From bd4037cbfab6c9a46f4e02dd18b01b55edf6a629 Mon Sep 17 00:00:00 2001 From: Akhilender Date: Wed, 4 Oct 2023 23:51:23 +0530 Subject: [PATCH 01/75] fix: Implement Automatic Activation of Virtual Environment in the 'dev' Nox Session - Implement automatic activation of the virtual environment in the 'dev' Nox session - Enhance the 'dev' Nox session defined in `noxfile.py` - Simplify the development setup process for contributors and developers This commit enhances the 'dev' Nox session to automatically activate the virtual environment when running 'nox -s dev'. Signed-off-by: Akhilender --- noxfile.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/noxfile.py b/noxfile.py index c215c73ca5..469dbb60d6 100644 --- a/noxfile.py +++ b/noxfile.py @@ -1,6 +1,7 @@ import nox import os import sys +import pathlib # Options to modify nox behaviour @@ -117,7 +118,11 @@ def set_dev(session): """Install PyBaMM in editable mode.""" set_environment_variables(PYBAMM_ENV, session=session) envbindir = session.bin - session.install("-e", ".[all]", silent=False) + venv_dir = pathlib.Path('./venv').resolve() + session.install("virtualenv") + session.run("virtualenv", os.fsdecode(venv_dir), silent=True) + python = os.fsdecode(venv_dir.joinpath("bin/python")) + session.run(python, "-m", "pip", "install", "-e", ".[all]", silent=False) session.install("cmake", silent=False) if sys.platform == "linux" or sys.platform == "darwin": session.run( From 42c6c556385db6e14cc9a37928443cd4ca4cf7bb Mon Sep 17 00:00:00 2001 From: Akhilender Date: Thu, 5 Oct 2023 23:55:16 +0530 Subject: [PATCH 02/75] refactor: Improve noxfile.py setup and install [jax,odes] - Imported `Path` directly from `pathlib` for better readability. - Moved the definition of `venv_dir` closer to `PYBAMM_ENV` and marked it as a constant. - Installed development dependencies using `-e .[dev]` to set up the developer environment and silenced the warning with `external=True`. - Added the installation of `[jax,odes]` extras for macOS and Linux platforms. Signed-off-by: Akhilender --- noxfile.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/noxfile.py b/noxfile.py index 469dbb60d6..ccbbc0d749 100644 --- a/noxfile.py +++ b/noxfile.py @@ -1,7 +1,7 @@ import nox import os import sys -import pathlib +from pathlib import Path # Options to modify nox behaviour @@ -17,6 +17,7 @@ "SUNDIALS_INST": f"{homedir}/.local", "LD_LIBRARY_PATH": f"{homedir}/.local/lib:", } +VENV_DIR = Path('./venv').resolve() def set_environment_variables(env_dict, session): @@ -118,12 +119,14 @@ def set_dev(session): """Install PyBaMM in editable mode.""" set_environment_variables(PYBAMM_ENV, session=session) envbindir = session.bin - venv_dir = pathlib.Path('./venv').resolve() session.install("virtualenv") - session.run("virtualenv", os.fsdecode(venv_dir), silent=True) - python = os.fsdecode(venv_dir.joinpath("bin/python")) + session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True) + python = os.fsdecode(VENV_DIR.joinpath("bin/python")) session.run(python, "-m", "pip", "install", "-e", ".[all]", silent=False) session.install("cmake", silent=False) + session.install("-e", ".[dev]", external=True) + if session.platform.startswith("macos") or session.platform.startswith("linux"): + session.run(python, "-m", "pip", "install", ".[jax,odes]", silent=False) if sys.platform == "linux" or sys.platform == "darwin": session.run( "echo", From 713da15376285851f11a89afb4ec33664bd80b3a Mon Sep 17 00:00:00 2001 From: Akhilender Date: Fri, 6 Oct 2023 15:20:58 +0530 Subject: [PATCH 03/75] refactor: Improve noxfile.py - Installed `virtualenv` and `cmake` before other dependencies. - Installed all and dev dependencies together using a single `pip` command to avoid redundant wheel building. - Corrected sys.platform to maintain consistency in the code Signed-off-by: Akhilender --- noxfile.py | 19 ++++--------------- 1 file changed, 4 insertions(+), 15 deletions(-) diff --git a/noxfile.py b/noxfile.py index ccbbc0d749..df00bfadc2 100644 --- a/noxfile.py +++ b/noxfile.py @@ -118,24 +118,13 @@ def run_scripts(session): def set_dev(session): """Install PyBaMM in editable mode.""" set_environment_variables(PYBAMM_ENV, session=session) - envbindir = session.bin - session.install("virtualenv") + session.install("virtualenv","cmake") session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True) python = os.fsdecode(VENV_DIR.joinpath("bin/python")) - session.run(python, "-m", "pip", "install", "-e", ".[all]", silent=False) - session.install("cmake", silent=False) - session.install("-e", ".[dev]", external=True) - if session.platform.startswith("macos") or session.platform.startswith("linux"): + session.run(python, "-m", "pip", "install", "-e", ".[all,dev]", silent=False) + if sys.platform == "linux" or sys.platform == "macos": session.run(python, "-m", "pip", "install", ".[jax,odes]", silent=False) - if sys.platform == "linux" or sys.platform == "darwin": - session.run( - "echo", - "export", - f"LD_LIBRARY_PATH={PYBAMM_ENV['LD_LIBRARY_PATH']}", - ">>", - f"{envbindir}/activate", - external=True, # silence warning about echo being an external command - ) + @nox.session(name="tests") From eae0bbb3bbcd974a1505404a1023b686d8f5b1ad Mon Sep 17 00:00:00 2001 From: Akhilender Bongirwar <112749383+akhilender-bongirwar@users.noreply.github.com> Date: Sun, 8 Oct 2023 14:16:03 +0530 Subject: [PATCH 04/75] fix: Resolved indentaions - Corrected the space between virtualenv and cmake - Changed macos to darwin --- noxfile.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/noxfile.py b/noxfile.py index df00bfadc2..736c5be6c2 100644 --- a/noxfile.py +++ b/noxfile.py @@ -118,11 +118,11 @@ def run_scripts(session): def set_dev(session): """Install PyBaMM in editable mode.""" set_environment_variables(PYBAMM_ENV, session=session) - session.install("virtualenv","cmake") + session.install("virtualenv", "cmake") session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True) python = os.fsdecode(VENV_DIR.joinpath("bin/python")) session.run(python, "-m", "pip", "install", "-e", ".[all,dev]", silent=False) - if sys.platform == "linux" or sys.platform == "macos": + if sys.platform == "linux" or sys.platform == "darwin": session.run(python, "-m", "pip", "install", ".[jax,odes]", silent=False) From 570b96f7315ceb3ded9f3595c03ec8e2c996bc8d Mon Sep 17 00:00:00 2001 From: Akhilender Bongirwar <112749383+akhilender-bongirwar@users.noreply.github.com> Date: Sun, 8 Oct 2023 14:21:52 +0530 Subject: [PATCH 05/75] Updated install-from-source.rst - Updated the docs regarding the virtual environment --- docs/source/user_guide/installation/install-from-source.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/source/user_guide/installation/install-from-source.rst b/docs/source/user_guide/installation/install-from-source.rst index 0aa50cf8a8..fb448950bf 100644 --- a/docs/source/user_guide/installation/install-from-source.rst +++ b/docs/source/user_guide/installation/install-from-source.rst @@ -116,7 +116,7 @@ Using Nox (recommended) .. note:: It is recommended to use ``--verbose`` or ``-v`` to see outputs of all commands run. -This creates a virtual environment ``.nox/dev`` inside the ``PyBaMM/`` directory. +This creates a virtual environment ``venv/`` inside the ``PyBaMM/`` directory. It comes ready with PyBaMM and some useful development tools like `pre-commit `_ and `ruff `_. You can now activate the environment with @@ -125,13 +125,13 @@ You can now activate the environment with .. code:: bash - source .nox/dev/bin/activate + source venv/bin/activate .. tab:: Windows .. code:: bash - .nox\dev\Scripts\activate.bat + venv\Scripts\activate.bat and run the tests to check your installation. From 94753958dd97888bc964e8b9d4d1066cfa098bd5 Mon Sep 17 00:00:00 2001 From: Akhilender Bongirwar <112749383+akhilender-bongirwar@users.noreply.github.com> Date: Sun, 8 Oct 2023 16:27:54 +0530 Subject: [PATCH 06/75] fix: Changed few minor instances - Added external = True instead of silent = False as it is not required for session.run() --- noxfile.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/noxfile.py b/noxfile.py index 736c5be6c2..9d13656894 100644 --- a/noxfile.py +++ b/noxfile.py @@ -121,9 +121,9 @@ def set_dev(session): session.install("virtualenv", "cmake") session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True) python = os.fsdecode(VENV_DIR.joinpath("bin/python")) - session.run(python, "-m", "pip", "install", "-e", ".[all,dev]", silent=False) + session.run(python, "-m", "pip", "install", "-e", ".[all,dev]", external=True) if sys.platform == "linux" or sys.platform == "darwin": - session.run(python, "-m", "pip", "install", ".[jax,odes]", silent=False) + session.run(python, "-m", "pip", "install", ".[jax,odes]", external=True) From 342c70312bad7780fd9b4c28b4983d2dc0ccc417 Mon Sep 17 00:00:00 2001 From: John Brittain Date: Mon, 9 Oct 2023 13:12:22 +0100 Subject: [PATCH 07/75] Fix for jax-gpu solver --- pybamm/solvers/jax_solver.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pybamm/solvers/jax_solver.py b/pybamm/solvers/jax_solver.py index 8e7b1b5cc5..beeb932597 100644 --- a/pybamm/solvers/jax_solver.py +++ b/pybamm/solvers/jax_solver.py @@ -215,7 +215,7 @@ def _integrate(self, model, t_eval, inputs=None): y = [] platform = jax.lib.xla_bridge.get_backend().platform.casefold() - if platform.startswith("cpu"): + if len(inputs) <= 1 or platform.startswith("cpu"): # cpu execution runs faster when multithreaded async def solve_model_for_inputs(): async def solve_model_async(inputs_v): From 9748a1b9957ef955839e7517785881b97ca3a3c2 Mon Sep 17 00:00:00 2001 From: John Brittain Date: Mon, 9 Oct 2023 16:36:01 +0100 Subject: [PATCH 08/75] Add changelog --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 421d3bfa29..17a54d41a8 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -18,6 +18,7 @@ ## Bug fixes +- Fixed a bug where the JaxSolver would fails when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423)) - Fixed a bug where empty lists passed to QuickPlot resulted in an IndexError and did not return a meaningful error message ([#3359](https://github.com/pybamm-team/PyBaMM/pull/3359)) - Fixed a bug where there was a missing thermal conductivity in the thermal pouch cell models ([#3330](https://github.com/pybamm-team/PyBaMM/pull/3330)) - Fixed a bug that caused incorrect results of “{Domain} electrode thickness change [m]” due to the absence of dimension for the variable `electrode_thickness_change`([#3329](https://github.com/pybamm-team/PyBaMM/pull/3329)). From 9fa63c320a874b7910003594a7bf93c49a1881ab Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Tue, 10 Oct 2023 03:09:34 +0530 Subject: [PATCH 09/75] Set up parallel build and push with matrices and tags #3316 fix discrepancy with tagging the images, add a step to append tags to GitHub output, add support for multi-architecture images by setting up QEMU. --- .github/workflows/docker.yml | 83 ++++++++++++++++-------------------- 1 file changed, 36 insertions(+), 47 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index c3575a50f8..fc7e52ec36 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -1,75 +1,64 @@ -name: Build & Push Docker Images +name: Build and push Docker images to Docker Hub on: workflow_dispatch: push: branches: - develop + # temporary + pull_request: jobs: - pre_job: + build_docker_images: + # This workflow is only of value to PyBaMM and would always be skipped in forks + if: github.repository_owner == 'pybamm-team' + name: Build image ({{ matrix.build-args }} / {{ matrix.architectures }}) runs-on: ubuntu-latest + strategy: + matrix: + build-args: ["", "JAX=true", "ODES=true", "IDAKLU=true", "ALL=true"] + architectures: [amd64, arm64] steps: - name: Checkout uses: actions/checkout@v4 + - name: Set up QEMU + uses: docker/setup-qemu-action@v3 + - name: Set up Docker Buildx uses: docker/setup-buildx-action@v3 - - name: Login to DockerHub + - name: Login to Docker Hub uses: docker/login-action@v3 with: username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_TOKEN }} - - name: List built images - run: docker images - - - name: Build and Push Docker Image (Without Solvers) - uses: docker/build-push-action@v5 - with: - context: . - file: scripts/Dockerfile - tags: pybamm/pybamm:latest - push: true - - - name: Build and Push Docker Image (With JAX Solver) - uses: docker/build-push-action@v5 - with: - context: . - file: scripts/Dockerfile - tags: pybamm/pybamm:jax - push: true - build-args: | - JAX=true - - - name: Build and Push Docker Image (With ODES & DAE Solver) - uses: docker/build-push-action@v5 - with: - context: . - file: scripts/Dockerfile - tags: pybamm/pybamm:odes - push: true - build-args: | - ODES=true + - name: Create tags for Docker images based on build-time arguments + id: tags + run: | + if [ "${{ matrix.build-args }}" = "" ]; then + echo "::set-output name=tag::latest" >> $GITHUB_OUTPUT + elif [ "${{ matrix.build-args }}" = "JAX=true" ]; then + echo "::set-output name=tag::jax" >> $GITHUB_OUTPUT + elif [ "${{ matrix.build-args }}" = "ODES=true" ]; then + echo "::set-output name=tag::odes" >> $GITHUB_OUTPUT + elif [ "${{ matrix.build-args }}" = "IDAKLU=true" ]; then + echo "::set-output name=tag::idaklu" >> $GITHUB_OUTPUT + elif [ "${{ matrix.build-args }}" = "ALL=true" ]; then + echo "::set-output name=tag::all" >> $GITHUB_OUTPUT + fi - - name: Build and Push Docker Image (With IDAKLU Solver) + - name: Build and push Docker image to Docker Hub uses: docker/build-push-action@v5 with: context: . file: scripts/Dockerfile - tags: pybamm/pybamm:idaklu - push: true - build-args: | - IDAKLU=true + tags: pybamm/pybamm:${{ steps.tags.outputs.tag }} + push: false # temporary + build-args: ${{ matrix.build-args }} + platforms: linux/${{ matrix.architectures }} - - name: Build and Push Docker Image (With All Solvers) - uses: docker/build-push-action@v5 - with: - context: . - file: scripts/Dockerfile - tags: pybamm/pybamm:latest - push: true - build-args: | - ALL=true + - name: List built image(s) + run: docker images From 14ffa0236f752f72fae5e3a61ef8f0f6a53dd8fc Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Tue, 10 Oct 2023 03:15:22 +0530 Subject: [PATCH 10/75] Don't add architectures to the matrix --- .github/workflows/docker.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index fc7e52ec36..e69492e3d6 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -17,7 +17,6 @@ jobs: strategy: matrix: build-args: ["", "JAX=true", "ODES=true", "IDAKLU=true", "ALL=true"] - architectures: [amd64, arm64] steps: - name: Checkout @@ -58,7 +57,7 @@ jobs: tags: pybamm/pybamm:${{ steps.tags.outputs.tag }} push: false # temporary build-args: ${{ matrix.build-args }} - platforms: linux/${{ matrix.architectures }} + platforms: linux/amd64, linux/arm64 - name: List built image(s) run: docker images From 192dc9dbd081cc94a0b2da84aaa12f0854051509 Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Tue, 10 Oct 2023 03:17:07 +0530 Subject: [PATCH 11/75] Run on forks, temporarily --- .github/workflows/docker.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index e69492e3d6..c39befe5af 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -11,7 +11,7 @@ on: jobs: build_docker_images: # This workflow is only of value to PyBaMM and would always be skipped in forks - if: github.repository_owner == 'pybamm-team' + # if: github.repository_owner == 'pybamm-team' name: Build image ({{ matrix.build-args }} / {{ matrix.architectures }}) runs-on: ubuntu-latest strategy: From 7cb7b20538bcc4d599f470ef0c5bb8ea373fd66f Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Tue, 10 Oct 2023 03:18:26 +0530 Subject: [PATCH 12/75] Fix name scheme for build job --- .github/workflows/docker.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index c39befe5af..47388e3830 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -12,7 +12,7 @@ jobs: build_docker_images: # This workflow is only of value to PyBaMM and would always be skipped in forks # if: github.repository_owner == 'pybamm-team' - name: Build image ({{ matrix.build-args }} / {{ matrix.architectures }}) + name: Build image ({{ matrix.build-args }}) runs-on: ubuntu-latest strategy: matrix: From b9db45c37c5e118b2fdc171ea09c952a92ed11f6 Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Tue, 10 Oct 2023 03:22:03 +0530 Subject: [PATCH 13/75] Enable fail fast to not push any broken images --- .github/workflows/docker.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 47388e3830..e45104b2fc 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -17,6 +17,7 @@ jobs: strategy: matrix: build-args: ["", "JAX=true", "ODES=true", "IDAKLU=true", "ALL=true"] + fail-fast: true steps: - name: Checkout From a3d561a25e286c6d91345259cb5239eca593e310 Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Tue, 10 Oct 2023 03:49:33 +0530 Subject: [PATCH 14/75] Properly set tags into GitHub step outputs --- .github/workflows/docker.yml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index e45104b2fc..8031d86203 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -39,15 +39,15 @@ jobs: id: tags run: | if [ "${{ matrix.build-args }}" = "" ]; then - echo "::set-output name=tag::latest" >> $GITHUB_OUTPUT + echo "tag=latest" >> "$GITHUB_OUTPUT" elif [ "${{ matrix.build-args }}" = "JAX=true" ]; then - echo "::set-output name=tag::jax" >> $GITHUB_OUTPUT + echo "tag=jax" >> "$GITHUB_OUTPUT" elif [ "${{ matrix.build-args }}" = "ODES=true" ]; then - echo "::set-output name=tag::odes" >> $GITHUB_OUTPUT + echo "tag=odes" >> "$GITHUB_OUTPUT" elif [ "${{ matrix.build-args }}" = "IDAKLU=true" ]; then - echo "::set-output name=tag::idaklu" >> $GITHUB_OUTPUT + echo "tag=idaklu" >> "$GITHUB_OUTPUT" elif [ "${{ matrix.build-args }}" = "ALL=true" ]; then - echo "::set-output name=tag::all" >> $GITHUB_OUTPUT + echo "tag=all" >> "$GITHUB_OUTPUT" fi - name: Build and push Docker image to Docker Hub From ec52a9ff1a4209359aad51173dc63185c02c1951 Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Tue, 10 Oct 2023 03:51:47 +0530 Subject: [PATCH 15/75] Rename job step name to include build args --- .github/workflows/docker.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 8031d86203..1f50801a0e 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -12,7 +12,7 @@ jobs: build_docker_images: # This workflow is only of value to PyBaMM and would always be skipped in forks # if: github.repository_owner == 'pybamm-team' - name: Build image ({{ matrix.build-args }}) + name: Build image (${{ matrix.build-args }}) runs-on: ubuntu-latest strategy: matrix: From 6506dfc066a68b1e2cfcf0a920b24f016a483c4b Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Tue, 10 Oct 2023 04:13:30 +0530 Subject: [PATCH 16/75] Do not fail fast until `Jax` issue is resolved --- .github/workflows/docker.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 1f50801a0e..28d958dadb 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -17,7 +17,7 @@ jobs: strategy: matrix: build-args: ["", "JAX=true", "ODES=true", "IDAKLU=true", "ALL=true"] - fail-fast: true + fail-fast: false steps: - name: Checkout From 96a74a4eeb9106979415fa0b9051afb7ca611f5c Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Tue, 10 Oct 2023 05:31:55 +0530 Subject: [PATCH 17/75] Clean up push and remove PR builds --- .github/workflows/docker.yml | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 28d958dadb..82bc0006fe 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -5,8 +5,6 @@ on: push: branches: - develop - # temporary - pull_request: jobs: build_docker_images: @@ -17,7 +15,7 @@ jobs: strategy: matrix: build-args: ["", "JAX=true", "ODES=true", "IDAKLU=true", "ALL=true"] - fail-fast: false + fail-fast: true steps: - name: Checkout @@ -56,7 +54,7 @@ jobs: context: . file: scripts/Dockerfile tags: pybamm/pybamm:${{ steps.tags.outputs.tag }} - push: false # temporary + push: true build-args: ${{ matrix.build-args }} platforms: linux/amd64, linux/arm64 From 34b7288173744922d161bab9b23aa15e8938739b Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Tue, 10 Oct 2023 18:52:50 +0530 Subject: [PATCH 18/75] Add note about ALL and JAX for now, fix arguments, prettify matrix job name --- .github/workflows/docker.yml | 42 ++++++++++++++++++++++++++++-------- 1 file changed, 33 insertions(+), 9 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 82bc0006fe..9c16f95705 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -10,11 +10,11 @@ jobs: build_docker_images: # This workflow is only of value to PyBaMM and would always be skipped in forks # if: github.repository_owner == 'pybamm-team' - name: Build image (${{ matrix.build-args }}) + name: Image (${{ matrix.build-args }}) runs-on: ubuntu-latest strategy: matrix: - build-args: ["", "JAX=true", "ODES=true", "IDAKLU=true", "ALL=true"] + build-args: ["No solvers", "JAX", "ODES", "IDAKLU", "ALL"] fail-fast: true steps: @@ -36,27 +36,51 @@ jobs: - name: Create tags for Docker images based on build-time arguments id: tags run: | - if [ "${{ matrix.build-args }}" = "" ]; then + if [ "${{ matrix.build-args }}" = "No solvers" ]; then echo "tag=latest" >> "$GITHUB_OUTPUT" - elif [ "${{ matrix.build-args }}" = "JAX=true" ]; then + elif [ "${{ matrix.build-args }}" = "JAX" ]; then echo "tag=jax" >> "$GITHUB_OUTPUT" - elif [ "${{ matrix.build-args }}" = "ODES=true" ]; then + elif [ "${{ matrix.build-args }}" = "ODES" ]; then echo "tag=odes" >> "$GITHUB_OUTPUT" - elif [ "${{ matrix.build-args }}" = "IDAKLU=true" ]; then + elif [ "${{ matrix.build-args }}" = "IDAKLU" ]; then echo "tag=idaklu" >> "$GITHUB_OUTPUT" - elif [ "${{ matrix.build-args }}" = "ALL=true" ]; then + elif [ "${{ matrix.build-args }}" = "ALL" ]; then echo "tag=all" >> "$GITHUB_OUTPUT" fi - - name: Build and push Docker image to Docker Hub + - name: Build and push Docker image to Docker Hub (no solvers) + if: matrix.build-args == 'No solvers' uses: docker/build-push-action@v5 with: context: . file: scripts/Dockerfile tags: pybamm/pybamm:${{ steps.tags.outputs.tag }} push: true - build-args: ${{ matrix.build-args }} platforms: linux/amd64, linux/arm64 + - name: Build and push Docker image to Docker Hub (with ODES and IDAKLU solvers) + if: matrix.build-args == 'ODES' || matrix.build-args == 'IDAKLU' + uses: docker/build-push-action@v5 + with: + context: . + file: scripts/Dockerfile + tags: pybamm/pybamm:${{ steps.tags.outputs.tag }} + push: true + build-args: ${{ matrix.build-args }}=true + platforms: linux/amd64, linux/arm64 + + - name: Build and push Docker image to Docker Hub (with ALL and JAX solvers) + if: matrix.build-args == 'ALL' || matrix.build-args == 'JAX' + uses: docker/build-push-action@v5 + with: + context: . + file: scripts/Dockerfile + tags: pybamm/pybamm:${{ steps.tags.outputs.tag }} + push: true + build-args: ${{ matrix.build-args }}=true + # exclude arm64 for JAX and ALL builds for now, see + # https://github.com/google/jax/issues/13608 + platforms: linux/amd64 + - name: List built image(s) run: docker images From 35ac761862b44bd3c31b24544fad2ace42e79310 Mon Sep 17 00:00:00 2001 From: martinjrobins Date: Tue, 10 Oct 2023 15:14:03 +0100 Subject: [PATCH 19/75] #3431 update sundials to 6.0.0 for windows wheels --- vcpkg-configuration.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vcpkg-configuration.json b/vcpkg-configuration.json index 27c1b0bcb1..1fe14cdd44 100644 --- a/vcpkg-configuration.json +++ b/vcpkg-configuration.json @@ -7,7 +7,7 @@ { "kind": "git", "repository": "https://github.com/pybamm-team/sundials-vcpkg-registry.git", - "baseline": "2aaffb6bba7bc0b50cb74ddad636832d673851a1", + "baseline": "5a6a8c4daf1e898809a19e60ea6e6cece85fe08e", "packages": ["sundials"] }, { From 00d661167d422458c05f1b82a8174c0c04924e3e Mon Sep 17 00:00:00 2001 From: martinjrobins Date: Tue, 10 Oct 2023 15:38:17 +0000 Subject: [PATCH 20/75] #3431 update windows wheels to sundials 6.5.0 --- vcpkg-configuration.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vcpkg-configuration.json b/vcpkg-configuration.json index 1fe14cdd44..1d728d704d 100644 --- a/vcpkg-configuration.json +++ b/vcpkg-configuration.json @@ -7,7 +7,7 @@ { "kind": "git", "repository": "https://github.com/pybamm-team/sundials-vcpkg-registry.git", - "baseline": "5a6a8c4daf1e898809a19e60ea6e6cece85fe08e", + "baseline": "7d19be6c0713589465e2eb67ef3f5ffbf9f5d5c1", "packages": ["sundials"] }, { From 6f1531be842022802901d08c8c39aaadf3186c85 Mon Sep 17 00:00:00 2001 From: "allcontributors[bot]" <46447321+allcontributors[bot]@users.noreply.github.com> Date: Tue, 10 Oct 2023 15:51:19 +0000 Subject: [PATCH 21/75] docs: update README.md [skip ci] --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 1354f3f81f..c7ca111873 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-65-orange.svg)](#-contributors) +[![All Contributors](https://img.shields.io/badge/all_contributors-66-orange.svg)](#-contributors) @@ -269,6 +269,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Andrés Ignacio Torres
