common test parameters + + in_axes = (0, None) # vmap over time, not inputs + k = 5 # time index for scalar tests + + # Define passthrough wrapper for non-jitted evaluation + def no_jit(f): + return f + + testcase = [ + (output_variables[:1], idaklu_jax_solver1, f1, no_jit), # single output + (output_variables[:1], idaklu_jax_solver1, f1, jax.jit), # jit single output + (output_variables, idaklu_jax_solver3, f3, no_jit), # multiple outputs + (output_variables, idaklu_jax_solver3, f3, jax.jit), # jit multiple outputs + ] + + +# Check the interface throws an appropriate error if either IDAKLU or JAX not available +@unittest.skipIf( + pybamm.have_idaklu() and pybamm.have_jax(), + "Both IDAKLU and JAX are available", +) +class TestIDAKLUJax_NoJax(TestCase): + def test_instantiate_fails(self): + with self.assertRaises(ModuleNotFoundError): + pybamm.IDAKLUJax([], [], []) + + +@unittest.skipIf( + not pybamm.have_idaklu() or not pybamm.have_jax(), + "IDAKLU Solver and/or JAX are not available", +) +class TestIDAKLUJax(TestCase): + # Initialisation tests + + def test_initialise_twice(self): + idaklu_jax_solver = idaklu_solver.jaxify( + model, + t_eval, + output_variables=output_variables, + calculate_sensitivities=True, + ) + with self.assertWarns(UserWarning): + idaklu_jax_solver.jaxify( + model, + t_eval, + output_variables=output_variables, + calculate_sensitivities=True, + ) + + def test_uninitialised(self): + idaklu_jax_solver = idaklu_solver.jaxify( + model, + t_eval, + output_variables=output_variables, + calculate_sensitivities=True, + ) + # simulate failure in initialisation + idaklu_jax_solver.jaxpr = None + with self.assertRaises(pybamm.SolverError): + idaklu_jax_solver.get_jaxpr() + with self.assertRaises(pybamm.SolverError): + idaklu_jax_solver.jax_value() + with self.assertRaises(pybamm.SolverError): + idaklu_jax_solver.jax_grad() + + def test_no_output_variables(self): + with self.assertRaises(pybamm.SolverError): + idaklu_solver.jaxify( + model, + t_eval, + ) + + def test_no_inputs(self): + # Regenerate model with no inputs + model = pybamm.BaseModel() + v = pybamm.Variable("v") + u1 = pybamm.Variable("u1") + u2 = pybamm.Variable("u2") + model.rhs = {u1: 0.1 * v, u2: 0.2 * v} + model.algebraic = {v: 1 - v} + model.initial_conditions = {u1: 0, u2: 0, v: 1} + model.variables = {"v": v, "u1": u1, "u2": u2} + t_eval = np.linspace(0, 1, 100) + idaklu_solver = pybamm.IDAKLUSolver(rtol=1e-6, atol=1e-6) + # Regenerate surrogate data + sim = idaklu_solver.solve(model, t_eval) + idaklu_jax_solver = idaklu_solver.jaxify( + model, + t_eval, + output_variables=output_variables, + ) + f = idaklu_jax_solver.get_jaxpr() + # Check that evaluation can occur (and is correct) with no inputs + out = f(t_eval) + np.testing.assert_allclose( + out, np.array([sim[outvar](t_eval) for outvar in output_variables]).T + ) + + # Scalar evaluation + + @parameterized.expand(testcase, skip_on_empty=True) + def test_f_scalar(self, output_variables, idaklu_jax_solver, f, wrapper): + out = wrapper(f)(t_eval[k], inputs) + np.testing.assert_allclose( + out, np.array([sim[outvar](t_eval[k]) for outvar in output_variables]).T + ) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_f_vector(self, output_variables, idaklu_jax_solver, f, wrapper): + out = wrapper(f)(t_eval, inputs) + np.testing.assert_allclose( + out, np.array([sim[outvar](t_eval) for outvar in output_variables]).T + ) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_f_vmap(self, output_variables, idaklu_jax_solver, f, wrapper): + out = wrapper(jax.vmap(f, in_axes=in_axes))(t_eval, inputs) + np.testing.assert_allclose( + out, np.array([sim[outvar](t_eval) for outvar in output_variables]).T + ) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_f_batch_over_inputs(self, output_variables, idaklu_jax_solver, f, wrapper): + inputs_mock = np.array([1.0, 2.0, 3.0]) + with self.assertRaises(NotImplementedError): + wrapper(jax.vmap(f, in_axes=(None, 0)))(t_eval, inputs_mock) + + # Get all vars (should mirror test_f_* [above]) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_getvars_call_signature( + self, output_variables, idaklu_jax_solver, f, wrapper + ): + if wrapper == jax.jit: + return # test does not involve a JAX expression + with self.assertRaises(ValueError): + idaklu_jax_solver.get_vars() # no variable name specified + idaklu_jax_solver.get_vars(output_variables) # (okay) + idaklu_jax_solver.get_vars(f, output_variables) # (okay) + with self.assertRaises(ValueError): + idaklu_jax_solver.get_vars(1, 2, 3) # too many arguments + + @parameterized.expand(testcase, skip_on_empty=True) + def test_getvars_scalar(self, output_variables, idaklu_jax_solver, f, wrapper): + out = wrapper(idaklu_jax_solver.get_vars(output_variables))(t_eval[k], inputs) + np.testing.assert_allclose( + out, np.array([sim[outvar](t_eval[k]) for outvar in output_variables]).T + ) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_getvars_vector(self, output_variables, idaklu_jax_solver, f, wrapper): + out = wrapper(idaklu_jax_solver.get_vars(output_variables))(t_eval, inputs) + np.testing.assert_allclose( + out, np.array([sim[outvar](t_eval) for outvar in output_variables]).T + ) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_getvars_vector_array( + self, output_variables, idaklu_jax_solver, f, wrapper + ): + if wrapper == jax.jit: + return # test does not involve a JAX expression + array = np.array([sim[outvar](t_eval) for outvar in output_variables]).T + out = idaklu_jax_solver.get_vars(array, output_variables) + np.testing.assert_allclose(out, array) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_getvars_vmap(self, output_variables, idaklu_jax_solver, f, wrapper): + out = wrapper( + jax.vmap( + idaklu_jax_solver.get_vars(output_variables), + in_axes=(0, None), + ), + )(t_eval, inputs) + np.testing.assert_allclose( + out, np.array([sim[outvar](t_eval) for outvar in output_variables]).T + ) + + # Isolate single output variable + + @parameterized.expand(testcase, skip_on_empty=True) + def test_getvar_call_signature( + self, output_variables, idaklu_jax_solver, f, wrapper + ): + if wrapper == jax.jit: + return # test does not involve a JAX expression + with self.assertRaises(ValueError): + idaklu_jax_solver.get_var() # no variable name specified + idaklu_jax_solver.get_var(output_variables[0]) # (okay) + idaklu_jax_solver.get_var(f, output_variables[0]) # (okay) + with self.assertRaises(ValueError): + idaklu_jax_solver.get_var(1, 2, 3) # too many arguments + + @parameterized.expand(testcase, skip_on_empty=True) + def test_getvar_scalar_float_jaxpr( + self, output_variables, idaklu_jax_solver, f, wrapper + ): + # Per variable checks using the default JAX expression (self.jaxpr) + for outvar in output_variables: + out = wrapper(idaklu_jax_solver.get_var(outvar))(float(t_eval[k]), inputs) + np.testing.assert_allclose(out, sim[outvar](float(t_eval[k]))) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_getvar_scalar_float_f( + self, output_variables, idaklu_jax_solver, f, wrapper + ): + # Per variable checks using a provided JAX expression (f) + for outvar in output_variables: + out = wrapper(idaklu_jax_solver.get_var(f, outvar))( + float(t_eval[k]), inputs + ) + np.testing.assert_allclose(out, sim[outvar](float(t_eval[k]))) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_getvar_scalar_jaxpr(self, output_variables, idaklu_jax_solver, f, wrapper): + # Per variable checks using the default JAX expression (self.jaxpr) + for outvar in output_variables: + out = wrapper(idaklu_jax_solver.get_var(outvar))(t_eval[k], inputs) + np.testing.assert_allclose(out, sim[outvar](t_eval[k])) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_getvar_scalar_f(self, output_variables, idaklu_jax_solver, f, wrapper): + # Per variable checks using a provided JAX expression (f) + for outvar in output_variables: + out = wrapper(idaklu_jax_solver.get_var(outvar))(t_eval[k], inputs) + np.testing.assert_allclose(out, sim[outvar](t_eval[k])) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_getvar_vector_jaxpr(self, output_variables, idaklu_jax_solver, f, wrapper): + # Per variable checks using the default JAX expression (self.jaxpr) + for outvar in output_variables: + out = wrapper(idaklu_jax_solver.get_var(outvar))(t_eval, inputs) + np.testing.assert_allclose(out, sim[outvar](t_eval)) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_getvar_vector_f(self, output_variables, idaklu_jax_solver, f, wrapper): + # Per variable checks using a provided JAX expression (f) + for outvar in output_variables: + out = wrapper(idaklu_jax_solver.get_var(f, outvar))(t_eval, inputs) + np.testing.assert_allclose(out, sim[outvar](t_eval)) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_getvar_vector_array(self, output_variables, idaklu_jax_solver, f, wrapper): + # Per variable checks using a provided np.ndarray + if wrapper == jax.jit: + return # test does not involve a JAX expression + array = np.array([sim[outvar](t_eval) for outvar in output_variables]).T + for outvar in output_variables: + out = idaklu_jax_solver.get_var(array, outvar) + np.testing.assert_allclose(out, sim[outvar](t_eval)) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_getvar_vmap(self, output_variables, idaklu_jax_solver, f, wrapper): + for outvar in output_variables: + out = wrapper( + jax.vmap( + idaklu_jax_solver.get_var(outvar), + in_axes=(0, None), + ), + )(t_eval, inputs) + np.testing.assert_allclose(out, sim[outvar](t_eval)) + + # Differentiation rules (jacfwd) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_jacfwd_scalar(self, output_variables, idaklu_jax_solver, f, wrapper): + out = wrapper(jax.jacfwd(f, argnums=1))(t_eval[k], inputs) + flat_out, _ = tree_flatten(out) + flat_out = np.array([f for f in flat_out]).flatten() + check = np.array( + [ + sim[outvar].sensitivities[invar][k] + for invar in inputs + for outvar in output_variables + ] + ).T + np.testing.assert_allclose(flat_out, check.flatten()) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_jacfwd_vector(self, output_variables, idaklu_jax_solver, f, wrapper): + out = wrapper(jax.jacfwd(f, argnums=1))(t_eval, inputs) + flat_out, _ = tree_flatten(out) + flat_out = np.concatenate(np.array([f for f in flat_out]), 1).T.flatten() + check = np.array( + [ + sim[outvar].sensitivities[invar] + for invar in inputs + for outvar in