Andrés Ignacio Torres
🚇 Agnik Bakshi
Agnik Bakshi
📖 + RuiheLi
RuiheLi
💻 ⚠️ From 19e8256587d9009ac2fa1f3d87dcf58ef8f2e710 Mon Sep 17 00:00:00 2001 From: "allcontributors[bot]" <46447321+allcontributors[bot]@users.noreply.github.com> Date: Tue, 10 Oct 2023 15:51:20 +0000 Subject: [PATCH 22/75] docs: update .all-contributorsrc [skip ci] --- .all-contributorsrc | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/.all-contributorsrc b/.all-contributorsrc index d9f4ec5f7f..5cbd6f5ebd 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -717,6 +717,16 @@ "contributions": [ "doc" ] + }, + { + "login": "RuiheLi", + "name": "RuiheLi", + "avatar_url": "https://avatars.githubusercontent.com/u/84007676?v=4", + "profile": "https://github.com/RuiheLi", + "contributions": [ + "code", + "test" + ] } ], "contributorsPerLine": 7, From e1585f79daf891ca420cd3ab5b2fec645166610b Mon Sep 17 00:00:00 2001 From: martinjrobins Date: Tue, 10 Oct 2023 16:11:34 +0000 Subject: [PATCH 23/75] update windows wheels to sundials 6.5.0 --- vcpkg-configuration.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vcpkg-configuration.json b/vcpkg-configuration.json index 1d728d704d..8ab4e738fc 100644 --- a/vcpkg-configuration.json +++ b/vcpkg-configuration.json @@ -7,7 +7,7 @@ { "kind": "git", "repository": "https://github.com/pybamm-team/sundials-vcpkg-registry.git", - "baseline": "7d19be6c0713589465e2eb67ef3f5ffbf9f5d5c1", + "baseline": "af9f5e4bc730bf2361c47f809dcfb733e7951faa", "packages": ["sundials"] }, { From ad703bf74fb460e4a733ddf059322ec61018ef02 Mon Sep 17 00:00:00 2001 From: kratman Date: Tue, 10 Oct 2023 14:12:59 -0400 Subject: [PATCH 24/75] Removing outputs --- ...olution-data-and-processed-variables.ipynb | 1025 +++++++++-------- 1 file changed, 523 insertions(+), 502 deletions(-) diff --git a/docs/source/examples/notebooks/solution-data-and-processed-variables.ipynb b/docs/source/examples/notebooks/solution-data-and-processed-variables.ipynb index 87e5e44730..2849fca58c 100644 --- a/docs/source/examples/notebooks/solution-data-and-processed-variables.ipynb +++ b/docs/source/examples/notebooks/solution-data-and-processed-variables.ipynb @@ -1,536 +1,557 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# A look at solution data and processed variables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once you have run a simulation the first thing you want to do is have a look at the data. Most of the examples so far have made use of PyBaMM's handy QuickPlot function but there are other ways to access the data and this notebook will explore them. First off we will generate a standard SPMe model and use QuickPlot to view the default variables." - ] - }, - { - "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" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9ad1d544145646a3ae6c71a74f1dd41d", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=3510.0, step=35.1), Output()), _dom_classes=…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "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 matplotlib.pyplot as plt\n", - "os.chdir(pybamm.__path__[0]+'/..')\n", - "\n", - "# load model\n", - "model = pybamm.lithium_ion.SPMe()\n", - "\n", - "# set up and solve simulation\n", - "simulation = pybamm.Simulation(model)\n", - "dt = 90\n", - "t_eval = np.arange(0, 3600, dt) # time in seconds\n", - "solution = simulation.solve(t_eval)\n", - "\n", - "quick_plot = pybamm.QuickPlot(solution)\n", - "quick_plot.dynamic_plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Behind the scenes the QuickPlot classed has created some processed variables which can interpolate the model variables for our solution and has also stored the results for the solution steps" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['Negative particle surface concentration [mol.m-3]', 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Current [A]', 'Negative electrode potential [V]', 'Electrolyte potential [V]', 'Positive electrode potential [V]', 'Voltage [V]'])" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solution.data.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(20, 40)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solution.data['Negative particle surface concentration [mol.m-3]'].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(40,)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solution.t.shape" - ] - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A look at solution data and processed variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you have run a simulation the first thing you want to do is have a look at the data. Most of the examples so far have made use of PyBaMM's handy QuickPlot function but there are other ways to access the data and this notebook will explore them. First off we will generate a standard SPMe model and use QuickPlot to view the default variables." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice that the dictionary keys are in the same order as the subplots in the QuickPlot figure. We can add new processed variables to the solution by simply using it like a dictionary. First let's find a few more variables to look at. As you will see there are quite a few:" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] }, { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "tags": [ - "outputPrepend" - ] + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9ad1d544145646a3ae6c71a74f1dd41d", + "version_major": 2, + "version_minor": 0 }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Ambient temperature', 'Ambient temperature [K]', 'Average negative particle concentration', 'Average negative particle concentration [mol.m-3]', 'Average positive particle concentration', 'Average positive particle concentration [mol.m-3]', 'Battery voltage [V]', 'C-rate', 'Cell temperature', 'Cell temperature [K]', 'Change in measured open-circuit voltage', 'Change in measured open-circuit voltage [V]', 'Current [A]', 'Current collector current density', 'Current collector current density [A.m-2]', 'Discharge capacity [A.h]', 'Electrode current density', 'Electrode tortuosity', 'Electrolyte concentration', 'Electrolyte concentration [Molar]', 'Electrolyte concentration [mol.m-3]', 'Electrolyte current density', 'Electrolyte current density [A.m-2]', 'Electrolyte flux', 'Electrolyte flux [mol.m-2.s-1]', 'Electrolyte potential', 'Electrolyte potential [V]', 'Electrolyte tortuosity', 'Exchange current density', 'Exchange current density [A.m-2]', 'Exchange current density per volume [A.m-3]', 'Gradient of electrolyte potential', 'Gradient of negative electrode potential', 'Gradient of negative electrolyte potential', 'Gradient of positive electrode potential', 'Gradient of positive electrolyte potential', 'Gradient of separator electrolyte potential', 'Inner SEI concentration [mol.m-3]', 'Inner SEI interfacial current density', 'Inner SEI interfacial current density [A.m-2]', 'Inner SEI thickness', 'Inner SEI thickness [m]', 'Inner positive electrode SEI concentration [mol.m-3]', 'Inner positive electrode SEI interfacial current density', 'Inner positive electrode SEI interfacial current density [A.m-2]', 'Inner positive electrode SEI thickness', 'Inner positive electrode SEI thickness [m]', 'Interfacial current density', 'Interfacial current density [A.m-2]', 'Interfacial current density per volume [A.m-3]', 'Irreversible electrochemical heating', 'Irreversible electrochemical heating [W.m-3]', 'Leading-order current collector current density', 'Leading-order electrode tortuosity', 'Leading-order electrolyte tortuosity', 'Leading-order negative electrode porosity', 'Leading-order negative electrode tortuosity', 'Leading-order negative electrolyte tortuosity', 'Leading-order porosity', 'Leading-order positive electrode porosity', 'Leading-order positive electrode tortuosity', 'Leading-order positive electrolyte tortuosity', 'Leading-order separator porosity', 'Leading-order separator tortuosity', 'Leading-order x-averaged negative electrode porosity', 'Leading-order x-averaged negative electrode porosity change', 'Leading-order x-averaged negative electrode tortuosity', 'Leading-order x-averaged negative electrolyte tortuosity', 'Leading-order x-averaged positive electrode porosity', 'Leading-order x-averaged positive electrode porosity change', 'Leading-order x-averaged positive electrode tortuosity', 'Leading-order x-averaged positive electrolyte tortuosity', 'Leading-order x-averaged separator porosity', 'Leading-order x-averaged separator porosity change', 'Leading-order x-averaged separator tortuosity', 'Local ECM resistance', 'Local ECM resistance [Ohm]', 'Local voltage', 'Local voltage [V]', 'Loss of lithium to SEI [mol]', 'Loss of lithium to positive electrode SEI [mol]', 'Maximum negative particle concentration', 'Maximum negative particle concentration [mol.m-3]', 'Maximum negative particle surface concentration', 'Maximum negative particle surface concentration [mol.m-3]', 'Maximum positive particle concentration', 'Maximum positive particle concentration [mol.m-3]', 'Maximum positive particle surface concentration', 'Maximum positive particle surface concentration [mol.m-3]', 'Measured battery open-circuit voltage [V]', 'Measured open-circuit voltage', 'Measured open-circuit voltage [V]', 'Minimum negative particle concentration', 'Minimum negative particle concentration [mol.m-3]', 'Minimum negative particle surface concentration', 'Minimum negative particle surface concentration [mol.m-3]', 'Minimum positive particle concentration', 'Minimum positive particle concentration [mol.m-3]', 'Minimum positive particle surface concentration', 'Minimum positive particle surface concentration [mol.m-3]', 'Negative current collector potential', 'Negative current collector potential [V]', 'Negative current collector temperature', 'Negative current collector temperature [K]', 'Negative electrode active material volume fraction', 'Negative electrode