output_variables + ] + ) + ( + np.testing.assert_allclose(flat_out, check.flatten()), + f"Got: {flat_out}\nExpected: {check}", + ) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_jacfwd_vmap(self, output_variables, idaklu_jax_solver, f, wrapper): + out = wrapper( + jax.vmap( + jax.jacfwd(f, argnums=1), + in_axes=(0, None), + ), + )(t_eval, inputs) + flat_out, _ = tree_flatten(out) + flat_out = np.concatenate(np.array([f for f in flat_out]), 1).T.flatten() + check = np.array( + [ + sim[outvar].sensitivities[invar] + for invar in inputs + for outvar in output_variables + ] + ) + np.testing.assert_allclose(flat_out, check.flatten()) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_jacfwd_vmap_wrt_time( + self, output_variables, idaklu_jax_solver, f, wrapper + ): + with self.assertRaises(NotImplementedError): + wrapper( + jax.vmap( + jax.jacfwd(f, argnums=0), + in_axes=(0, None), + ), + )(t_eval, inputs) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_jacfwd_batch_over_inputs( + self, output_variables, idaklu_jax_solver, f, wrapper + ): + inputs_mock = np.array([1.0, 2.0, 3.0]) + with self.assertRaises(NotImplementedError): + wrapper( + jax.vmap( + jax.jacfwd(f, argnums=1), + in_axes=(None, 0), + ), + )(t_eval, inputs_mock) + + # Differentiation rules (jacrev) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_jacrev_scalar(self, output_variables, idaklu_jax_solver, f, wrapper): + out = wrapper(jax.jacrev(f, argnums=1))(t_eval[k], inputs) + flat_out, _ = tree_flatten(out) + flat_out = np.array([f for f in flat_out]).flatten() + check = np.array( + [ + sim[outvar].sensitivities[invar][k] + for invar in inputs + for outvar in output_variables + ] + ).T + np.testing.assert_allclose(flat_out, check.flatten()) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_jacrev_vector(self, output_variables, idaklu_jax_solver, f, wrapper): + out = wrapper(jax.jacrev(f, argnums=1))(t_eval[k], inputs) + out = wrapper(jax.jacrev(f, argnums=1))(t_eval, inputs) + flat_out, _ = tree_flatten(out) + flat_out = np.concatenate(np.array([f for f in flat_out]), 1).T.flatten() + check = np.array( + [ + sim[outvar].sensitivities[invar] + for invar in inputs + for outvar in output_variables + ] + ) + np.testing.assert_allclose(flat_out, check.flatten()) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_jacrev_vmap(self, output_variables, idaklu_jax_solver, f, wrapper): + out = wrapper( + jax.vmap( + jax.jacrev(f, argnums=1), + in_axes=(0, None), + ), + )(t_eval, inputs) + flat_out, _ = tree_flatten(out) + flat_out = np.concatenate(np.array([f for f in flat_out]), 1).T.flatten() + check = np.array( + [ + sim[outvar].sensitivities[invar] + for invar in inputs + for outvar in output_variables + ] + ) + np.testing.assert_allclose(flat_out, check.flatten()) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_jacrev_batch_over_inputs( + self, output_variables, idaklu_jax_solver, f, wrapper + ): + inputs_mock = np.array([1.0, 2.0, 3.0]) + with self.assertRaises(NotImplementedError): + wrapper( + jax.vmap( + jax.jacrev(f, argnums=1), + in_axes=(None, 0), + ), + )(t_eval, inputs_mock) + + # Forward differentiation rules with get_vars (multiple) and get_var (singular) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_jacfwd_scalar_getvars( + self, output_variables, idaklu_jax_solver, f, wrapper + ): + out = wrapper( + jax.jacfwd( + idaklu_jax_solver.get_vars(output_variables), + argnums=1, + ), + )(t_eval[k], inputs) + flat_out, _ = tree_flatten(out) + check = { # Form dictionary of results from IDAKLU simulation + invar: np.array( + [ + np.array(sim[outvar].sensitivities[invar][k]).squeeze() + for outvar in output_variables + ] + ) + for invar in inputs + } + flat_check, _ = tree_flatten(check) + np.testing.assert_allclose(flat_out, flat_check) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_jacfwd_scalar_getvar( + self, output_variables, idaklu_jax_solver, f, wrapper + ): + for outvar in output_variables: + out = wrapper( + jax.jacfwd( + idaklu_jax_solver.get_var(outvar), + argnums=1, + ), + )(t_eval[k], inputs) + flat_out, _ = tree_flatten(out) + check = { # Form dictionary of results from IDAKLU simulation + invar: np.array(sim[outvar].sensitivities[invar][k]).squeeze() + for invar in inputs + } + flat_check, _ = tree_flatten(check) + np.testing.assert_allclose(flat_out, flat_check) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_jacfwd_vector_getvars( + self, output_variables, idaklu_jax_solver, f, wrapper + ): + out = wrapper( + jax.jacfwd( + idaklu_jax_solver.get_vars(output_variables), + argnums=1, + ), + )(t_eval, inputs) + flat_out, _ = tree_flatten(out) + check = { # Form dictionary of results from IDAKLU simulation + invar: np.concatenate( + [ + np.array(sim[outvar].sensitivities[invar]) + for outvar in output_variables + ], + axis=1, + ) + for invar in inputs + } + flat_check, _ = tree_flatten(check) + np.testing.assert_allclose(flat_out, flat_check) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_jacfwd_vector_getvar( + self, output_variables, idaklu_jax_solver, f, wrapper + ): + for outvar in output_variables: + out = wrapper( + jax.jacfwd( + idaklu_jax_solver.get_var(outvar), + argnums=1, + ), + )(t_eval, inputs) + flat_out, _ = tree_flatten(out) + check = { # Form dictionary of results from IDAKLU simulation + invar: np.array(sim[outvar].sensitivities[invar]).flatten() + for invar in inputs + } + flat_check, _ = tree_flatten(check) + np.testing.assert_allclose(flat_out, flat_check) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_jacfwd_vmap_getvars(self, output_variables, idaklu_jax_solver, f, wrapper): + out = wrapper( + jax.vmap( + jax.jacfwd(idaklu_jax_solver.get_vars(output_variables), argnums=1), + in_axes=(0, None), + ), + )(t_eval, inputs) + flat_out, _ = tree_flatten(out) + flat_out = np.concatenate(np.array([f for f in flat_out]), 1).T.flatten() + check = np.array( + [ + sim[outvar].sensitivities[invar] + for invar in inputs + for outvar in output_variables + ] + ) + np.testing.assert_allclose(flat_out, check.flatten()) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_jacfwd_vmap_getvar(self, output_variables, idaklu_jax_solver, f, wrapper): + for outvar in output_variables: + out = wrapper( + jax.vmap( + jax.jacfwd(idaklu_jax_solver.get_var(outvar), argnums=1), + in_axes=(0, None), + ), + )(t_eval, inputs) + flat_out, _ = tree_flatten(out) + check = { # Form dictionary of results from IDAKLU simulation + invar: np.array(sim[outvar].sensitivities[invar]).flatten() + for invar in inputs + } + flat_check, _ = tree_flatten(check) + np.testing.assert_allclose(flat_out, flat_check) + + # Reverse differentiation rules with get_vars (multiple) and get_var (singular) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_jacrev_scalar_getvars( + self, output_variables, idaklu_jax_solver, f, wrapper + ): + out = wrapper( + jax.jacrev( + idaklu_jax_solver.get_vars(output_variables), + argnums=1, + ), + )(t_eval[k], inputs) + flat_out, _ = tree_flatten(out) + check = { # Form dictionary of results from IDAKLU simulation + invar: np.array( + [ + np.array(sim[outvar].sensitivities[invar][k]).squeeze() + for outvar in output_variables + ] + ) + for invar in inputs + } + flat_check, _ = tree_flatten(check) + np.testing.assert_allclose(flat_out, flat_check) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_jacrev_scalar_getvar( + self, output_variables, idaklu_jax_solver, f, wrapper + ): + for outvar in output_variables: + out = wrapper( + jax.jacrev( + idaklu_jax_solver.get_var(outvar), + argnums=1, + ), + )(t_eval[k], inputs) + flat_out, _ = tree_flatten(out) + flat_out = np.array([f for f in flat_out]).flatten() + check = np.array( + [sim[outvar].sensitivities[invar][k] for invar in inputs] + ).T + ( + np.testing.assert_allclose(flat_out, check.flatten()), + f"Got: {flat_out}\nExpected: {check}", + ) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_jacrev_vector_getvars( + self, output_variables, idaklu_jax_solver, f, wrapper + ): + out = wrapper( + jax.jacrev( + idaklu_jax_solver.get_vars(output_variables), + argnums=1, + ), + )(t_eval, inputs) + flat_out, _ = tree_flatten(out) + check = { # Form dictionary of results from IDAKLU simulation + invar: np.concatenate( + [ + np.array(sim[outvar].sensitivities[invar]) + for outvar in output_variables + ], + axis=1, + ) + for invar in inputs + } + flat_check, _ = tree_flatten(check) + np.testing.assert_allclose(flat_out, flat_check) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_jacrev_vector_getvar( + self, output_variables, idaklu_jax_solver, f, wrapper + ): + for outvar in output_variables: + out = wrapper( + jax.jacrev( + idaklu_jax_solver.get_var(outvar), + argnums=1, + ), + )(t_eval, inputs) + flat_out, _ = tree_flatten(out) + check = { # Form dictionary of results from IDAKLU simulation + invar: np.array(sim[outvar].sensitivities[invar]).flatten() + for invar in inputs + } + flat_check, _ = tree_flatten(check) + np.testing.assert_allclose(flat_out, flat_check) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_jacrev_vmap_getvars(self, output_variables, idaklu_jax_solver, f, wrapper): + out = wrapper( + jax.vmap( + jax.jacrev(idaklu_jax_solver.get_vars(output_variables), argnums=1), + in_axes=(0, None), + ), + )(t_eval, inputs) + flat_out, _ = tree_flatten(out) + flat_out = np.concatenate(np.array([f for f in flat_out]), 1).T.flatten() + check = np.array( + [ + sim[outvar].sensitivities[invar] + for invar in inputs + for outvar in output_variables + ] + ) + np.testing.assert_allclose(flat_out, check.flatten()) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_jacrev_vmap_getvar(self, output_variables, idaklu_jax_solver, f, wrapper): + for outvar in output_variables: + out = wrapper( + jax.vmap( + jax.jacrev(idaklu_jax_solver.get_var(outvar), argnums=1), + in_axes=(0, None), + ), + )(t_eval, inputs) + flat_out, _ = tree_flatten(out) + check = { # Form dictionary of results from IDAKLU simulation + invar: np.array(sim[outvar].sensitivities[invar]).flatten() + for invar in inputs + } + flat_check, _ = tree_flatten(check) + np.testing.assert_allclose(flat_out, flat_check) + + # Gradient rule (takes single variable) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_grad_scalar_getvar(self, output_variables, idaklu_jax_solver, f, wrapper): + for outvar in output_variables: + out = wrapper( + jax.grad( + idaklu_jax_solver.get_var(outvar), + argnums=1, + ), + )(t_eval[k], inputs) # output should be a dictionary of inputs + flat_out, _ = tree_flatten(out) + flat_out = np.array([f for f in flat_out]).flatten() + check = np.array([sim[outvar].sensitivities[invar][k] for invar in inputs]) + np.testing.assert_allclose(flat_out, check.flatten()) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_grad_vmap_getvar(self, output_variables, idaklu_jax_solver, f, wrapper): + for outvar in output_variables: + out = wrapper( + jax.vmap( + jax.grad( + idaklu_jax_solver.get_var(outvar), + argnums=1, + ), + in_axes=(0, None), + ), + )(t_eval, inputs) + flat_out, _ = tree_flatten(out) + flat_out = np.array([f for f in flat_out]).flatten() + check = np.array([sim[outvar].sensitivities[invar] for invar in inputs]) + np.testing.assert_allclose(flat_out, check.flatten()) + + # Value and gradient (takes single variable) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_value_and_grad_scalar( + self, output_variables, idaklu_jax_solver, f, wrapper + ): + for outvar in output_variables: + primals, tangents = wrapper( + jax.value_and_grad( + idaklu_jax_solver.get_var(outvar), + argnums=1, + ), + )(t_eval[k], inputs) + flat_p, _ = tree_flatten(primals) + flat_p = np.array([f for f in flat_p]).flatten() + check = np.array(sim[outvar].data[k]) + np.testing.assert_allclose(flat_p, check.flatten()) + flat_t, _ = tree_flatten(tangents) + flat_t = np.array([f for f in flat_t]).flatten() + check = np.array([sim[outvar].sensitivities[invar][k] for invar in inputs]) + np.testing.assert_allclose(flat_t, check.flatten()) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_value_and_grad_vmap(self, output_variables, idaklu_jax_solver, f, wrapper): + for outvar in output_variables: + primals, tangents = wrapper( + jax.vmap( + jax.value_and_grad( + idaklu_jax_solver.get_var(outvar), + argnums=1, + ), + in_axes=(0, None), + ), + )(t_eval, inputs) + flat_p, _ = tree_flatten(primals) + flat_p = np.array([f for f in flat_p]).flatten() + check = np.array(sim[outvar].data) + np.testing.assert_allclose(flat_p, check.flatten()) + flat_t, _ = tree_flatten(tangents) + flat_t = np.array([f for f in flat_t]).flatten() + check = np.array([sim[outvar].sensitivities[invar] for invar in inputs]) + np.testing.assert_allclose(flat_t, check.flatten()) + + # Helper functions - These return values (not jaxexprs) so cannot be JITed + + @parameterized.expand(testcase, skip_on_empty=True) + def test_jax_vars(self, output_variables, idaklu_jax_solver, f, wrapper): + if wrapper == jax.jit: + # Skipping test_jax_vars for jax.jit, jit not supported on helper functions + return + out = idaklu_jax_solver.jax_value(t_eval, inputs) + for outvar in output_variables: + flat_out, _ = tree_flatten(out[outvar]) + flat_out = np.array([f for f in flat_out]).flatten() + check = np.array(sim[outvar].data) + ( + np.testing.assert_allclose(flat_out, check.flatten()), + f"{outvar}: Got: {flat_out}\nExpected: {check}", + ) + + @parameterized.expand(testcase, skip_on_empty=True) + def test_jax_grad(self, output_variables, idaklu_jax_solver, f, wrapper): + if wrapper == jax.jit: + # Skipping test_jax_grad for jax.jit, jit not supported on helper functions + return + out = idaklu_jax_solver.jax_grad(t_eval, inputs) + for outvar in output_variables: + flat_out, _ = tree_flatten(out[outvar]) + flat_out = np.array([f for f in flat_out]).flatten() + check = np.array([sim[outvar].sensitivities[invar] for invar in inputs]) + ( + np.testing.assert_allclose(flat_out, check.flatten()), + f"{outvar}: Got: {flat_out}\nExpected: {check}", + ) + + # Wrap jaxified expression in another function and take the gradient + + @parameterized.expand(testcase, skip_on_empty=True) + def test_grad_wrapper_sse(self, output_variables, idaklu_jax_solver, f, wrapper): + # Use surrogate for experimental data + data = sim["v"](t_eval) + + # Define SSE function + # + # Note that although f returns a vector over time, sse() returns a scalar so + # that it can be passed to grad() directly using time-vector inputs. + def sse(t, inputs): + vf = idaklu_jax_solver.get_var("v") + return jnp.sum((vf(t_eval, inputs) - data) ** 2) + + # Create an imperfect prediction + inputs_pred = inputs.copy() + inputs_pred["a"] = 0.150 + sim_pred = idaklu_solver.solve( + model, + t_eval, + inputs=inputs_pred, + calculate_sensitivities=True, + ) + pred = sim_pred["v"] + + # Check value against actual SSE + sse_actual = np.sum((pred(t_eval) - data) ** 2) + flat_out, _ = tree_flatten(sse(t_eval, inputs_pred)) + flat_out = np.array([f for f in flat_out]).flatten() + flat_check_val, _ = tree_flatten(sse_actual) + ( + np.testing.assert_allclose(flat_out, flat_check_val, 1e-3), + f"Got: {flat_out}\nExpected: {flat_check_val}", + ) + + # Check grad against actual + sse_grad_actual = {} + for k, _ in inputs_pred.items(): + sse_grad_actual[k] = 2 * np.sum( + (pred(t_eval) - data) * pred.sensitivities[k] + ) + sse_grad = wrapper(jax.grad(sse, argnums=1))(t_eval, inputs_pred) + flat_out, _ = tree_flatten(sse_grad) + flat_out = np.array([f for f in flat_out]).flatten() + flat_check_grad, _ = tree_flatten(sse_grad_actual) + ( + np.testing.assert_allclose(flat_out, flat_check_grad, 1e-3), + f"Got: {flat_out}\nExpected: {flat_check_grad}", + ) + + # Check value_and_grad return + sse_val, sse_grad = wrapper(jax.value_and_grad(sse, argnums=1))( + t_eval, inputs_pred + ) + flat_sse_grad, _ = tree_flatten(sse_grad) + flat_sse_grad = np.array([f for f in flat_sse_grad]).flatten() + ( + np.testing.assert_allclose(sse_val, flat_check_val, 1e3), + f"Got: {sse_val}\nExpected: {flat_check_val}", + ) + ( + np.testing.assert_allclose(flat_sse_grad, flat_check_grad, 1e3), + f"Got: {sse_grad}\nExpected: {sse_grad}", + ) diff --git a/tests/unit/test_solvers/test_idaklu_solver.py b/tests/unit/test_solvers/test_idaklu_solver.py index 604f559049..1697623486 100644 --- a/tests/unit/test_solvers/test_idaklu_solver.py +++ b/tests/unit/test_solvers/test_idaklu_solver.py @@ -160,7 +160,7 @@ def test_input_params(self): model.initial_conditions = {u1: 0, u2: 0, u3: 0, v: 1} disc = pybamm.Discretisation() - disc.process_model(model, remove_independent_variables_from_rhs=False) + disc.process_model(model) solver = pybamm.IDAKLUSolver(root_method=root_method) @@ -550,21 +550,27 @@ def test_with_output_variables(self): # Construct a model and solve for all vairables, then test # the 'output_variables' option for each variable in turn, confirming # equivalence - - # construct model - model = pybamm.lithium_ion.DFN() - geometry = model.default_geometry - param = model.default_parameter_values input_parameters = {} # Sensitivities dictionary - param.update({key: "[input]" for key in input_parameters}) - param.process_model(model) - param.process_geometry(geometry) - var_pts = {"x_n": 50, "x_s": 50, "x_p": 50, "r_n": 5, "r_p": 5} - mesh = pybamm.Mesh(geometry, model.default_submesh_types, var_pts) - disc = pybamm.Discretisation(mesh, model.default_spatial_methods) - disc.process_model(model) t_eval = np.linspace(0, 3600, 100) + # construct model + def construct_model(): + model = pybamm.lithium_ion.DFN() + geometry = model.default_geometry + param = model.default_parameter_values + param.update({key: "[input]" for key in input_parameters}) + param.process_model(model) + param.process_geometry(geometry) + var_pts = {"x_n": 50, "x_s": 50, "x_p": 50, "r_n": 5, "r_p": 5} + mesh = pybamm.Mesh(geometry, model.default_submesh_types, var_pts) + disc = pybamm.Discretisation( + mesh, + model.default_spatial_methods, + remove_independent_variables_from_rhs=True, + ) + disc.process_model(model) + return model + options = { "linear_solver": "SUNLinSol_KLU", "jacobian": "sparse", @@ -585,6 +591,24 @@ def test_with_output_variables(self): "Throughput capacity [A.h]", # ExplicitTimeIntegral ] + # vars that are not in the output_variables list, but are still accessible as + # they are either model parameters, or do not require access to the state vector + model_vars = [ + "Time [s]", + "C-rate", + "Ambient temperature [K]", + "Porosity", + ] + + # A list of variables that are not in the model and cannot be computed + inaccessible_vars = [ + "Terminal voltage [V]", + "Negative particle surface stoichiometry", + "Electrode current density [A.m-2]", + "Power [W]", + "Resistance [Ohm]", + ] + # Use the full model as comparison (tested separately) solver_all = pybamm.IDAKLUSolver( atol=1e-8, @@ -592,7 +616,7 @@ def test_with_output_variables(self): options=options, ) sol_all = solver_all.solve( - model, + construct_model(), t_eval, inputs=input_parameters, calculate_sensitivities=True, @@ -606,15 +630,20 @@ def test_with_output_variables(self): output_variables=output_variables, ) sol = solver.solve( - model, + construct_model(), t_eval, inputs=input_parameters, ) # Compare output to sol_all - for varname in output_variables: + for varname in [*output_variables, *model_vars]: self.assertTrue(np.allclose(sol[varname].data, sol_all[varname].data)) + # Check that the missing variables are not available in the solution + for varname in inaccessible_vars: + with self.assertRaises(KeyError): + sol[varname].data + # Mock a 1D current collector and initialise (none in the model) sol["x_s [m]"].domain = ["current collector"] sol["x_s [m]"].initialise_1D() diff --git a/tests/unit/test_solvers/test_jax_bdf_solver.py b/tests/unit/test_solvers/test_jax_bdf_solver.py index 92fb710ea9..854a618fba 100644 --- a/tests/unit/test_solvers/test_jax_bdf_solver.py +++ b/tests/unit/test_solvers/test_jax_bdf_solver.py @@ -3,7 +3,6 @@ from tests import get_mesh_for_testing from tests import TestCase import sys -import time import numpy as np if pybamm.have_jax(): @@ -36,19 +35,12 @@ def test_solver_(self): # Trailing _ manipulates the random seed def fun(y, t): return rhs(t=t, y=y).reshape(-1) - t0 = time.perf_counter() y = pybamm.jax_bdf_integrate(fun, y0, t_eval, rtol=1e-8, atol=1e-8) - t1 = time.perf_counter() - t0 # test accuracy np.testing.assert_allclose(y[:, 0], np.exp(0.1 * t_eval), rtol=1e-6, atol=1e-6) - t0 = time.perf_counter() y = pybamm.jax_bdf_integrate(fun, y0, t_eval, rtol=1e-8, atol=1e-8) - t2 = time.perf_counter() - t0 - - # second run should be much quicker - self.assertLess(t2, t1) # test second run is accurate np.testing.assert_allclose(y[:, 0], np.exp(0.1 * t_eval), rtol=1e-6, atol=1e-6) @@ -66,21 +58,14 @@ def fun(y, t): # this as a guess y0 = jax.numpy.array([1.0, 1.5]) - t0 = time.perf_counter() y = pybamm.jax_bdf_integrate(fun, y0, t_eval, mass=mass, rtol=1e-8, atol=1e-8) - t1 = time.perf_counter() - t0 # test accuracy soln = np.exp(0.05 * t_eval) np.testing.assert_allclose(y[:, 0], soln, rtol=1e-7, atol=1e-7) np.testing.assert_allclose(y[:, 1], 2.0 * soln, rtol=1e-7, atol=1e-7) - t0 = time.perf_counter() y = pybamm.jax_bdf_integrate(fun, y0, t_eval, mass=mass, rtol=1e-8, atol=1e-8) - t2 = time.perf_counter() - t0 - - # second run should be much quicker - self.assertLess(t2, t1) # test second run is accurate np.testing.assert_allclose(y[:, 0], np.exp(0.05 * t_eval), rtol=1e-7, atol=1e-7) diff --git a/tests/unit/test_solvers/test_jax_solver.py b/tests/unit/test_solvers/test_jax_solver.py index c6b482e3f9..9df28e8ac2 100644 --- a/tests/unit/test_solvers/test_jax_solver.py +++ b/tests/unit/test_solvers/test_jax_solver.py @@ -3,7 +3,6 @@ from tests import get_mesh_for_testing from tests import TestCase import sys -import time import numpy as np if pybamm.have_jax(): @@ -32,9 +31,7 @@ def test_model_solver(self): # Solve solver = pybamm.JaxSolver(method=method, rtol=1e-8, atol=1e-8) t_eval = np.linspace(0, 1, 80) - t0 = time.perf_counter() solution = solver.solve(model, t_eval) - t_first_solve = time.perf_counter() - t0 np.testing.assert_array_equal(solution.t, t_eval) np.testing.assert_allclose( solution.y[0], np.exp(0.1 * solution.t), rtol=1e-6, atol=1e-6 @@ -46,11 +43,8 @@ def test_model_solver(self): ) self.assertEqual(solution.termination, "final time") - t0 = time.perf_counter() second_solution = solver.solve(model, t_eval) - t_second_solve = time.perf_counter() - t0 - self.assertLess(t_second_solve, t_first_solve) np.testing.assert_array_equal(second_solution.y, solution.y) def test_semi_explicit_model(self): @@ -75,9 +69,7 @@ def test_semi_explicit_model(self): # Solve solver = pybamm.JaxSolver(method="BDF", rtol=1e-8, atol=1e-8) t_eval = np.linspace(0, 1, 80) - t0 = time.perf_counter() solution = solver.solve(model, t_eval) - t_first_solve = time.perf_counter() - t0 np.testing.assert_array_equal(solution.t, t_eval) soln = np.exp(0.1 * solution.t) np.testing.assert_allclose(solution.y[0], soln, rtol=1e-7, atol=1e-7) @@ -89,11 +81,7 @@ def test_semi_explicit_model(self): ) self.assertEqual(solution.termination, "final time") - t0 = time.perf_counter() second_solution = solver.solve(model, t_eval) - t_second_solve = time.perf_counter() - t0 - - self.assertLess(t_second_solve, t_first_solve) np.testing.assert_array_equal(second_solution.y, solution.y) def test_solver_sensitivities(self): @@ -125,7 +113,7 @@ def test_solver_sensitivities(self): solve = solver.get_solve(model, t_eval) # create a dummy "model" where we calculate the sum of the time series - def solve_model(rate): + def solve_model(rate, solve=solve): return jax.numpy.sum(solve({"rate": rate})) # check answers with finite difference @@ -205,25 +193,18 @@ def test_model_solver_with_inputs(self): # Solve solver = pybamm.JaxSolver(rtol=1e-8, atol=1e-8) t_eval = np.linspace(0, 5, 80) - - t0 = time.perf_counter() solution = solver.solve(model, t_eval, inputs={"rate": 0.1}) - t_first_solve = time.perf_counter() - t0 np.testing.assert_allclose( solution.y[0], np.exp(-0.1 * solution.t), rtol=1e-6, atol=1e-6 ) - t0 = time.perf_counter() solution = solver.solve(model, t_eval, inputs={"rate": 0.2}) - t_second_solve = time.perf_counter() - t0 np.testing.assert_allclose( solution.y[0], np.exp(-0.2 * solution.t), rtol=1e-6, atol=1e-6 ) - self.assertLess(t_second_solve, t_first_solve) - def test_get_solve(self): # Create model model = pybamm.BaseModel() diff --git a/tests/unit/test_solvers/test_processed_variable.py b/tests/unit/test_solvers/test_processed_variable.py index d8b4ccfd0c..4cf3f9392e 100644 --- a/tests/unit/test_solvers/test_processed_variable.py +++ b/tests/unit/test_solvers/test_processed_variable.py @@ -28,9 +28,11 @@ def to_casadi(var_pybamm, y, inputs=None): def process_and_check_2D_variable( - var, first_spatial_var, second_spatial_var, disc=None, geometry_options={} + var, first_spatial_var, second_spatial_var, disc=None, geometry_options=None ): # first_spatial_var should be on the "smaller" domain, i.e "r" for an "r-x" variable + if geometry_options is None: + geometry_options = {} if disc is None: disc = tests.get_discretisation_for_testing() disc.set_variable_slices([var]) diff --git a/tests/unit/test_solvers/test_processed_variable_computed.py b/tests/unit/test_solvers/test_processed_variable_computed.py index b5f105b34b..7e0616c81b 100644 --- a/tests/unit/test_solvers/test_processed_variable_computed.py +++ b/tests/unit/test_solvers/test_processed_variable_computed.py @@ -32,9 +32,11 @@ def to_casadi(var_pybamm, y, inputs=None): def process_and_check_2D_variable( - var, first_spatial_var, second_spatial_var, disc=None, geometry_options={} + var, first_spatial_var, second_spatial_var, disc=None, geometry_options=None ): # first_spatial_var should be on the "smaller" domain, i.e "r" for an "r-x" variable + if geometry_options is None: + geometry_options = {} if disc is None: disc = tests.get_discretisation_for_testing() disc.set_variable_slices([var]) diff --git a/tests/unit/test_solvers/test_scikits_solvers.py b/tests/unit/test_solvers/test_scikits_solvers.py deleted file mode 100644 index db07678a3d..0000000000 --- a/tests/unit/test_solvers/test_scikits_solvers.py +++ /dev/null @@ -1,920 +0,0 @@ -# -# Tests for the Scikits Solver classes -# -from tests import TestCase -import pybamm -import numpy as np -import unittest -import warnings -from tests import get_mesh_for_testing, get_discretisation_for_testing -import sys - - -@unittest.skipIf(not pybamm.have_scikits_odes(), "scikits.odes not installed") -class TestScikitsSolvers(TestCase): - def test_model_ode_integrate_failure(self): - # Turn off warnings to ignore sqrt error - warnings.simplefilter("ignore") - - model = pybamm.BaseModel() - var = pybamm.Variable("var") - model.rhs = {var: -pybamm.sqrt(var)} - model.initial_conditions = {var: 1} - disc = pybamm.Discretisation() - disc.process_model(model) - - t_eval = np.linspace(0, 3, 100) - solver = pybamm.ScikitsOdeSolver() - # Expect solver to fail when y goes negative - with self.assertRaises(pybamm.SolverError): - solver.solve(model, t_eval) - - # Turn warnings back on - warnings.simplefilter("default") - - def test_model_dae_integrate_failure_bad_ics(self): - # Force model to fail by providing bad ics - solver = pybamm.ScikitsDaeSolver(rtol=1e-8, atol=1e-8) - - # Create custom model so that custom ics - class Model: - mass_matrix = pybamm.Matrix([[1.0, 0.0], [0.0, 0.0]]) - y0 = np.array([0.0, 1.0]) - terminate_events_eval = [] - convert_to_format = "python" - - def rhs_algebraic_eval(self, t, y, inputs): - return np.array([0.5 * np.ones_like(y[0]), 2 * y[0] - y[1]]) - - def jac_rhs_algebraic_eval(self, t, y, inputs): - return np.array([[0.0, 0.0], [2.0, -1.0]]) - - model = Model() - t_eval = np.linspace(0, 1, 100) - - with self.assertRaises(pybamm.SolverError): - solver._integrate(model, t_eval) - - def test_dae_integrate_bad_ics(self): - # Make sure that dae solver can fix bad ics automatically - # Constant - solver = pybamm.ScikitsDaeSolver(rtol=1e-8, atol=1e-8) - - model = pybamm.BaseModel() - var = pybamm.Variable("var") - var2 = pybamm.Variable("var2") - model.rhs = {var: 0.5} - model.algebraic = {var2: 2 * var - var2} - model.initial_conditions = {var: 0, var2: 1} - disc = pybamm.Discretisation() - disc.process_model(model) - - t_eval = np.linspace(0, 1, 100) - solver.set_up(model) - solver._set_initial_conditions(model, 0, {}, True) - # check y0 - np.testing.assert_array_equal(model.y0.full().flatten(), [0, 0]) - # check dae solutions - solution = solver.solve(model, t_eval) - np.testing.assert_array_equal(solution.t, t_eval) - np.testing.assert_allclose(0.5 * solution.t, solution.y[0]) - np.testing.assert_allclose(1.0 * solution.t, solution.y[1]) - - def test_dae_integrate_with_non_unity_mass(self): - # Constant - solver = pybamm.ScikitsDaeSolver(rtol=1e-8, atol=1e-8) - - # Create custom model so that custom mass matrix can be used - class Model: - mass_matrix = pybamm.Matrix([[4.0, 0.0], [0.0, 0.0]]) - y0 = np.array([0.0, 0.0]) - terminate_events_eval = [] - convert_to_format = "python" - len_rhs_and_alg = 2 - - def rhs_algebraic_eval(self, t, y, inputs): - return np.array([0.5 * np.ones_like(y[0]), 2.0 * y[0] - y[1]]) - - def jac_rhs_algebraic_eval(self, t, y, inputs): - return np.array([[0.0, 0.0], [2.0, -1.0]]) - - model = Model() - t_eval = np.linspace(0, 1, 100) - solution = solver._integrate(model, t_eval) - np.testing.assert_array_equal(solution.t, t_eval) - np.testing.assert_allclose(0.125 * solution.t, solution.y[0]) - np.testing.assert_allclose(0.25 * solution.t, solution.y[1]) - - def test_model_solver_ode_python(self): - model = pybamm.BaseModel() - model.convert_to_format = "python" - whole_cell = ["negative electrode", "separator", "positive electrode"] - var = pybamm.Variable("var", domain=whole_cell) - model.rhs = {var: 0.1 * var} - model.initial_conditions = {var: 1} - disc = get_discretisation_for_testing() - disc.process_model(model) - - # Solve - solver = pybamm.ScikitsOdeSolver(rtol=1e-9, atol=1e-9) - t_eval = np.linspace(0, 1, 100) - solution = solver.solve(model, t_eval) - np.testing.assert_array_equal(solution.t, t_eval) - np.testing.assert_allclose(solution.y[0], np.exp(0.1 * solution.t)) - - def test_model_solver_ode_events_python(self): - model = pybamm.BaseModel() - model.convert_to_format = "python" - whole_cell = ["negative electrode", "separator", "positive electrode"] - var = pybamm.Variable("var", domain=whole_cell) - model.rhs = {var: 0.1 * var} - model.initial_conditions = {var: 1} - model.events = [ - pybamm.Event("2 * var = 2.5", pybamm.min(2.5 - 2 * var)), - pybamm.Event("var = 1.5", pybamm.min(1.5 - var)), - ] - disc = get_discretisation_for_testing() - disc.process_model(model) - - # Solve - solver = pybamm.ScikitsOdeSolver(rtol=1e-9, atol=1e-9) - t_eval = np.linspace(0, 10, 100) - solution = solver.solve(model, t_eval) - np.testing.assert_allclose(solution.y[0], np.exp(0.1 * solution.t)) - np.testing.assert_array_less(solution.y[0, :-1], 1.5) - np.testing.assert_array_less(solution.y[0, :-1], 