active material volume fraction change', 'Negative electrode current density', 'Negative electrode current density [A.m-2]', 'Negative electrode entropic change', 'Negative electrode exchange current density', 'Negative electrode exchange current density [A.m-2]', 'Negative electrode exchange current density per volume [A.m-3]', 'Negative electrode extent of lithiation', 'Negative electrode interfacial current density', 'Negative electrode interfacial current density [A.m-2]', 'Negative electrode interfacial current density per volume [A.m-3]', 'Negative electrode ohmic losses', 'Negative electrode ohmic losses [V]', 'Negative electrode open-circuit potential', 'Negative electrode open-circuit potential [V]', 'Negative electrode oxygen exchange current density', 'Negative electrode oxygen exchange current density [A.m-2]', 'Negative electrode oxygen exchange current density per volume [A.m-3]', 'Negative electrode oxygen interfacial current density', 'Negative electrode oxygen interfacial current density [A.m-2]', 'Negative electrode oxygen interfacial current density per volume [A.m-3]', 'Negative electrode oxygen open-circuit potential', 'Negative electrode oxygen open-circuit potential [V]', 'Negative electrode oxygen reaction overpotential', 'Negative electrode oxygen reaction overpotential [V]', 'Negative electrode porosity', 'Negative electrode porosity change', 'Negative electrode potential', 'Negative electrode potential [V]', 'Negative electrode pressure', 'Negative electrode reaction overpotential', 'Negative electrode reaction overpotential [V]', 'SEI film overpotential', 'SEI film overpotential [V]', 'SEI interfacial current density', 'SEI interfacial current density [A.m-2]', 'Negative electrode surface area to volume ratio', 'Negative electrode surface area to volume ratio [m-1]', 'Negative electrode surface potential difference', 'Negative electrode surface potential difference [V]', 'Negative electrode temperature', 'Negative electrode temperature [K]', 'Negative electrode tortuosity', 'Negative electrode transverse volume-averaged acceleration', 'Negative electrode transverse volume-averaged acceleration [m.s-2]', 'Negative electrode transverse volume-averaged velocity', 'Negative electrode transverse volume-averaged velocity [m.s-2]', 'Negative electrode volume-averaged acceleration', 'Negative electrode volume-averaged acceleration [m.s-1]', 'Negative electrode volume-averaged concentration', 'Negative electrode volume-averaged concentration [mol.m-3]', 'Negative electrode volume-averaged velocity', 'Negative electrode volume-averaged velocity [m.s-1]', 'Negative electrolyte concentration', 'Negative electrolyte concentration [Molar]', 'Negative electrolyte concentration [mol.m-3]', 'Negative electrolyte current density', 'Negative electrolyte current density [A.m-2]', 'Negative electrolyte potential', 'Negative electrolyte potential [V]', 'Negative electrolyte tortuosity', 'Negative particle concentration', 'Negative particle concentration [mol.m-3]', 'Negative particle flux', 'Negative particle radius', 'Negative particle radius [m]', 'Negative particle surface concentration', 'Negative particle surface concentration [mol.m-3]', 'Negative SEI concentration [mol.m-3]', 'Ohmic heating', 'Ohmic heating [W.m-3]', 'Outer SEI concentration [mol.m-3]', 'Outer SEI interfacial current density', 'Outer SEI interfacial current density [A.m-2]', 'Outer SEI thickness', 'Outer SEI thickness [m]', 'Outer positive electrode SEI concentration [mol.m-3]', 'Outer positive electrode SEI interfacial current density', 'Outer positive electrode SEI interfacial current density [A.m-2]', 'Outer positive electrode SEI thickness', 'Outer positive electrode SEI thickness [m]', 'Oxygen exchange current density', 'Oxygen exchange current density [A.m-2]', 'Oxygen exchange current density per volume [A.m-3]', 'Oxygen interfacial current density', 'Oxygen interfacial current density [A.m-2]', 'Oxygen interfacial current density per volume [A.m-3]', 'Porosity', 'Porosity change', 'Positive current collector potential', 'Positive current collector potential [V]', 'Positive current collector temperature', 'Positive current collector temperature [K]', 'Positive electrode active material volume fraction', 'Positive electrode active material volume fraction change', 'Positive electrode current density', 'Positive electrode current density [A.m-2]', 'Positive electrode entropic change', 'Positive electrode exchange current density', 'Positive electrode exchange current density [A.m-2]', 'Positive electrode exchange current density per volume [A.m-3]', 'Positive electrode extent of lithiation', 'Positive electrode interfacial current density', 'Positive electrode interfacial current density [A.m-2]', 'Positive electrode interfacial current density per volume [A.m-3]', 'Positive electrode ohmic losses', 'Positive electrode ohmic losses [V]', 'Positive electrode open-circuit potential', 'Positive electrode open-circuit potential [V]', 'Positive electrode oxygen exchange current density', 'Positive electrode oxygen exchange current density [A.m-2]', 'Positive electrode oxygen exchange current density per volume [A.m-3]', 'Positive electrode oxygen interfacial current density', 'Positive electrode oxygen interfacial current density [A.m-2]', 'Positive electrode oxygen interfacial current density per volume [A.m-3]', 'Positive electrode oxygen open-circuit potential', 'Positive electrode oxygen open-circuit potential [V]', 'Positive electrode oxygen reaction overpotential', 'Positive electrode oxygen reaction overpotential [V]', 'Positive electrode porosity', 'Positive electrode porosity change', 'Positive electrode potential', 'Positive electrode potential [V]', 'Positive electrode pressure', 'Positive electrode reaction overpotential', 'Positive electrode reaction overpotential [V]', 'Positive electrode SEI film overpotential', 'Positive electrode SEI film overpotential [V]', 'Positive electrode SEI interfacial current density', 'Positive electrode SEI interfacial current density [A.m-2]', 'Positive electrode surface area to volume ratio', 'Positive electrode surface area to volume ratio [m-1]', 'Positive electrode surface potential difference', 'Positive electrode surface potential difference [V]', 'Positive electrode temperature', 'Positive electrode temperature [K]', 'Positive electrode tortuosity', 'Positive electrode transverse volume-averaged acceleration', 'Positive electrode transverse volume-averaged acceleration [m.s-2]', 'Positive electrode transverse volume-averaged velocity', 'Positive electrode transverse volume-averaged velocity [m.s-2]', 'Positive electrode volume-averaged acceleration', 'Positive electrode volume-averaged acceleration [m.s-1]', 'Positive electrode volume-averaged concentration', 'Positive electrode volume-averaged concentration [mol.m-3]', 'Positive electrode volume-averaged velocity', 'Positive electrode volume-averaged velocity [m.s-1]', 'Positive electrolyte concentration', 'Positive electrolyte concentration [Molar]', 'Positive electrolyte concentration [mol.m-3]', 'Positive electrolyte current density', 'Positive electrolyte current density [A.m-2]', 'Positive electrolyte potential', 'Positive electrolyte potential [V]', 'Positive electrolyte tortuosity', 'Positive particle concentration', 'Positive particle concentration [mol.m-3]', 'Positive particle flux', 'Positive particle radius', 'Positive particle radius [m]', 'Positive particle surface concentration', 'Positive particle surface concentration [mol.m-3]', 'Positive SEI concentration [mol.m-3]', 'Pressure', 'R-averaged negative particle concentration', 'R-averaged negative particle concentration [mol.m-3]', 'R-averaged positive particle concentration', 'R-averaged positive particle concentration [mol.m-3]', 'Reversible heating', 'Reversible heating [W.m-3]', 'Sei interfacial current density', 'Sei interfacial current density [A.m-2]', 'Sei interfacial current density per volume [A.m-3]', 'Separator electrolyte concentration', 'Separator electrolyte concentration [Molar]', 'Separator electrolyte concentration [mol.m-3]', 'Separator electrolyte potential', 'Separator electrolyte potential [V]', 'Separator porosity', 'Separator porosity change', 'Separator pressure', 'Separator temperature', 'Separator temperature [K]', 'Separator tortuosity', 'Separator transverse volume-averaged acceleration', 'Separator transverse volume-averaged acceleration [m.s-2]', 'Separator transverse volume-averaged velocity', 'Separator transverse volume-averaged velocity [m.s-2]', 'Separator volume-averaged acceleration', 'Separator volume-averaged acceleration [m.s-1]', 'Separator volume-averaged velocity', 'Separator volume-averaged velocity [m.s-1]', 'Sum of electrolyte reaction source terms', 'Sum of interfacial current densities', 'Sum of negative electrode electrolyte reaction source terms', 'Sum of negative electrode interfacial current densities', 'Sum of positive electrode electrolyte reaction source terms', 'Sum of positive electrode interfacial current densities', 'Sum of x-averaged negative electrode electrolyte reaction source terms', 'Sum of x-averaged negative electrode interfacial current densities', 'Sum of x-averaged positive electrode electrolyte reaction source terms', 'Sum of x-averaged positive electrode interfacial current densities', 'Terminal power [W]', 'Voltage', 'Voltage [V]', 'Time', 'Time [h]', 'Time [min]', 'Time [s]', 'Total concentration in electrolyte [mol]', 'Total current density', 'Total current density [A.m-2]', 'Total heating', 'Total heating [W.m-3]', 'Total lithium in negative electrode [mol]', 'Total lithium in positive electrode [mol]', 'Total SEI thickness', 'Total SEI thickness [m]', 'Total positive electrode SEI thickness', 'Total positive electrode SEI thickness [m]', 'Transverse volume-averaged acceleration', 'Transverse volume-averaged acceleration [m.s-2]', 'Transverse volume-averaged velocity', 'Transverse volume-averaged velocity [m.s-2]', 'Volume-averaged Ohmic heating', 'Volume-averaged Ohmic heating [W.m-3]', 'Volume-averaged acceleration', 'Volume-averaged acceleration [m.s-1]', 'Volume-averaged cell temperature', 'Volume-averaged cell temperature [K]', 'Volume-averaged irreversible electrochemical heating', 'Volume-averaged irreversible electrochemical heating[W.m-3]', 'Volume-averaged reversible heating', 'Volume-averaged reversible heating [W.m-3]', 'Volume-averaged total heating', 'Volume-averaged total heating [W.m-3]', 'Volume-averaged velocity', 'Volume-averaged velocity [m.s-1]', 'X-averaged Ohmic heating', 'X-averaged Ohmic heating [W.m-3]', 'X-averaged battery concentration overpotential [V]', 'X-averaged battery electrolyte ohmic losses [V]', 'X-averaged battery open-circuit voltage [V]', 'X-averaged battery reaction overpotential [V]', 'X-averaged battery solid phase ohmic losses [V]', 'X-averaged cell temperature', 'X-averaged cell temperature [K]', 'X-averaged concentration overpotential', 'X-averaged concentration overpotential [V]', 'X-averaged electrolyte concentration', 'X-averaged electrolyte concentration [Molar]', 'X-averaged electrolyte concentration [mol.m-3]', 'X-averaged electrolyte ohmic losses', 'X-averaged electrolyte ohmic losses [V]', 'X-averaged electrolyte overpotential', 'X-averaged electrolyte overpotential [V]', 'X-averaged electrolyte potential', 'X-averaged electrolyte potential [V]', 'X-averaged inner SEI concentration [mol.m-3]', 'X-averaged inner SEI interfacial current density', 'X-averaged inner SEI interfacial current density [A.m-2]', 'X-averaged inner SEI thickness', 'X-averaged inner SEI thickness [m]', 'X-averaged inner positive electrode SEI concentration [mol.m-3]', 'X-averaged inner positive electrode SEI interfacial current density', 'X-averaged inner positive electrode SEI interfacial current density [A.m-2]', 'X-averaged inner positive electrode SEI thickness', 'X-averaged inner positive electrode SEI thickness [m]', 'X-averaged irreversible electrochemical heating', 'X-averaged irreversible electrochemical heating [W.m-3]', 'X-averaged negative electrode active material volume fraction', 'X-averaged negative electrode active material volume fraction change', 'X-averaged negative electrode entropic change', 'X-averaged negative electrode exchange current density', 'X-averaged negative electrode exchange current density [A.m-2]', 'X-averaged negative electrode exchange current density per volume [A.m-3]', 'X-averaged negative electrode extent of lithiation', 'X-averaged negative electrode interfacial current density', 'X-averaged negative electrode interfacial current density [A.m-2]', 'X-averaged negative electrode interfacial current density per volume [A.m-3]', 'X-averaged negative electrode ohmic losses', 'X-averaged negative electrode ohmic losses [V]', 'X-averaged negative electrode open-circuit potential', 'X-averaged negative electrode open-circuit potential [V]', 'X-averaged negative electrode oxygen exchange current density', 'X-averaged negative electrode oxygen exchange current density [A.m-2]', 'X-averaged negative electrode oxygen exchange current density per volume [A.m-3]', 'X-averaged negative electrode oxygen interfacial current density', 'X-averaged negative electrode oxygen interfacial current density [A.m-2]', 'X-averaged negative electrode oxygen interfacial current density per volume [A.m-3]', 'X-averaged negative electrode oxygen open-circuit potential', 'X-averaged negative electrode oxygen open-circuit potential [V]', 'X-averaged negative electrode oxygen reaction overpotential', 'X-averaged negative electrode oxygen reaction overpotential [V]', 'X-averaged negative electrode porosity', 'X-averaged negative electrode porosity change', 'X-averaged negative electrode potential', 'X-averaged negative electrode potential [V]', 'X-averaged negative electrode pressure', 'X-averaged negative electrode reaction overpotential', 'X-averaged negative electrode reaction overpotential [V]', 'X-averaged negative electrode resistance [Ohm.m2]', 'X-averaged SEI concentration [mol.m-3]', 'X-averaged SEI film overpotential', 'X-averaged SEI film overpotential [V]', 'X-averaged SEI interfacial current density', 'X-averaged SEI interfacial current density [A.m-2]', 'X-averaged negative electrode surface area to volume ratio', 'X-averaged negative electrode surface area to volume ratio [m-1]', 'X-averaged negative electrode surface potential difference', 'X-averaged negative electrode surface potential difference [V]', 'X-averaged negative electrode temperature', 'X-averaged negative electrode temperature [K]', 'X-averaged negative electrode tortuosity', 'X-averaged negative electrode total interfacial current density', 'X-averaged negative electrode total interfacial current density [A.m-2]', 'X-averaged negative electrode total interfacial current density per volume [A.m-3]', 'X-averaged negative electrode transverse volume-averaged acceleration', 'X-averaged negative electrode transverse volume-averaged acceleration [m.s-2]', 'X-averaged negative electrode transverse volume-averaged velocity', 'X-averaged negative electrode transverse volume-averaged velocity [m.s-2]', 'X-averaged negative electrode volume-averaged acceleration', 'X-averaged negative electrode volume-averaged acceleration [m.s-1]', 'X-averaged negative electrolyte concentration', 'X-averaged negative electrolyte concentration [mol.m-3]', 'X-averaged negative electrolyte potential', 'X-averaged negative electrolyte potential [V]', 'X-averaged negative electrolyte tortuosity', 'X-averaged negative particle concentration', 'X-averaged negative particle concentration [mol.m-3]', 'X-averaged negative particle flux', 'X-averaged negative particle surface concentration', 'X-averaged negative particle surface concentration [mol.m-3]', 'X-averaged open-circuit voltage', 'X-averaged open-circuit voltage [V]', 'X-averaged outer SEI concentration [mol.m-3]', 'X-averaged outer SEI interfacial current density', 'X-averaged outer SEI interfacial current density [A.m-2]', 'X-averaged outer SEI thickness', 'X-averaged outer SEI thickness [m]', 'X-averaged outer positive electrode SEI concentration [mol.m-3]', 'X-averaged outer positive electrode SEI interfacial current density', 'X-averaged outer positive electrode SEI interfacial current density [A.m-2]', 'X-averaged outer positive electrode SEI thickness', 'X-averaged outer positive electrode SEI thickness [m]', 'X-averaged positive electrode active material volume fraction', 'X-averaged positive electrode active material volume fraction change', 'X-averaged positive electrode entropic change', 'X-averaged positive electrode exchange current density', 'X-averaged positive electrode exchange current density [A.m-2]', 'X-averaged positive electrode exchange current density per volume [A.m-3]', 'X-averaged positive electrode extent of lithiation', 'X-averaged positive electrode interfacial current density', 'X-averaged positive electrode interfacial current density [A.m-2]', 'X-averaged positive electrode interfacial current density per volume [A.m-3]', 'X-averaged positive electrode ohmic losses', 'X-averaged positive electrode ohmic losses [V]', 'X-averaged positive electrode open-circuit potential', 'X-averaged positive electrode open-circuit potential [V]', 'X-averaged positive electrode oxygen exchange current density', 'X-averaged positive electrode oxygen exchange current density [A.m-2]', 'X-averaged positive electrode oxygen exchange current density per volume [A.m-3]', 'X-averaged positive electrode oxygen interfacial current density', 'X-averaged positive electrode oxygen interfacial current density [A.m-2]', 'X-averaged positive electrode oxygen interfacial current density per volume [A.m-3]', 'X-averaged positive electrode oxygen open-circuit potential', 'X-averaged positive electrode oxygen open-circuit potential [V]', 'X-averaged positive electrode oxygen reaction overpotential', 'X-averaged positive electrode oxygen reaction overpotential [V]', 'X-averaged positive electrode porosity', 'X-averaged positive electrode porosity change', 'X-averaged positive electrode potential', 'X-averaged positive electrode potential [V]', 'X-averaged positive electrode pressure', 'X-averaged positive electrode reaction overpotential', 'X-averaged positive electrode reaction overpotential [V]', 'X-averaged positive electrode resistance [Ohm.m2]', 'X-averaged positive electrode SEI concentration [mol.m-3]', 'X-averaged positive electrode SEI film overpotential', 'X-averaged positive electrode SEI film overpotential [V]', 'X-averaged positive electrode SEI interfacial current density', 'X-averaged positive electrode SEI interfacial current density [A.m-2]', 'X-averaged positive electrode surface area to volume ratio', 'X-averaged positive electrode surface area to volume ratio [m-1]', 'X-averaged positive electrode surface potential difference', 'X-averaged positive electrode surface potential difference [V]', 'X-averaged positive electrode temperature', 'X-averaged positive electrode temperature [K]', 'X-averaged positive electrode tortuosity', 'X-averaged positive electrode total interfacial current density', 'X-averaged positive electrode total interfacial current density [A.m-2]', 'X-averaged positive electrode total interfacial current density per volume [A.m-3]', 'X-averaged positive electrode transverse volume-averaged acceleration', 'X-averaged positive electrode transverse volume-averaged acceleration [m.s-2]', 'X-averaged positive electrode transverse volume-averaged velocity', 'X-averaged positive electrode transverse volume-averaged velocity [m.s-2]', 'X-averaged positive electrode volume-averaged acceleration', 'X-averaged positive electrode volume-averaged acceleration [m.s-1]', 'X-averaged positive electrolyte concentration', 'X-averaged positive electrolyte concentration [mol.m-3]', 'X-averaged positive electrolyte potential', 'X-averaged positive electrolyte potential [V]', 'X-averaged positive electrolyte tortuosity', 'X-averaged positive particle concentration', 'X-averaged positive particle concentration [mol.m-3]', 'X-averaged positive particle flux', 'X-averaged positive particle surface concentration', 'X-averaged positive particle surface concentration [mol.m-3]', 'X-averaged reaction overpotential', 'X-averaged reaction overpotential [V]', 'X-averaged reversible heating', 'X-averaged reversible heating [W.m-3]', 'X-averaged SEI film overpotential', 'X-averaged SEI film overpotential [V]', 'X-averaged separator electrolyte concentration', 'X-averaged separator electrolyte concentration [mol.m-3]', 'X-averaged separator electrolyte potential', 'X-averaged separator electrolyte potential [V]', 'X-averaged separator porosity', 'X-averaged separator porosity change', 'X-averaged separator pressure', 'X-averaged separator temperature', 'X-averaged separator temperature [K]', 'X-averaged separator tortuosity', 'X-averaged separator transverse volume-averaged acceleration', 'X-averaged separator transverse volume-averaged acceleration [m.s-2]', 'X-averaged separator transverse volume-averaged velocity', 'X-averaged separator transverse volume-averaged velocity [m.s-2]', 'X-averaged separator volume-averaged acceleration', 'X-averaged separator volume-averaged acceleration [m.s-1]', 'X-averaged solid phase ohmic losses', 'X-averaged solid phase ohmic losses [V]', 'X-averaged total heating', 'X-averaged total heating [W.m-3]', 'X-averaged total SEI thickness', 'X-averaged total SEI thickness [m]', 'X-averaged total positive electrode SEI thickness', 'X-averaged total positive electrode SEI thickness [m]', 'X-averaged volume-averaged acceleration', 'X-averaged volume-averaged acceleration [m.s-1]', 'r_n', 'r_n [m]', 'r_p', 'r_p [m]', 'x', 'x [m]', 'x_n', 'x_n [m]', 'x_p', 'x_p [m]', 'x_s', 'x_s [m]']\n" - ] - } - ], - "source": [ - "keys = list(model.variables.keys())\n", - "keys.sort()\n", - "print(keys)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you want to find a particular variable you can search the variables dictionary" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time\n", - "Time [h]\n", - "Time [min]\n", - "Time [s]\n" - ] - } - ], - "source": [ - "model.variables.search(\"time\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll use the time in hours" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solution['Time [h]']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This created a new processed variable and stored it on the solution object" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['Negative particle surface concentration [mol.m-3]', 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Current [A]', 'Negative electrode potential [V]', 'Electrolyte potential [V]', 'Positive electrode potential [V]', 'Voltage [V]', 'Time [h]'])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solution.data.keys()" + "text/plain": [ + "interactive(children=(FloatSlider(value=0.0, description='t', max=3510.0, step=35.1), Output()), _dom_classes=…" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see the data by simply accessing the entries attribute of the processed variable" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0. , 0.025, 0.05 , 0.075, 0.1 , 0.125, 0.15 , 0.175, 0.2 ,\n", - " 0.225, 0.25 , 0.275, 0.3 , 0.325, 0.35 , 0.375, 0.4 , 0.425,\n", - " 0.45 , 0.475, 0.5 , 0.525, 0.55 , 0.575, 0.6 , 0.625, 0.65 ,\n", - " 0.675, 0.7 , 0.725, 0.75 , 0.775, 0.8 , 0.825, 0.85 , 0.875,\n", - " 0.9 , 0.925, 0.95 , 0.975])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solution['Time [h]'].entries" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also call the method with specified time(s) in SI units of seconds" - ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "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 matplotlib.pyplot as plt\n", + "os.chdir(pybamm.