1.25) - np.testing.assert_equal(solution.t_event[0], solution.t[-1]) - np.testing.assert_array_equal(solution.y_event[:, 0], solution.y[:, -1]) - - def test_model_solver_ode_jacobian_python(self): - model = pybamm.BaseModel() - model.convert_to_format = "python" - whole_cell = ["negative electrode", "separator", "positive electrode"] - var1 = pybamm.Variable("var1", domain=whole_cell) - var2 = pybamm.Variable("var2", domain=whole_cell) - model.rhs = {var1: var1, var2: 1 - var1} - model.initial_conditions = {var1: 1.0, var2: -1.0} - model.variables = {"var1": var1, "var2": var2} - - disc = get_discretisation_for_testing() - disc.process_model(model) - - # Add user-supplied Jacobian to model - mesh = get_mesh_for_testing() - submesh = mesh[("negative electrode", "separator", "positive electrode")] - N = submesh.npts - - # Solve testing various linear solvers - linsolvers = [ - "dense", - # "lapackdense", - "spgmr", - "spbcgs", - "sptfqmr", - ] - - for linsolver in linsolvers: - solver = pybamm.ScikitsOdeSolver( - rtol=1e-9, atol=1e-9, extra_options={"linsolver": linsolver} - ) - t_eval = np.linspace(0, 1, 100) - solution = solver.solve(model, t_eval) - np.testing.assert_array_equal(solution.t, t_eval) - - T, Y = solution.t, solution.y - np.testing.assert_array_almost_equal( - model.variables["var1"].evaluate(T, Y), - np.ones((N, T.size)) * np.exp(T[np.newaxis, :]), - ) - np.testing.assert_array_almost_equal( - model.variables["var2"].evaluate(T, Y), - np.ones((N, T.size)) * (T[np.newaxis, :] - np.exp(T[np.newaxis, :])), - ) - - def test_model_solver_dae_python(self): - model = pybamm.BaseModel() - model.convert_to_format = "python" - whole_cell = ["negative electrode", "separator", "positive electrode"] - var1 = pybamm.Variable("var1", domain=whole_cell) - var2 = pybamm.Variable("var2", domain=whole_cell) - model.rhs = {var1: 0.1 * var1} - model.algebraic = {var2: 2 * var1 - var2} - model.initial_conditions = {var1: 1, var2: 2} - model.use_jacobian = False - disc = get_discretisation_for_testing() - disc.process_model(model) - - # Solve - solver = pybamm.ScikitsDaeSolver(rtol=1e-8, atol=1e-8, root_method="lm") - t_eval = np.linspace(0, 1, 100) - solution = solver.solve(model, t_eval) - np.testing.assert_array_equal(solution.t, t_eval) - np.testing.assert_allclose(solution.y[0], np.exp(0.1 * solution.t)) - np.testing.assert_allclose(solution.y[-1], 2 * np.exp(0.1 * solution.t)) - - @unittest.skipIf(not pybamm.have_jax(), "jax or jaxlib is not installed") - def test_model_solver_dae_jax(self): - model = pybamm.BaseModel() - model.convert_to_format = "jax" - whole_cell = ["negative electrode", "separator", "positive electrode"] - var1 = pybamm.Variable("var1", domain=whole_cell) - var2 = pybamm.Variable("var2", domain=whole_cell) - model.rhs = {var1: 0.1 * var1} - model.algebraic = {var2: 2 * var1 - var2} - model.initial_conditions = {var1: 1, var2: 2} - model.use_jacobian = False - disc = get_discretisation_for_testing() - disc.process_model(model) - - # Solve - solver = pybamm.ScikitsDaeSolver(rtol=1e-8, atol=1e-8, root_method="lm") - t_eval = np.linspace(0, 1, 100) - solution = solver.solve(model, t_eval) - np.testing.assert_array_equal(solution.t, t_eval) - np.testing.assert_allclose(solution.y[0], np.exp(0.1 * solution.t)) - np.testing.assert_allclose(solution.y[-1], 2 * np.exp(0.1 * solution.t)) - - def test_model_solver_dae_bad_ics_python(self): - model = pybamm.BaseModel() - model.convert_to_format = "python" - whole_cell = ["negative electrode", "separator", "positive electrode"] - var1 = pybamm.Variable("var1", domain=whole_cell) - var2 = pybamm.Variable("var2", domain=whole_cell) - model.rhs = {var1: 0.1 * var1} - model.algebraic = {var2: 2 * var1 - var2} - model.initial_conditions = {var1: 1, var2: 3} - disc = get_discretisation_for_testing() - disc.process_model(model) - - # Solve - solver = pybamm.ScikitsDaeSolver(rtol=1e-8, atol=1e-8, root_method="lm") - t_eval = np.linspace(0, 1, 100) - solution = solver.solve(model, t_eval) - np.testing.assert_array_equal(solution.t, t_eval) - np.testing.assert_allclose(solution.y[0], np.exp(0.1 * solution.t)) - np.testing.assert_allclose(solution.y[-1], 2 * np.exp(0.1 * solution.t)) - - def test_model_solver_dae_events_python(self): - model = pybamm.BaseModel() - model.convert_to_format = "python" - whole_cell = ["negative electrode", "separator", "positive electrode"] - var1 = pybamm.Variable("var1", domain=whole_cell) - var2 = pybamm.Variable("var2", domain=whole_cell) - model.rhs = {var1: 0.1 * var1} - model.algebraic = {var2: 2 * var1 - var2} - model.initial_conditions = {var1: 1, var2: 2} - model.events = [ - pybamm.Event("var1 = 1.5", pybamm.min(1.5 - var1)), - pybamm.Event("var2 = 2.5", pybamm.min(2.5 - var2)), - ] - disc = get_discretisation_for_testing() - disc.process_model(model) - - # Solve - solver = pybamm.ScikitsDaeSolver(rtol=1e-8, atol=1e-8, root_method="lm") - t_eval = np.linspace(0, 5, 100) - solution = solver.solve(model, t_eval) - np.testing.assert_array_less(solution.y[0, :-1], 1.5) - np.testing.assert_array_less(solution.y[-1, :-1], 2.5) - np.testing.assert_allclose(solution.y[0], np.exp(0.1 * solution.t)) - np.testing.assert_allclose(solution.y[-1], 2 * np.exp(0.1 * solution.t)) - np.testing.assert_equal(solution.t_event[0], solution.t[-1]) - np.testing.assert_array_equal(solution.y_event[:, 0], solution.y[:, -1]) - - def test_model_solver_dae_nonsmooth_python(self): - model = pybamm.BaseModel() - model.convert_to_format = "python" - whole_cell = ["negative electrode", "separator", "positive electrode"] - var1 = pybamm.Variable("var1", domain=whole_cell) - var2 = pybamm.Variable("var2", domain=whole_cell) - discontinuity = 0.6 - - def nonsmooth_rate(t): - return 0.1 * (t < discontinuity) + 0.1 - - def nonsmooth_mult(t): - return (t < discontinuity) + 1.0 - - rate = nonsmooth_rate(pybamm.t) - mult = nonsmooth_mult(pybamm.t) - # put in an extra heaviside with no time dependence, this should be ignored by - # the solver i.e. no extra discontinuities added - model.rhs = {var1: rate * var1 + (var1 < 0)} - model.algebraic = {var2: mult * var1 - var2} - model.initial_conditions = {var1: 1, var2: 2} - model.events = [ - pybamm.Event("var1 = 1.5", pybamm.min(1.5 - var1)), - pybamm.Event("var2 = 2.5", pybamm.min(2.5 - var2)), - pybamm.Event( - "nonsmooth rate", - pybamm.Scalar(discontinuity), - pybamm.EventType.DISCONTINUITY, - ), - pybamm.Event( - "nonsmooth mult", - pybamm.Scalar(discontinuity), - pybamm.EventType.DISCONTINUITY, - ), - ] - disc = get_discretisation_for_testing() - disc.process_model(model) - - # Solve - solver = pybamm.ScikitsDaeSolver(rtol=1e-8, atol=1e-8, root_method="lm") - - # create two time series, one without a time point on the discontinuity, - # and one with - t_eval1 = np.linspace(0, 5, 10) - t_eval2 = np.insert( - t_eval1, np.searchsorted(t_eval1, discontinuity), discontinuity - ) - solution1 = solver.solve(model, t_eval1) - solution2 = solver.solve(model, t_eval2) - - # check time vectors - for solution in [solution1, solution2]: - # time vectors are ordered - self.assertTrue(np.all(solution.t[:-1] <= solution.t[1:])) - - # time value before and after discontinuity is an epsilon away - dindex = np.searchsorted(solution.t, discontinuity) - value_before = solution.t[dindex - 1] - value_after = solution.t[dindex] - self.assertEqual(value_before / (1 - sys.float_info.epsilon), discontinuity) - self.assertEqual(value_after / (1 + sys.float_info.epsilon), discontinuity) - - # both solution time vectors should have same number of points - self.assertEqual(len(solution1.t), len(solution2.t)) - - # check solution - for solution in [solution1, solution2]: - np.testing.assert_array_less(solution.y[0, :-1], 1.5) - np.testing.assert_array_less(solution.y[-1, :-1], 2.5) - var1_soln = np.exp(0.2 * solution.t) - y0 = np.exp(0.2 * discontinuity) - var1_soln[solution.t > discontinuity] = y0 * np.exp( - 0.1 * (solution.t[solution.t > discontinuity] - discontinuity) - ) - var2_soln = 2 * var1_soln - var2_soln[solution.t > discontinuity] = var1_soln[ - solution.t > discontinuity - ] - np.testing.assert_allclose(solution.y[0], var1_soln, rtol=1e-06) - np.testing.assert_allclose(solution.y[-1], var2_soln, rtol=1e-06) - - def test_model_solver_dae_multiple_nonsmooth_python(self): - model = pybamm.BaseModel() - model.convert_to_format = "python" - whole_cell = ["negative electrode", "separator", "positive electrode"] - var1 = pybamm.Variable("var1", domain=whole_cell) - var2 = pybamm.Variable("var2", domain=whole_cell) - a = 0.6 - discontinuities = (np.arange(3) + 1) * a - - model.rhs = {var1: pybamm.Modulo(pybamm.t, a)} - model.algebraic = {var2: 2 * var1 - var2} - model.initial_conditions = {var1: 0, var2: 0} - model.events = [ - pybamm.Event("var1 = 0.55", pybamm.min(0.55 - var1)), - pybamm.Event("var2 = 1.2", pybamm.min(1.2 - var2)), - ] - for discontinuity in discontinuities: - model.events.append( - pybamm.Event("nonsmooth rate", pybamm.Scalar(discontinuity)) - ) - disc = get_discretisation_for_testing() - disc.process_model(model) - - # Solve - solver = pybamm.ScikitsDaeSolver(rtol=1e-8, atol=1e-8, root_method="lm") - - # create two time series, one without a time point on the discontinuity, - # and one with - t_eval1 = np.linspace(0, 2, 10) - t_eval2 = np.insert( - t_eval1, np.searchsorted(t_eval1, discontinuities), discontinuities - ) - solution1 = solver.solve(model, t_eval1) - solution2 = solver.solve(model, t_eval2) - - # check time vectors - for solution in [solution1, solution2]: - # time vectors are ordered - self.assertTrue(np.all(solution.t[:-1] <= solution.t[1:])) - - # time value before and after discontinuity is an epsilon away - for discontinuity in discontinuities: - dindex = np.searchsorted(solution.t, discontinuity) - value_before = solution.t[dindex - 1] - value_after = solution.t[dindex] - self.assertEqual( - value_before / (1 - sys.float_info.epsilon), discontinuity - ) - self.assertEqual( - value_after / (1 + sys.float_info.epsilon), discontinuity - ) - - # both solution time vectors should have same number of points - self.assertEqual(len(solution1.t), len(solution2.t)) - - # check solution - for solution in [solution1, solution2]: - np.testing.assert_array_less(solution.y[0, :-1], 0.55) - np.testing.assert_array_less(solution.y[-1, :-1], 1.2) - var1_soln = (solution.t % a) ** 2 / 2 + a**2 / 2 * (solution.t // a) - var2_soln = 2 * var1_soln - np.testing.assert_allclose(solution.y[0], var1_soln, rtol=1e-06) - np.testing.assert_allclose(solution.y[-1], var2_soln, rtol=1e-06) - - def test_model_solver_dae_no_nonsmooth_python(self): - model = pybamm.BaseModel() - model.convert_to_format = "python" - whole_cell = ["negative electrode", "separator", "positive electrode"] - var1 = pybamm.Variable("var1", domain=whole_cell) - var2 = pybamm.Variable("var2", domain=whole_cell) - discontinuity = 5.6 - - def nonsmooth_rate(t): - return 0.1 * int(t < discontinuity) + 0.1 - - def nonsmooth_mult(t): - return int(t < discontinuity) + 1.0 - - # put in an extra heaviside with no time dependence, this should be ignored by - # the solver i.e. no extra discontinuities added - model.rhs = {var1: 0.1 * var1} - model.algebraic = {var2: 2 * var1 - var2} - model.initial_conditions = {var1: 1, var2: 2} - disc = get_discretisation_for_testing() - disc.process_model(model) - - # Solve - solver = pybamm.ScikitsDaeSolver(rtol=1e-9, atol=1e-9, root_method="lm") - - # create two time series, one without a time point on the discontinuity, - # and one with - t_eval = np.linspace(0, 5, 10) - solution = solver.solve(model, t_eval) - - # test solution, discontinuity should not be triggered - np.testing.assert_array_equal(solution.t, t_eval) - np.testing.assert_allclose(solution.y[0], np.exp(0.1 * solution.t)) - np.testing.assert_allclose(solution.y[-1], 2 * np.exp(0.1 * solution.t)) - - def test_model_solver_dae_with_jacobian_python(self): - model = pybamm.BaseModel() - model.convert_to_format = "python" - whole_cell = ["negative electrode", "separator", "positive electrode"] - var1 = pybamm.Variable("var1", domain=whole_cell) - var2 = pybamm.Variable("var2", domain=whole_cell) - model.rhs = {var1: 0.1 * var1} - model.algebraic = {var2: 2 * var1 - var2} - model.initial_conditions = {var1: 1.0, var2: 2.0} - model.initial_conditions_ydot = {var1: 0.1, var2: 0.2} - disc = get_discretisation_for_testing() - disc.process_model(model) - - # Add user-supplied Jacobian to model - mesh = get_mesh_for_testing() - submesh = mesh[("negative electrode", "separator", "positive electrode")] - N = submesh.npts - - def jacobian(t, y): - return np.block( - [ - [0.1 * np.eye(N), np.zeros((N, N))], - [2.0 * np.eye(N), -1.0 * np.eye(N)], - ] - ) - - model.jacobian = jacobian - # Solve - solver = pybamm.ScikitsDaeSolver(rtol=1e-8, atol=1e-8, root_method="lm") - t_eval = np.linspace(0, 1, 100) - solution = solver.solve(model, t_eval) - np.testing.assert_array_equal(solution.t, t_eval) - np.testing.assert_allclose(solution.y[0], np.exp(0.1 * solution.t)) - np.testing.assert_allclose(solution.y[-1], 2 * np.exp(0.1 * solution.t)) - - def test_solve_ode_model_with_dae_solver_python(self): - model = pybamm.BaseModel() - model.convert_to_format = "python" - var = pybamm.Variable("var") - model.rhs = {var: 0.1 * var} - model.initial_conditions = {var: 1} - disc = get_discretisation_for_testing() - disc.process_model(model) - - # Solve - solver = pybamm.ScikitsDaeSolver(rtol=1e-8, atol=1e-8, root_method="lm") - t_eval = np.linspace(0, 1, 100) - solution = solver.solve(model, t_eval) - np.testing.assert_array_equal(solution.t, t_eval) - np.testing.assert_allclose(solution.y[0], np.exp(0.1 * solution.t)) - - def test_model_step_ode_python(self): - model = pybamm.BaseModel() - model.convert_to_format = "python" - whole_cell = ["negative electrode", "separator", "positive electrode"] - var = pybamm.Variable("var", domain=whole_cell) - model.rhs = {var: -0.1 * var} - model.initial_conditions = {var: 1} - disc = get_discretisation_for_testing() - disc.process_model(model) - - solver = pybamm.ScikitsOdeSolver(rtol=1e-9, atol=1e-9) - - # Step once - dt = 1 - step_sol = solver.step(None, model, dt) - np.testing.assert_array_equal(step_sol.t, [0, dt]) - np.testing.assert_allclose(step_sol.y[0], np.exp(-0.1 * step_sol.t)) - - # Step again (return 5 points) - step_sol_2 = solver.step(step_sol, model, dt, npts=5) - np.testing.assert_array_equal( - step_sol_2.t, np.array([0, 1, 1 + 1e-9, 1.25, 1.5, 1.75, 2]) - ) - np.testing.assert_allclose(step_sol_2.y[0], np.exp(-0.1 * step_sol_2.t)) - - # Check steps give same solution as solve - t_eval = step_sol.t - solution = solver.solve(model, t_eval) - np.testing.assert_allclose(solution.y[0], step_sol.y[0], atol=1e-6, rtol=1e-6) - - def test_model_step_dae_python(self): - model = pybamm.BaseModel() - model.convert_to_format = "python" - whole_cell = ["negative electrode", "separator", "positive electrode"] - var1 = pybamm.Variable("var1", domain=whole_cell) - var2 = pybamm.Variable("var2", domain=whole_cell) - model.rhs = {var1: 0.1 * var1} - model.algebraic = {var2: 2 * var1 - var2} - model.initial_conditions = {var1: 1, var2: 2} - model.use_jacobian = False - disc = get_discretisation_for_testing() - disc.process_model(model) - - solver = pybamm.ScikitsDaeSolver(rtol=1e-8, atol=1e-8, root_method="lm") - - # Step once - dt = 1 - step_sol = solver.step(None, model, dt) - np.testing.assert_array_equal(step_sol.t, [0, dt]) - np.testing.assert_allclose(step_sol.y[0, :], np.exp(0.1 * step_sol.t)) - np.testing.assert_allclose(step_sol.y[-1, :], 2 * np.exp(0.1 * step_sol.t)) - - # Step again (return 5 points) - step_sol_2 = solver.step(step_sol, model, dt, npts=5) - np.testing.assert_array_equal( - step_sol_2.t, np.array([0, 1, 1 + 1e-9, 1.25, 1.5, 1.75, 2]) - ) - np.testing.assert_allclose(step_sol_2.y[0, :], np.exp(0.1 * step_sol_2.t)) - np.testing.assert_allclose(step_sol_2.y[-1, :], 2 * np.exp(0.1 * step_sol_2.t)) - - # Check steps give same solution as solve - t_eval = step_sol.t - solution = solver.solve(model, t_eval) - np.testing.assert_allclose(solution.y[0, :], step_sol.y[0, :]) - np.testing.assert_allclose(solution.y[-1, :], step_sol.y[-1, :]) - - def test_model_solver_ode_events_casadi(self): - # Create model - model = pybamm.BaseModel() - model.convert_to_format = "casadi" - whole_cell = ["negative electrode", "separator", "positive electrode"] - var = pybamm.Variable("var", domain=whole_cell) - model.rhs = {var: 0.1 * var} - model.initial_conditions = {var: 1} - model.events = [ - pybamm.Event("2 * var = 2.5", pybamm.min(2.5 - 2 * var)), - pybamm.Event("var = 1.5", pybamm.min(1.5 - var)), - ] - disc = get_discretisation_for_testing() - disc.process_model(model) - - # Solve - solver = pybamm.ScikitsOdeSolver(rtol=1e-9, atol=1e-9) - t_eval = np.linspace(0, 10, 100) - solution = solver.solve(model, t_eval) - np.testing.assert_allclose(solution.y[0], np.exp(0.1 * solution.t)) - np.testing.assert_array_less(solution.y[0:, -1], 1.5) - np.testing.assert_array_less(solution.y[0:, -1], 1.25 + 1e-9) - np.testing.assert_equal(solution.t_event[0], solution.t[-1]) - np.testing.assert_array_equal(solution.y_event[:, 0], solution.y[:, -1]) - - def test_model_solver_dae_events_casadi(self): - # Create model - model = pybamm.BaseModel() - for use_jacobian in [True, False]: - model.use_jacobian = use_jacobian - model.convert_to_format = "casadi" - whole_cell = ["negative electrode", "separator", "positive electrode"] - var1 = pybamm.Variable("var1", domain=whole_cell) - var2 = pybamm.Variable("var2", domain=whole_cell) - model.rhs = {var1: 0.1 * var1} - model.algebraic = {var2: 2 * var1 - var2} - model.initial_conditions = {var1: 1, var2: 2} - model.events = [ - pybamm.Event("var1 = 1.5", pybamm.min(1.5 - var1)), - pybamm.Event("var2 = 2.5", pybamm.min(2.5 - var2)), - ] - disc = get_discretisation_for_testing() - model_disc = disc.process_model(model, inplace=False) - - # Solve - solver = pybamm.ScikitsDaeSolver(rtol=1e-8, atol=1e-8) - t_eval = np.linspace(0, 5, 100) - solution = solver.solve(model_disc, t_eval) - np.testing.assert_array_less(solution.y[0, :-1], 1.5) - np.testing.assert_array_less(solution.y[-1, :-1], 2.5) - np.testing.assert_equal(solution.t_event[0], solution.t[-1]) - np.testing.assert_array_equal(solution.y_event[:, 0], solution.y[:, -1]) - np.testing.assert_allclose(solution.y[0], np.exp(0.1 * solution.t)) - np.testing.assert_allclose(solution.y[-1], 2 * np.exp(0.1 * solution.t)) - - def test_model_solver_dae_inputs_events(self): - # Create model - for form in ["python", "casadi"]: - model = pybamm.BaseModel() - model.convert_to_format = form - whole_cell = ["negative electrode", "separator", "positive electrode"] - var1 = pybamm.Variable("var1", domain=whole_cell) - var2 = pybamm.Variable("var2", domain=whole_cell) - model.rhs = {var1: pybamm.InputParameter("rate 1") * var1} - model.algebraic = {var2: pybamm.InputParameter("rate 2") * var1 - var2} - model.initial_conditions = {var1: 1, var2: 2} - model.events = [ - pybamm.Event("var1 = 1.5", pybamm.min(1.5 - var1)), - pybamm.Event("var2 = 2.5", pybamm.min(2.5 - var2)), - ] - disc = get_discretisation_for_testing() - disc.process_model(model) - - # Solve - if form == "python": - solver = pybamm.ScikitsDaeSolver(rtol=1e-8, atol=1e-8, root_method="lm") - else: - solver = pybamm.ScikitsDaeSolver(rtol=1e-8, atol=1e-8) - t_eval = np.linspace(0, 5, 100) - solution = solver.solve(model, t_eval, inputs={"rate 1": 0.1, "rate 2": 2}) - np.testing.assert_array_less(solution.y[0, :-1], 1.5) - np.testing.assert_array_less(solution.y[-1, :-1], 2.5) - np.testing.assert_array_equal(solution.y_event[:, 0], solution.y[:, -1]) - np.testing.assert_equal(solution.t_event[0], solution.t[-1]) - - np.testing.assert_allclose(solution.y[0], np.exp(0.1 * solution.t)) - np.testing.assert_allclose(solution.y[-1], 2 * np.exp(0.1 * solution.t)) - - def test_model_solver_dae_inputs_in_initial_conditions(self): - # Create model - model = pybamm.BaseModel() - var1 = pybamm.Variable("var1") - var2 = pybamm.Variable("var2") - model.rhs = {var1: pybamm.InputParameter("rate") * var1} - model.algebraic = {var2: var1 - var2} - model.initial_conditions = { - var1: pybamm.InputParameter("ic 1"), - var2: pybamm.InputParameter("ic 2"), - } - - # Solve - solver = pybamm.ScikitsDaeSolver(rtol=1e-8, atol=1e-8) - t_eval = np.linspace(0, 5, 100) - solution = solver.solve( - model, t_eval, inputs={"rate": -1, "ic 1": 0.1, "ic 2": 2} - ) - np.testing.assert_array_almost_equal( - solution.y[0], 0.1 * np.exp(-solution.t), decimal=5 - ) - np.testing.assert_array_almost_equal( - solution.y[-1], 0.1 * np.exp(-solution.t), decimal=5 - ) - - # Solve again with different initial conditions - solution = solver.solve( - model, t_eval, inputs={"rate": -0.1, "ic 1": 1, "ic 2": 3} - ) - np.testing.assert_array_almost_equal( - solution.y[0], 1 * np.exp(-0.1 * solution.t), decimal=5 - ) - np.testing.assert_array_almost_equal( - solution.y[-1], 1 * np.exp(-0.1 * solution.t), decimal=5 - ) - - def test_solve_ode_model_with_dae_solver_casadi(self): - model = pybamm.BaseModel() - model.convert_to_format = "casadi" - var = pybamm.Variable("var") - model.rhs = {var: 0.1 * var} - model.initial_conditions = {var: 1} - disc = get_discretisation_for_testing() - disc.process_model(model) - - # Solve - solver = pybamm.ScikitsDaeSolver(rtol=1e-8, atol=1e-8) - t_eval = np.linspace(0, 1, 100) - solution = solver.solve(model, t_eval) - np.testing.assert_array_equal(solution.t, t_eval) - np.testing.assert_allclose(solution.y[0], np.exp(0.1 * solution.t)) - - def test_model_step_events(self): - # Create model - model = pybamm.BaseModel() - var1 = pybamm.Variable("var1") - var2 = pybamm.Variable("var2") - model.rhs = {var1: 0.1 * var1} - model.algebraic = {var2: 2 * var1 - var2} - model.initial_conditions = {var1: 1, var2: 2} - model.events = [ - pybamm.Event("var1 = 1.5", pybamm.min(1.5 - var1)), - pybamm.Event("var2 = 2.5", pybamm.min(2.5 - var2)), - ] - disc = pybamm.Discretisation() - disc.process_model(model) - - # Solve - step_solver = pybamm.ScikitsDaeSolver(rtol=1e-8, atol=1e-8) - dt = 0.05 - time = 0 - end_time = 5 - step_solution = None - while time < end_time: - step_solution = step_solver.step(step_solution, model, dt=dt, npts=10) - time += dt - np.testing.assert_array_less(step_solution.y[0, :-1], 1.5) - np.testing.assert_array_less(step_solution.y[-1, :-1], 2.5) - np.testing.assert_equal(step_solution.t_event[0], step_solution.t[-1]) - np.testing.assert_array_equal( - step_solution.y_event[:, 0], step_solution.y[:, -1] - ) - np.testing.assert_array_almost_equal( - step_solution.y[0], np.exp(0.1 * step_solution.t), decimal=5 - ) - np.testing.assert_array_almost_equal( - step_solution.y[-1], 2 * np.exp(0.1 * step_solution.t), decimal=5 - ) - - def test_model_step_nonsmooth_events(self): - # Create model - model = pybamm.BaseModel() - var1 = pybamm.Variable("var1") - var2 = pybamm.Variable("var2") - - a = 0.6 - discontinuities = (np.arange(3) + 1) * a - - model.rhs = {var1: pybamm.Modulo(pybamm.t, a)} - model.algebraic = {var2: 2 * var1 - var2} - model.initial_conditions = {var1: 0, var2: 0} - model.events = [ - pybamm.Event("var1 = 0.55", pybamm.min(0.55 - var1)), - pybamm.Event("var2 = 1.2", pybamm.min(1.2 - var2)), - ] - for discontinuity in discontinuities: - model.events.append( - pybamm.Event("nonsmooth rate", pybamm.Scalar(discontinuity)) - ) - disc = get_discretisation_for_testing() - disc.process_model(model) - - # Solve - step_solver = pybamm.ScikitsDaeSolver(rtol=1e-8, atol=1e-8) - dt = 0.05 - time = 0 - end_time = 3 - step_solution = None - while time < end_time: - step_solution = step_solver.step(step_solution, model, dt=dt, npts=10) - time += dt - np.testing.assert_array_less(step_solution.y[0, :-1], 0.55) - np.testing.assert_array_less(step_solution.y[-1, :-1], 1.2) - np.testing.assert_equal(step_solution.t_event[0], step_solution.t[-1]) - np.testing.assert_array_equal( - step_solution.y_event[:, 0], step_solution.y[:, -1] - ) - var1_soln = (step_solution.t % a) ** 2 / 2 + a**2 / 2 * (step_solution.t // a) - var2_soln = 2 * var1_soln - np.testing.assert_array_almost_equal(step_solution.y[0], var1_soln, decimal=4) - np.testing.assert_array_almost_equal(step_solution.y[-1], var2_soln, decimal=4) - - def test_model_solver_dae_nonsmooth(self): - whole_cell = ["negative electrode", "separator", "positive electrode"] - var1 = pybamm.Variable("var1", domain=whole_cell) - var2 = pybamm.Variable("var2") - discontinuity = 0.6 - - # Create three different models with the same solution, each expressing the - # discontinuity in a different way - - # first model explicitly adds a discontinuity event - def nonsmooth_rate(t): - return 0.1 * (t < discontinuity) + 0.1 - - rate = pybamm.Function(nonsmooth_rate, pybamm.t) - model1 = pybamm.BaseModel() - model1.rhs = {var1: rate * var1} - model1.algebraic = {var2: var2} - model1.initial_conditions = {var1: 1, var2: 0} - model1.events = [ - pybamm.Event("var1 = 1.5", pybamm.min(1.5 - var1)), - pybamm.Event( - "nonsmooth rate", - pybamm.Scalar(discontinuity), - pybamm.EventType.DISCONTINUITY, - ), - ] - - # second model implicitly adds a discontinuity event via a heaviside function - model2 = pybamm.BaseModel() - model2.rhs = {var1: (0.1 * (pybamm.t < discontinuity) + 0.1) * var1} - model2.algebraic = {var2: var2} - model2.initial_conditions = {var1: 1, var2: 0} - model2.events = [pybamm.Event("var1 = 1.5", pybamm.min(1.5 - var1))] - - # third model implicitly adds a discontinuity event via another heaviside - # function - model3 = pybamm.BaseModel() - model3.rhs = {var1: (-0.1 * (discontinuity < pybamm.t) + 0.2) * var1} - model3.algebraic = {var2: var2} - model3.initial_conditions = {var1: 1, var2: 0} - model3.events = [pybamm.Event("var1 = 1.5", pybamm.min(1.5 - var1))] - - for model in [model1, model2, model3]: - disc = get_discretisation_for_testing() - disc.process_model(model) - - # Solve - solver = pybamm.ScikitsDaeSolver(rtol=1e-8, atol=1e-8) - - # create two time series, one without a time point on the discontinuity, - # and one with - t_eval1 = np.linspace(0, 5, 10) - t_eval2 = np.insert( - t_eval1, np.searchsorted(t_eval1, discontinuity), discontinuity - ) - solution1 = solver.solve(model, t_eval1) - solution2 = solver.solve(model, t_eval2) - - # check time vectors - for solution in [solution1, solution2]: - # time vectors are ordered - self.assertTrue(np.all(solution.t[:-1] <= solution.t[1:])) - - # time value before and after discontinuity is an epsilon away - dindex = np.searchsorted(solution.t, discontinuity) - value_before = solution.t[dindex - 1] - value_after = solution.t[dindex] - self.assertEqual( - value_before / (1 - sys.float_info.epsilon), discontinuity - ) - self.assertEqual( - value_after / (1 + sys.float_info.epsilon), discontinuity - ) - - # both solution time vectors should have same number of points - self.assertEqual(len(solution1.t), len(solution2.t)) - - # check solution - for solution in [solution1, solution2]: - np.testing.assert_array_less(solution.y[0, :-1], 1.5) - np.testing.assert_array_less(solution.y[-1, :-1], 2.5) - np.testing.assert_equal(solution.t_event[0], solution.t[-1]) - np.testing.assert_array_equal(solution.y_event[:, 0], solution.y[:, -1]) - var1_soln = np.exp(0.2 * solution.t) - y0 = np.exp(0.2 * discontinuity) - var1_soln[solution.t > discontinuity] = y0 * np.exp( - 0.1 * (solution.t[solution.t > discontinuity] - discontinuity) - ) - np.testing.assert_allclose(solution.y[0], var1_soln, rtol=1e-06) - - def test_ode_solver_fail_with_dae(self): - model = pybamm.BaseModel() - a = pybamm.Scalar(1) - model.algebraic = {a: a} - model.concatenated_initial_conditions = a - solver = pybamm.ScikitsOdeSolver() - with self.assertRaisesRegex(pybamm.SolverError, "Cannot use ODE solver"): - solver.set_up(model) - - def test_dae_solver_algebraic_model(self): - model = pybamm.BaseModel() - var = pybamm.Variable("var") - model.algebraic = {var: var + 1} - model.initial_conditions = {var: 0} - - disc = pybamm.Discretisation() - disc.process_model(model) - - solver = pybamm.ScikitsDaeSolver() - t_eval = np.linspace(0, 1) - solution = solver.solve(model, t_eval) - np.testing.assert_array_equal(solution.y, -1) - - -if __name__ == "__main__": - print("Add -v for more debug output") - if "-v" in sys.argv: - debug = True - pybamm.settings.debug_mode = True - unittest.main() diff --git a/tests/unit/test_solvers/test_solution.py b/tests/unit/test_solvers/test_solution.py index c7dfb716de..ecc8d1bd8e 100644 --- a/tests/unit/test_solvers/test_solution.py +++ b/tests/unit/test_solvers/test_solution.py @@ -232,7 +232,7 @@ def test_plot(self): solution = pybamm.ScipySolver().solve(model, np.linspace(0, 1)) - solution.plot(["c", "2c"], testing=True) + solution.plot(["c", "2c"], show_plot=False) def test_save(self): with TemporaryDirectory() as dir_name: diff --git a/tests/unit/test_spatial_methods/test_finite_volume/test_finite_volume.py b/tests/unit/test_spatial_methods/test_finite_volume/test_finite_volume.py index 16a3bbde2c..1d5844d7b0 100644 --- a/tests/unit/test_spatial_methods/test_finite_volume/test_finite_volume.py +++ b/tests/unit/test_spatial_methods/test_finite_volume/test_finite_volume.py @@ -410,11 +410,11 @@ def test_upwind_downwind(self): y_test = 2 * np.ones_like(nodes) np.testing.assert_array_equal( disc_upwind.evaluate(y=y_test), - np.concatenate([np.array([5, 0.5]), 2 * np.ones(n - 1)])[:, np.newaxis], + np.concatenate([np.array([8]), 2 * np.ones(n)])[:, np.newaxis], ) np.testing.assert_array_equal( disc_downwind.evaluate(y=y_test), - np.concatenate([2 * np.ones(n - 1), np.array([1.5, 3])])[:, np.newaxis], + np.concatenate([2 * np.ones(n), np.array([4])])[:, np.newaxis], ) # Remove boundary conditions and check error is raised @@ -575,6 +575,51 @@ def test_evaluate_at(self): y = np.arange(n)[:, np.newaxis] self.assertEqual(evaluate_at_disc.evaluate(y=y), y[idx]) + def test_inner(self): + # standard + mesh = get_mesh_for_testing() + spatial_methods = { + "macroscale": pybamm.FiniteVolume(), + "negative particle": pybamm.FiniteVolume(), + } + + var = pybamm.Variable("var", domain="negative particle") + grad_var = pybamm.grad(var) + inner = pybamm.inner(grad_var, grad_var) + + disc = pybamm.Discretisation(mesh, spatial_methods) + disc.set_variable_slices([var]) + boundary_conditions = { + var: { + "left": (pybamm.Scalar(0), "Neumann"), + "right": (pybamm.Scalar(0), "Neumann"), + } + } + disc.bcs = boundary_conditions + inner_disc = disc.process_symbol(inner) + + self.assertIsInstance(inner_disc, pybamm.Inner) + self.assertIsInstance(inner_disc.left, pybamm.MatrixMultiplication) + self.assertIsInstance(inner_disc.right, pybamm.MatrixMultiplication) + + n = mesh["negative particle"].npts + y = np.ones(n)[:, np.newaxis] + np.testing.assert_array_equal(inner_disc.evaluate(y=y), np.zeros((n, 1))) + mesh = get_mesh_for_testing() + + # with secondary broadcast + grad_var = pybamm.grad(pybamm.SecondaryBroadcast(var, "negative electrode")) + inner = pybamm.inner(grad_var, grad_var) + + inner_disc = disc.process_symbol(inner) + + self.assertIsInstance(inner_disc, pybamm.Inner) + self.assertIsInstance(inner_disc.left, pybamm.MatrixMultiplication) + self.assertIsInstance(inner_disc.right, pybamm.MatrixMultiplication) + + m = mesh["negative electrode"].npts + np.testing.assert_array_equal(inner_disc.evaluate(y=y), np.zeros((n * m, 1))) + if __name__ == "__main__": print("Add -v for more debug output") diff --git a/tests/unit/test_spatial_methods/test_finite_volume/test_ghost_nodes_and_neumann.py b/tests/unit/test_spatial_methods/test_finite_volume/test_ghost_nodes_and_neumann.py index 5651a9dd1e..47e1a2fda1 100644 --- a/tests/unit/test_spatial_methods/test_finite_volume/test_ghost_nodes_and_neumann.py +++ b/tests/unit/test_spatial_methods/test_finite_volume/test_ghost_nodes_and_neumann.py @@ -52,6 +52,8 @@ def test_add_ghost_nodes(self): bcs = {"left": (pybamm.Scalar(0), "Neumann"), "right": (pybamm.Scalar(3), "x")} with self.assertRaisesRegex(ValueError, "boundary condition must be"): sp_meth.add_ghost_nodes(var, discretised_symbol, bcs) + with self.assertRaisesRegex(ValueError, "No boundary conditions"): + sp_meth.add_ghost_nodes(var, discretised_symbol, {}) with self.assertRaisesRegex(ValueError, "boundary condition must be"): sp_meth.add_neumann_values(var, discretised_symbol, bcs, var.domain) diff --git a/tests/unit/test_spatial_methods/test_finite_volume/test_integration.py b/tests/unit/test_spatial_methods/test_finite_volume/test_integration.py index f1908b1238..57113259c1 100644 --- a/tests/unit/test_spatial_methods/test_finite_volume/test_integration.py +++ b/tests/unit/test_spatial_methods/test_finite_volume/test_integration.py @@ -339,7 +339,7 @@ def test_indefinite_integral(self): phi_exact = np.ones((submesh.npts, 1)) phi_approx = int_grad_phi_disc.evaluate(None, phi_exact) phi_approx += 1 # add constant of integration - np.testing.assert_array_equal(phi_exact, phi_approx) + np.testing.assert_array_almost_equal(phi_exact, phi_approx) self.assertEqual(left_boundary_value_disc.evaluate(y=phi_exact), 0) # linear case phi_exact = submesh.nodes[:, np.newaxis] @@ -379,7 +379,7 @@ def test_indefinite_integral(self): phi_exact = np.ones((submesh.npts, 1)) phi_approx = int_grad_phi_disc.evaluate(None, phi_exact) phi_approx += 1 # add constant of integration - np.testing.assert_array_equal(phi_exact, phi_approx) + np.testing.assert_array_almost_equal(phi_exact, phi_approx) self.assertEqual(left_boundary_value_disc.evaluate(y=phi_exact), 0) # linear case @@ -440,7 +440,7 @@ def test_indefinite_integral(self): c_exact = np.ones((submesh.npts, 1)) c_approx = c_integral_disc.evaluate(None, c_exact) c_approx += 1 # add constant of integration - np.testing.assert_array_equal(c_exact, c_approx) + np.testing.assert_array_almost_equal(c_exact, c_approx) self.assertEqual(left_boundary_value_disc.evaluate(y=c_exact), 0) # linear case @@ -488,7 +488,7 @@ def test_backward_indefinite_integral(self): phi_exact = np.ones((submesh.npts, 1)) phi_approx = int_grad_phi_disc.evaluate(None, phi_exact) phi_approx += 1 # add constant of integration - np.testing.assert_array_equal(phi_exact, phi_approx) + np.testing.assert_array_almost_equal(phi_exact, phi_approx) self.assertEqual(right_boundary_value_disc.evaluate(y=phi_exact), 0) # linear case @@ -561,7 +561,7 @@ def test_indefinite_integral_on_nodes(self): phi_exact = np.ones((submesh.npts, 1)) int_phi_exact = submesh.edges int_phi_approx = int_phi_disc.evaluate(None, phi_exact).flatten() - np.testing.assert_array_equal(int_phi_exact, int_phi_approx) + np.testing.assert_array_almost_equal(int_phi_exact, int_phi_approx) # linear case phi_exact = submesh.nodes int_phi_exact = submesh.edges**2 / 2 diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py index d0ac5337bf..d726cf1870 100644 --- a/tests/unit/test_util.py +++ b/tests/unit/test_util.py @@ -1,8 +1,5 @@ -# -# Tests the utility functions. -# +import importlib from tests import TestCase -import numpy as np import os import sys import pybamm @@ -10,9 +7,12 @@ import unittest from unittest.mock import patch from io import StringIO -from tempfile import TemporaryDirectory -anytree = sys.modules["anytree"] +from tests import ( + get_optional_distribution_deps, + get_required_distribution_deps, + get_present_optional_import_deps, +) class TestUtil(TestCase): @@ -20,19 +20,7 @@ class TestUtil(TestCase): Test the functionality in util.py """ - def test_rmse(self): - self.assertEqual(pybamm.rmse(np.ones(5), np.zeros(5)), 1) - self.assertEqual(pybamm.rmse(2 * np.ones(5), np.zeros(5)), 2) - self.assertEqual(pybamm.rmse(2 * np.ones(5), np.ones(5)), 1) - - x = np.array([1, 2, 3, 4, 5]) - self.assertEqual(pybamm.rmse(x, x), 0) - - with self.assertRaisesRegex(ValueError, "same length"): - pybamm.rmse(np.ones(5), np.zeros(3)) - def test_is_constant_and_can_evaluate(self): - sys.modules["anytree"] = anytree symbol = pybamm.PrimaryBroadcast(0, "negative electrode") self.assertEqual(False, pybamm.is_constant_and_can_evaluate(symbol)) symbol = pybamm.StateVector(slice(0, 1)) @@ -48,6 +36,12 @@ def test_fuzzy_dict(self): "SEI current": 3, "Lithium plating current": 4, "A dimensional variable [m]": 5, + "Positive particle diffusivity [m2.s-1]": 6, + } + ) + d2 = pybamm.FuzzyDict( + { + "Positive electrode diffusivity [m2.s-1]": 6, } ) self.assertEqual(d["test"], 1) @@ -69,6 +63,22 @@ def test_fuzzy_dict(self): with self.assertRaisesRegex(KeyError, "Upper voltage"): d.__getitem__("Open-circuit voltage at 100% SOC [V]") + assert ( + d2["Positive particle diffusivity [m2.s-1]"] + == d["Positive particle diffusivity [m2.s-1]"] + ) + + assert ( + d2["Positive electrode diffusivity [m2.s-1]"] + == d["Positive electrode diffusivity [m2.s-1]"] + ) + + with self.assertWarns(DeprecationWarning): + self.assertEqual( + d["Positive electrode diffusivity [m2.s-1]"], + d["Positive particle diffusivity [m2.s-1]"], + ) + def test_get_parameters_filepath(self): tempfile_obj = tempfile.NamedTemporaryFile("w", dir=".") self.assertTrue( @@ -92,28 +102,80 @@ def test_git_commit_info(self): self.assertIsInstance(git_commit_info, str) self.assertEqual(git_commit_info[:2], "v2") - def test_have_optional_dependency(self): - with self.assertRaisesRegex( - ModuleNotFoundError, "Optional dependency pybtex is not available." - ): - pybtex = sys.modules["pybtex"] - sys.modules["pybtex"] = None - pybamm.print_citations() - with self.assertRaisesRegex( - ModuleNotFoundError, "Optional dependency anytree is not available." - ): - with TemporaryDirectory() as dir_name: - sys.modules["anytree"] = None - test_stub = os.path.join(dir_name, "test_visualize") - test_name = f"{test_stub}.png" - c = pybamm.Variable("c", "negative electrode") - d = pybamm.Variable("d", "negative electrode") - sym = pybamm.div(c * pybamm.grad(c)) + (c / d + c - d) ** 5 - sym.visualise(test_name) - - sys.modules["pybtex"] = pybtex - pybamm.util.have_optional_dependency("pybtex") - pybamm.print_citations() + def test_import_optional_dependency(self): + optional_distribution_deps = get_optional_distribution_deps("pybamm") + present_optional_import_deps = get_present_optional_import_deps( + "pybamm", optional_distribution_deps=optional_distribution_deps + ) + + # Save optional dependencies, then make them not importable + modules = {} + for import_pkg in present_optional_import_deps: + modules[import_pkg] = sys.modules.get(import_pkg) + sys.modules[import_pkg] = None + + # Test import optional dependency + for import_pkg in present_optional_import_deps: + with self.assertRaisesRegex( + ModuleNotFoundError, + f"Optional dependency {import_pkg} is not available.", + ): + pybamm.util.import_optional_dependency(import_pkg) + + # Restore optional dependencies + for import_pkg in present_optional_import_deps: + sys.modules[import_pkg] = modules[import_pkg] + + def test_pybamm_import(self): + optional_distribution_deps = get_optional_distribution_deps("pybamm") + present_optional_import_deps = get_present_optional_import_deps( + "pybamm", optional_distribution_deps=optional_distribution_deps + ) + + # Save optional dependencies and their sub-modules, then make them not importable + modules = {} + for module_name, module in sys.modules.items(): + base_module_name = module_name.split(".")[0] + if base_module_name in present_optional_import_deps: + modules[module_name] = module + sys.modules[module_name] = None + + # Unload pybamm and its sub-modules + for module_name in list(sys.modules.keys()): + base_module_name = module_name.split(".")[0] + if base_module_name == "pybamm": + sys.modules.pop(module_name) + + # Test pybamm is still importable + try: + importlib.import_module("pybamm") + except ModuleNotFoundError as error: + self.fail( + f"Import of 'pybamm' shouldn't require optional dependencies. Error: {error}" + ) + finally: + # Restore optional dependencies and their sub-modules + for module_name, module in modules.items(): + sys.modules[module_name] = module + + def test_optional_dependencies(self): + optional_distribution_deps = get_optional_distribution_deps("pybamm") + required_distribution_deps = get_required_distribution_deps("pybamm") + + # Get nested required dependencies + for distribution_dep in list(required_distribution_deps): + required_distribution_deps.update( + get_required_distribution_deps(distribution_dep) + ) + + # Check that optional dependencies are not present in the core PyBaMM installation + optional_present_deps = optional_distribution_deps & required_distribution_deps + self.assertFalse( + bool(optional_present_deps), + f"Optional dependencies installed: {optional_present_deps}.\n" + "Please ensure that optional dependencies are not present in the core PyBaMM installation, " + "or list them as required.", + ) class TestSearch(TestCase): diff --git a/vcpkg-configuration.json b/vcpkg-configuration.json index d97bc3c617..11505be46e 100644 --- a/vcpkg-configuration.json +++ b/vcpkg-configuration.json @@ -13,7 +13,7 @@ { "kind": "git", "repository": "https://github.com/pybamm-team/casadi-vcpkg-registry.git", - "baseline": "baa26c2e629ea18fbb1aefa7d27c6612c4068fa7", + "baseline": "ceee3ed50246744cdef43517d7d7617b8ac291e7", "packages": ["casadi"] } ] diff --git a/vcpkg.json b/vcpkg.json index 23cdcb3f58..a4ae73f302 100644 --- a/vcpkg.json +++ b/vcpkg.json @@ -1,6 +1,6 @@ { "name": "pybamm", - "version-string": "24.1", + "version-string": "24.5", "dependencies": [ "casadi", {