__path__[0]+'/..')\n", + "\n", + "# load model\n", + "model = pybamm.lithium_ion.SPMe()\n", + "\n", + "# set up and solve simulation\n", + "simulation = pybamm.Simulation(model)\n", + "dt = 90\n", + "t_eval = np.arange(0, 3600, dt) # time in seconds\n", + "solution = simulation.solve(t_eval)\n", + "\n", + "quick_plot = pybamm.QuickPlot(solution)\n", + "quick_plot.dynamic_plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Behind the scenes the QuickPlot classed has created some processed variables which can interpolate the model variables for our solution and has also stored the results for the solution steps" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "time_in_seconds = np.array([0, 600, 900, 1700, 3000 ])" + "data": { + "text/plain": [ + "dict_keys(['Negative particle surface concentration [mol.m-3]', 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Current [A]', 'Negative electrode potential [V]', 'Electrolyte potential [V]', 'Positive electrode potential [V]', 'Voltage [V]'])" ] - }, + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solution.data.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0. , 0.16666667, 0.25 , 0.47222222, 0.83333333])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solution['Time [h]'](time_in_seconds)" + "data": { + "text/plain": [ + "(20, 40)" ] - }, + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solution.data['Negative particle surface concentration [mol.m-3]'].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If the variable has not already been processed it will be created behind the scenes" + "data": { + "text/plain": [ + "(40,)" ] - }, + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solution.t.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the dictionary keys are in the same order as the subplots in the QuickPlot figure. We can add new processed variables to the solution by simply using it like a dictionary. First let's find a few more variables to look at. As you will see there are quite a few:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "tags": [ + "outputPrepend" + ] + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([298.15, 298.15, 298.15, 298.15, 298.15])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "var = 'X-averaged negative electrode temperature [K]'\n", - "solution[var](time_in_seconds)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "['Ambient temperature', 'Ambient temperature [K]', 'Average negative particle concentration', 'Average negative particle concentration [mol.m-3]', 'Average positive particle concentration', 'Average positive particle concentration [mol.m-3]', 'Battery voltage [V]', 'C-rate', 'Cell temperature', 'Cell temperature [K]', 'Change in measured open-circuit voltage', 'Change in measured open-circuit voltage [V]', 'Current [A]', 'Current collector current density', 'Current collector current density [A.m-2]', 'Discharge capacity [A.h]', 'Electrode current density', 'Electrode tortuosity', 'Electrolyte concentration', 'Electrolyte concentration [Molar]', 'Electrolyte concentration [mol.m-3]', 'Electrolyte current density', 'Electrolyte current density [A.m-2]', 'Electrolyte flux', 'Electrolyte flux [mol.m-2.s-1]', 'Electrolyte potential', 'Electrolyte potential [V]', 'Electrolyte tortuosity', 'Exchange current density', 'Exchange current density [A.m-2]', 'Exchange current density per volume [A.m-3]', 'Gradient of electrolyte potential', 'Gradient of negative electrode potential', 'Gradient of negative electrolyte potential', 'Gradient of positive electrode potential', 'Gradient of positive electrolyte potential', 'Gradient of separator electrolyte potential', 'Inner SEI concentration [mol.m-3]', 'Inner SEI interfacial current density', 'Inner SEI interfacial current density [A.m-2]', 'Inner SEI thickness', 'Inner SEI thickness [m]', 'Inner positive electrode SEI concentration [mol.m-3]', 'Inner positive electrode SEI interfacial current density', 'Inner positive electrode SEI interfacial current density [A.m-2]', 'Inner positive electrode SEI thickness', 'Inner positive electrode SEI thickness [m]', 'Interfacial current density', 'Interfacial current density [A.m-2]', 'Interfacial current density per volume [A.m-3]', 'Irreversible electrochemical heating', 'Irreversible electrochemical heating [W.m-3]', 'Leading-order current collector current density', 'Leading-order electrode tortuosity', 'Leading-order electrolyte tortuosity', 'Leading-order negative electrode porosity', 'Leading-order negative electrode tortuosity', 'Leading-order negative electrolyte tortuosity', 'Leading-order porosity', 'Leading-order positive electrode porosity', 'Leading-order positive electrode tortuosity', 'Leading-order positive electrolyte tortuosity', 'Leading-order separator porosity', 'Leading-order separator tortuosity', 'Leading-order x-averaged negative electrode porosity', 'Leading-order x-averaged negative electrode porosity change', 'Leading-order x-averaged negative electrode tortuosity', 'Leading-order x-averaged negative electrolyte tortuosity', 'Leading-order x-averaged positive electrode porosity', 'Leading-order x-averaged positive electrode porosity change', 'Leading-order x-averaged positive electrode tortuosity', 'Leading-order x-averaged positive electrolyte tortuosity', 'Leading-order x-averaged separator porosity', 'Leading-order x-averaged separator porosity change', 'Leading-order x-averaged separator tortuosity', 'Local ECM resistance', 'Local ECM resistance [Ohm]', 'Local voltage', 'Local voltage [V]', 'Loss of lithium to SEI [mol]', 'Loss of lithium to positive electrode SEI [mol]', 'Maximum negative particle concentration', 'Maximum negative particle concentration [mol.m-3]', 'Maximum negative particle surface concentration', 'Maximum negative particle surface concentration [mol.m-3]', 'Maximum positive particle concentration', 'Maximum positive particle concentration [mol.m-3]', 'Maximum positive particle surface concentration', 'Maximum positive particle surface concentration [mol.m-3]', 'Measured battery open-circuit voltage [V]', 'Measured open-circuit voltage', 'Measured open-circuit voltage [V]', 'Minimum negative particle concentration', 'Minimum negative particle concentration [mol.m-3]', 'Minimum negative particle surface concentration', 'Minimum negative particle surface concentration [mol.m-3]', 'Minimum positive particle concentration', 'Minimum positive particle concentration [mol.m-3]', 'Minimum positive particle surface concentration', 'Minimum positive particle surface concentration [mol.m-3]', 'Negative current collector potential', 'Negative current collector potential [V]', 'Negative current collector temperature', 'Negative current collector temperature [K]', 'Negative electrode active material volume fraction', 'Negative electrode active material volume fraction change', 'Negative electrode current density', 'Negative electrode current density [A.m-2]', 'Negative electrode entropic change', 'Negative electrode exchange current density', 'Negative electrode exchange current density [A.m-2]', 'Negative electrode exchange current density per volume [A.m-3]', 'Negative electrode extent of lithiation', 'Negative electrode interfacial current density', 'Negative electrode interfacial current density [A.m-2]', 'Negative electrode interfacial current density per volume [A.m-3]', 'Negative electrode ohmic losses', 'Negative electrode ohmic losses [V]', 'Negative electrode open-circuit potential', 'Negative electrode open-circuit potential [V]', 'Negative electrode oxygen exchange current density', 'Negative electrode oxygen exchange current density [A.m-2]', 'Negative electrode oxygen exchange current density per volume [A.m-3]', 'Negative electrode oxygen interfacial current density', 'Negative electrode oxygen interfacial current density [A.m-2]', 'Negative electrode oxygen interfacial current density per volume [A.m-3]', 'Negative electrode oxygen open-circuit potential', 'Negative electrode oxygen open-circuit potential [V]', 'Negative electrode oxygen reaction overpotential', 'Negative electrode oxygen reaction overpotential [V]', 'Negative electrode porosity', 'Negative electrode porosity change', 'Negative electrode potential', 'Negative electrode potential [V]', 'Negative electrode pressure', 'Negative electrode reaction overpotential', 'Negative electrode reaction overpotential [V]', 'SEI film overpotential', 'SEI film overpotential [V]', 'SEI interfacial current density', 'SEI interfacial current density [A.m-2]', 'Negative electrode surface area to volume ratio', 'Negative electrode surface area to volume ratio [m-1]', 'Negative electrode surface potential difference', 'Negative electrode surface potential difference [V]', 'Negative electrode temperature', 'Negative electrode temperature [K]', 'Negative electrode tortuosity', 'Negative electrode transverse volume-averaged acceleration', 'Negative electrode transverse volume-averaged acceleration [m.s-2]', 'Negative electrode transverse volume-averaged velocity', 'Negative electrode transverse volume-averaged velocity [m.s-2]', 'Negative electrode volume-averaged acceleration', 'Negative electrode volume-averaged acceleration [m.s-1]', 'Negative electrode volume-averaged concentration', 'Negative electrode volume-averaged concentration [mol.m-3]', 'Negative electrode volume-averaged velocity', 'Negative electrode volume-averaged velocity [m.s-1]', 'Negative electrolyte concentration', 'Negative electrolyte concentration [Molar]', 'Negative electrolyte concentration [mol.m-3]', 'Negative electrolyte current density', 'Negative electrolyte current density [A.m-2]', 'Negative electrolyte potential', 'Negative electrolyte potential [V]', 'Negative electrolyte tortuosity', 'Negative particle concentration', 'Negative particle concentration [mol.m-3]', 'Negative particle flux', 'Negative particle radius', 'Negative particle radius [m]', 'Negative particle surface concentration', 'Negative particle surface concentration [mol.m-3]', 'Negative SEI concentration [mol.m-3]', 'Ohmic heating', 'Ohmic heating [W.m-3]', 'Outer SEI concentration [mol.m-3]', 'Outer SEI interfacial current density', 'Outer SEI interfacial current density [A.m-2]', 'Outer SEI thickness', 'Outer SEI thickness [m]', 'Outer positive electrode SEI concentration [mol.m-3]', 'Outer positive electrode SEI interfacial current density', 'Outer positive electrode SEI interfacial current density [A.m-2]', 'Outer positive electrode SEI thickness', 'Outer positive electrode SEI thickness [m]', 'Oxygen exchange current density', 'Oxygen exchange current density [A.m-2]', 'Oxygen exchange current density per volume [A.m-3]', 'Oxygen interfacial current density', 'Oxygen interfacial current density [A.m-2]', 'Oxygen interfacial current density per volume [A.m-3]', 'Porosity', 'Porosity change', 'Positive current collector potential', 'Positive current collector potential [V]', 'Positive current collector temperature', 'Positive current collector temperature [K]', 'Positive electrode active material volume fraction', 'Positive electrode active material volume fraction change', 'Positive electrode current density', 'Positive electrode current density [A.m-2]', 'Positive electrode entropic change', 'Positive electrode exchange current density', 'Positive electrode exchange current density [A.m-2]', 'Positive electrode exchange current density per volume [A.m-3]', 'Positive electrode extent of lithiation', 'Positive electrode interfacial current density', 'Positive electrode interfacial current density [A.m-2]', 'Positive electrode interfacial current density per volume [A.m-3]', 'Positive electrode ohmic losses', 'Positive electrode ohmic losses [V]', 'Positive electrode open-circuit potential', 'Positive electrode open-circuit potential [V]', 'Positive electrode oxygen exchange current density', 'Positive electrode oxygen exchange current density [A.m-2]', 'Positive electrode oxygen exchange current density per volume [A.m-3]', 'Positive electrode oxygen interfacial current density', 'Positive electrode oxygen interfacial current density [A.m-2]', 'Positive electrode oxygen interfacial current density per volume [A.m-3]', 'Positive electrode oxygen open-circuit potential', 'Positive electrode oxygen open-circuit potential [V]', 'Positive electrode oxygen reaction overpotential', 'Positive electrode oxygen reaction overpotential [V]', 'Positive electrode porosity', 'Positive electrode porosity change', 'Positive electrode potential', 'Positive electrode potential [V]', 'Positive electrode pressure', 'Positive electrode reaction overpotential', 'Positive electrode reaction overpotential [V]', 'Positive electrode SEI film overpotential', 'Positive electrode SEI film overpotential [V]', 'Positive electrode SEI interfacial current density', 'Positive electrode SEI interfacial current density [A.m-2]', 'Positive electrode surface area to volume ratio', 'Positive electrode surface area to volume ratio [m-1]', 'Positive electrode surface potential difference', 'Positive electrode surface potential difference [V]', 'Positive electrode temperature', 'Positive electrode temperature [K]', 'Positive electrode tortuosity', 'Positive electrode transverse volume-averaged acceleration', 'Positive electrode transverse volume-averaged acceleration [m.s-2]', 'Positive electrode transverse volume-averaged velocity', 'Positive electrode transverse volume-averaged velocity [m.s-2]', 'Positive electrode volume-averaged acceleration', 'Positive electrode volume-averaged acceleration [m.s-1]', 'Positive electrode volume-averaged concentration', 'Positive electrode volume-averaged concentration [mol.m-3]', 'Positive electrode volume-averaged velocity', 'Positive electrode volume-averaged velocity [m.s-1]', 'Positive electrolyte concentration', 'Positive electrolyte concentration [Molar]', 'Positive electrolyte concentration [mol.m-3]', 'Positive electrolyte current density', 'Positive electrolyte current density [A.m-2]', 'Positive electrolyte potential', 'Positive electrolyte potential [V]', 'Positive electrolyte tortuosity', 'Positive particle concentration', 'Positive particle concentration [mol.m-3]', 'Positive particle flux', 'Positive particle radius', 'Positive particle radius [m]', 'Positive particle surface concentration', 'Positive particle surface concentration [mol.m-3]', 'Positive SEI concentration [mol.m-3]', 'Pressure', 'R-averaged negative particle concentration', 'R-averaged negative particle concentration [mol.m-3]', 'R-averaged positive particle concentration', 'R-averaged positive particle concentration [mol.m-3]', 'Reversible heating', 'Reversible heating [W.m-3]', 'Sei interfacial current density', 'Sei interfacial current density [A.m-2]', 'Sei interfacial current density per volume [A.m-3]', 'Separator electrolyte concentration', 'Separator electrolyte concentration [Molar]', 'Separator electrolyte concentration [mol.m-3]', 'Separator electrolyte potential', 'Separator electrolyte potential [V]', 'Separator porosity', 'Separator porosity change', 'Separator pressure', 'Separator temperature', 'Separator temperature [K]', 'Separator tortuosity', 'Separator transverse volume-averaged acceleration', 'Separator transverse volume-averaged acceleration [m.s-2]', 'Separator transverse volume-averaged velocity', 'Separator transverse volume-averaged velocity [m.s-2]', 'Separator volume-averaged acceleration', 'Separator volume-averaged acceleration [m.s-1]', 'Separator volume-averaged velocity', 'Separator volume-averaged velocity [m.s-1]', 'Sum of electrolyte reaction source terms', 'Sum of interfacial current densities', 'Sum of negative electrode electrolyte reaction source terms', 'Sum of negative electrode interfacial current densities', 'Sum of positive electrode electrolyte reaction source terms', 'Sum of positive electrode interfacial current densities', 'Sum of x-averaged negative electrode electrolyte reaction source terms', 'Sum of x-averaged negative electrode interfacial current densities', 'Sum of x-averaged positive electrode electrolyte reaction source terms', 'Sum of x-averaged positive electrode interfacial current densities', 'Terminal power [W]', 'Voltage', 'Voltage [V]', 'Time', 'Time [h]', 'Time [min]', 'Time [s]', 'Total concentration in electrolyte [mol]', 'Total current density', 'Total current density [A.m-2]', 'Total heating', 'Total heating [W.m-3]', 'Total lithium in negative electrode [mol]', 'Total lithium in positive electrode [mol]', 'Total SEI thickness', 'Total SEI thickness [m]', 'Total positive electrode SEI thickness', 'Total positive electrode SEI thickness [m]', 'Transverse volume-averaged acceleration', 'Transverse volume-averaged acceleration [m.s-2]', 'Transverse volume-averaged velocity', 'Transverse volume-averaged velocity [m.s-2]', 'Volume-averaged Ohmic heating', 'Volume-averaged Ohmic heating [W.m-3]', 'Volume-averaged acceleration', 'Volume-averaged acceleration [m.s-1]', 'Volume-averaged cell temperature', 'Volume-averaged cell temperature [K]', 'Volume-averaged irreversible electrochemical heating', 'Volume-averaged irreversible electrochemical heating[W.m-3]', 'Volume-averaged reversible heating', 'Volume-averaged reversible heating [W.m-3]', 'Volume-averaged total heating', 'Volume-averaged total heating [W.m-3]', 'Volume-averaged velocity', 'Volume-averaged velocity [m.s-1]', 'X-averaged Ohmic heating', 'X-averaged Ohmic heating [W.m-3]', 'X-averaged battery concentration overpotential [V]', 'X-averaged battery electrolyte ohmic losses [V]', 'X-averaged battery open-circuit voltage [V]', 'X-averaged battery reaction overpotential [V]', 'X-averaged battery solid phase ohmic losses [V]', 'X-averaged cell temperature', 'X-averaged cell temperature [K]', 'X-averaged concentration overpotential', 'X-averaged concentration overpotential [V]', 'X-averaged electrolyte concentration', 'X-averaged electrolyte concentration [Molar]', 'X-averaged electrolyte concentration [mol.m-3]', 'X-averaged electrolyte ohmic losses', 'X-averaged electrolyte ohmic losses [V]', 'X-averaged electrolyte overpotential', 'X-averaged electrolyte overpotential [V]', 'X-averaged electrolyte potential', 'X-averaged electrolyte potential [V]', 'X-averaged inner SEI concentration [mol.m-3]', 'X-averaged inner SEI interfacial current density', 'X-averaged inner SEI interfacial current density [A.m-2]', 'X-averaged inner SEI thickness', 'X-averaged inner SEI thickness [m]', 'X-averaged inner positive electrode SEI concentration [mol.m-3]', 'X-averaged inner positive electrode SEI interfacial current density', 'X-averaged inner positive electrode SEI interfacial current density [A.m-2]', 'X-averaged inner positive electrode SEI thickness', 'X-averaged inner positive electrode SEI thickness [m]', 'X-averaged irreversible electrochemical heating', 'X-averaged irreversible electrochemical heating [W.m-3]', 'X-averaged negative electrode active material volume fraction', 'X-averaged negative electrode active material volume fraction change', 'X-averaged negative electrode entropic change', 'X-averaged negative electrode exchange current density', 'X-averaged negative electrode exchange current density [A.m-2]', 'X-averaged negative electrode exchange current density per volume [A.m-3]', 'X-averaged negative electrode extent of lithiation', 'X-averaged negative electrode interfacial current density', 'X-averaged negative electrode interfacial current density [A.m-2]', 'X-averaged negative electrode interfacial current density per volume [A.m-3]', 'X-averaged negative electrode ohmic losses', 'X-averaged negative electrode ohmic losses [V]', 'X-averaged negative electrode open-circuit potential', 'X-averaged negative electrode open-circuit potential [V]', 'X-averaged negative electrode oxygen exchange current density', 'X-averaged negative electrode oxygen exchange current density [A.m-2]', 'X-averaged negative electrode oxygen exchange current density per volume [A.m-3]', 'X-averaged negative electrode oxygen interfacial current density', 'X-averaged negative electrode oxygen interfacial current density [A.m-2]', 'X-averaged negative electrode oxygen interfacial current density per volume [A.m-3]', 'X-averaged negative electrode oxygen open-circuit potential', 'X-averaged negative electrode oxygen open-circuit potential [V]', 'X-averaged negative electrode oxygen reaction overpotential', 'X-averaged negative electrode oxygen reaction overpotential [V]', 'X-averaged negative electrode porosity', 'X-averaged negative electrode porosity change', 'X-averaged negative electrode potential', 'X-averaged negative electrode potential [V]', 'X-averaged negative electrode pressure', 'X-averaged negative electrode reaction overpotential', 'X-averaged negative electrode reaction overpotential [V]', 'X-averaged negative electrode resistance [Ohm.m2]', 'X-averaged SEI concentration [mol.m-3]', 'X-averaged SEI film overpotential', 'X-averaged SEI film overpotential [V]', 'X-averaged SEI interfacial current density', 'X-averaged SEI interfacial current density [A.m-2]', 'X-averaged negative electrode surface area to volume ratio', 'X-averaged negative electrode surface area to volume ratio [m-1]', 'X-averaged negative electrode surface potential difference', 'X-averaged negative electrode surface potential difference [V]', 'X-averaged negative electrode temperature', 'X-averaged negative electrode temperature [K]', 'X-averaged negative electrode tortuosity', 'X-averaged negative electrode total interfacial current density', 'X-averaged negative electrode total interfacial current density [A.m-2]', 'X-averaged negative electrode total interfacial current density per volume [A.m-3]', 'X-averaged negative electrode transverse volume-averaged acceleration', 'X-averaged negative electrode transverse volume-averaged acceleration [m.s-2]', 'X-averaged negative electrode transverse volume-averaged velocity', 'X-averaged negative electrode transverse volume-averaged velocity [m.s-2]', 'X-averaged negative electrode volume-averaged acceleration', 'X-averaged negative electrode volume-averaged acceleration [m.s-1]', 'X-averaged negative electrolyte concentration', 'X-averaged negative electrolyte concentration [mol.m-3]', 'X-averaged negative electrolyte potential', 'X-averaged negative electrolyte potential [V]', 'X-averaged negative electrolyte tortuosity', 'X-averaged negative particle concentration', 'X-averaged negative particle concentration [mol.m-3]', 'X-averaged negative particle flux', 'X-averaged negative particle surface concentration', 'X-averaged negative particle surface concentration [mol.m-3]', 'X-averaged open-circuit voltage', 'X-averaged open-circuit voltage [V]', 'X-averaged outer SEI concentration [mol.m-3]', 'X-averaged outer SEI interfacial current density', 'X-averaged outer SEI interfacial current density [A.m-2]', 'X-averaged outer SEI thickness', 'X-averaged outer SEI thickness [m]', 'X-averaged outer positive electrode SEI concentration [mol.m-3]', 'X-averaged outer positive electrode SEI interfacial current density', 'X-averaged outer positive electrode SEI interfacial current density [A.m-2]', 'X-averaged outer positive electrode SEI thickness', 'X-averaged outer positive electrode SEI thickness [m]', 'X-averaged positive electrode active material volume fraction', 'X-averaged positive electrode active material volume fraction change', 'X-averaged positive electrode entropic change', 'X-averaged positive electrode exchange current density', 'X-averaged positive electrode exchange current density [A.m-2]', 'X-averaged positive electrode exchange current density per volume [A.m-3]', 'X-averaged positive electrode extent of lithiation', 'X-averaged positive electrode interfacial current density', 'X-averaged positive electrode interfacial current density [A.m-2]', 'X-averaged positive electrode interfacial current density per volume [A.m-3]', 'X-averaged positive electrode ohmic losses', 'X-averaged positive electrode ohmic losses [V]', 'X-averaged positive electrode open-circuit potential', 'X-averaged positive electrode open-circuit potential [V]', 'X-averaged positive electrode oxygen exchange current density', 'X-averaged positive electrode oxygen exchange current density [A.m-2]', 'X-averaged positive electrode oxygen exchange current density per volume [A.m-3]', 'X-averaged positive electrode oxygen interfacial current density', 'X-averaged positive electrode oxygen interfacial current density [A.m-2]', 'X-averaged positive electrode oxygen interfacial current density per volume [A.m-3]', 'X-averaged positive electrode oxygen open-circuit potential', 'X-averaged positive electrode oxygen open-circuit potential [V]', 'X-averaged positive electrode oxygen reaction overpotential', 'X-averaged positive electrode oxygen reaction overpotential [V]', 'X-averaged positive electrode porosity', 'X-averaged positive electrode porosity change', 'X-averaged positive electrode potential', 'X-averaged positive electrode potential [V]', 'X-averaged positive electrode pressure', 'X-averaged positive electrode reaction overpotential', 'X-averaged positive electrode reaction overpotential [V]', 'X-averaged positive electrode resistance [Ohm.m2]', 'X-averaged positive electrode SEI concentration [mol.m-3]', 'X-averaged positive electrode SEI film overpotential', 'X-averaged positive electrode SEI film overpotential [V]', 'X-averaged positive electrode SEI interfacial current density', 'X-averaged positive electrode SEI interfacial current density [A.m-2]', 'X-averaged positive electrode surface area to volume ratio', 'X-averaged positive electrode surface area to volume ratio [m-1]', 'X-averaged positive electrode surface potential difference', 'X-averaged positive electrode surface potential difference [V]', 'X-averaged positive electrode temperature', 'X-averaged positive electrode temperature [K]', 'X-averaged positive electrode tortuosity', 'X-averaged positive electrode total interfacial current density', 'X-averaged positive electrode total interfacial current density [A.m-2]', 'X-averaged positive electrode total interfacial current density per volume [A.m-3]', 'X-averaged positive electrode transverse volume-averaged acceleration', 'X-averaged positive electrode transverse volume-averaged acceleration [m.s-2]', 'X-averaged positive electrode transverse volume-averaged velocity', 'X-averaged positive electrode transverse volume-averaged velocity [m.s-2]', 'X-averaged positive electrode volume-averaged acceleration', 'X-averaged positive electrode volume-averaged acceleration [m.s-1]', 'X-averaged positive electrolyte concentration', 'X-averaged positive electrolyte concentration [mol.m-3]', 'X-averaged positive electrolyte potential', 'X-averaged positive electrolyte potential [V]', 'X-averaged positive electrolyte tortuosity', 'X-averaged positive particle concentration', 'X-averaged positive particle concentration [mol.m-3]', 'X-averaged positive particle flux', 'X-averaged positive particle surface concentration', 'X-averaged positive particle surface concentration [mol.m-3]', 'X-averaged reaction overpotential', 'X-averaged reaction overpotential [V]', 'X-averaged reversible heating', 'X-averaged reversible heating [W.m-3]', 'X-averaged SEI film overpotential', 'X-averaged SEI film overpotential [V]', 'X-averaged separator electrolyte concentration', 'X-averaged separator electrolyte concentration [mol.m-3]', 'X-averaged separator electrolyte potential', 'X-averaged separator electrolyte potential [V]', 'X-averaged separator porosity', 'X-averaged separator porosity change', 'X-averaged separator pressure', 'X-averaged separator temperature', 'X-averaged separator temperature [K]', 'X-averaged separator tortuosity', 'X-averaged separator transverse volume-averaged acceleration', 'X-averaged separator transverse volume-averaged acceleration [m.s-2]', 'X-averaged separator transverse volume-averaged velocity', 'X-averaged separator transverse volume-averaged velocity [m.s-2]', 'X-averaged separator volume-averaged acceleration', 'X-averaged separator volume-averaged acceleration [m.s-1]', 'X-averaged solid phase ohmic losses', 'X-averaged solid phase ohmic losses [V]', 'X-averaged total heating', 'X-averaged total heating [W.m-3]', 'X-averaged total SEI thickness', 'X-averaged total SEI thickness [m]', 'X-averaged total positive electrode SEI thickness', 'X-averaged total positive electrode SEI thickness [m]', 'X-averaged volume-averaged acceleration', 'X-averaged volume-averaged acceleration [m.s-1]', 'r_n', 'r_n [m]', 'r_p', 'r_p [m]', 'x', 'x [m]', 'x_n', 'x_n [m]', 'x_p', 'x_p [m]', 'x_s', 'x_s [m]']\n" + ] + } + ], + "source": [ + "keys = list(model.variables.keys())\n", + "keys.sort()\n", + "print(keys)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you want to find a particular variable you can search the variables dictionary" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example the simulation was isothermal, so the temperature remains unchanged." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Time\n", + "Time [h]\n", + "Time [min]\n", + "Time [s]\n" + ] + } + ], + "source": [ + "model.variables.search(\"time\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll use the time in hours" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Saving the solution\n", - "\n", - "The solution can be saved in a number of ways:" + "data": { + "text/plain": [ + "" ] - }, + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solution['Time [h]']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This created a new processed variable and stored it on the solution object" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# to a pickle file (default)\n", - "solution.save_data(\n", - " \"outputs.pickle\", [\"Time [h]\", \"Current [A]\", \"Voltage [V]\", \"Electrolyte concentration [mol.m-3]\"]\n", - ")\n", - "# to a matlab file\n", - "# need to give variable names without space\n", - "solution.save_data(\n", - " \"outputs.mat\", \n", - " [\"Time [h]\", \"Current [A]\", \"Voltage [V]\", \"Electrolyte concentration [mol.m-3]\"], \n", - " to_format=\"matlab\",\n", - " short_names={\n", - " \"Time [h]\": \"t\", \"Current [A]\": \"I\", \"Voltage [V]\": \"V\", \"Electrolyte concentration [mol.m-3]\": \"c_e\",\n", - " }\n", - ")\n", - "# to a csv file (time-dependent outputs only, no spatial dependence allowed)\n", - "solution.save_data(\n", - " \"outputs.csv\", [\"Time [h]\", \"Current [A]\", \"Voltage [V]\"], to_format=\"csv\"\n", - ")" + "data": { + "text/plain": [ + "dict_keys(['Negative particle surface concentration [mol.m-3]', 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Current [A]', 'Negative electrode potential [V]', 'Electrolyte potential [V]', 'Positive electrode potential [V]', 'Voltage [V]', 'Time [h]'])" ] - }, + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solution.data.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see the data by simply accessing the entries attribute of the processed variable" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Stepping the solver\n", - "\n", - "The previous solution was created in one go with the solve method, but it is also possible to step the solution and look at the results as we go. In doing so, the results are automatically updated at each step." + "data": { + "text/plain": [ + "array([0. , 0.025, 0.05 , 0.075, 0.1 , 0.125, 0.15 , 0.175, 0.2 ,\n", + " 0.225, 0.25 , 0.275, 0.3 , 0.325, 0.35 , 0.375, 0.4 , 0.425,\n", + " 0.45 , 0.475, 0.5 , 0.525, 0.55 , 0.575, 0.6 , 0.625, 0.65 ,\n", + " 0.675, 0.7 , 0.725, 0.75 , 0.775, 0.8 , 0.825, 0.85 , 0.875,\n", + " 0.9 , 0.925, 0.95 , 0.975])" ] - }, + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solution['Time [h]'].entries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also call the method with specified time(s) in SI units of seconds" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "time_in_seconds = np.array([0, 600, 900, 1700, 3000 ])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time 0\n", - "[3.77047806 3.71250693]\n", - "Time 360\n", - "[3.77047806 3.71250693 3.68215218]\n", - "Time 720\n", - "[3.77047806 3.71250693 3.68215218 3.66125574]\n", - "Time 1080\n", - "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942]\n", - "Time 1440\n", - "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857]\n", - "Time 1800\n", - "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n", - " 3.59709451]\n", - "Time 2160\n", - "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n", - " 3.59709451 3.58821334]\n", - "Time 2520\n", - "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n", - " 3.59709451 3.58821334 3.58056055]\n", - "Time 2880\n", - "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n", - " 3.59709451 3.58821334 3.58056055 3.55158694]\n", - "Time 3240\n", - "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n", - " 3.59709451 3.58821334 3.58056055 3.55158694 3.16842636]\n" - ] - } - ], - "source": [ - "dt = 360\n", - "time = 0\n", - "end_time = solution[\"Time [s]\"].entries[-1]\n", - "step_simulation = pybamm.Simulation(model)\n", - "while time < end_time:\n", - " step_solution = step_simulation.step(dt)\n", - " print('Time', time)\n", - " print(step_solution[\"Voltage [V]\"].entries)\n", - " time += dt" + "data": { + "text/plain": [ + "array([0. , 0.16666667, 0.25 , 0.47222222, 0.83333333])" ] - }, + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solution['Time [h]'](time_in_seconds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If the variable has not already been processed it will be created behind the scenes" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can plot the voltages and see that the solutions are the same" + "data": { + "text/plain": [ + "array([298.15, 298.15, 298.15, 298.15, 298.15])" ] - }, + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "var = 'X-averaged negative electrode temperature [K]'\n", + "solution[var](time_in_seconds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example the simulation was isothermal, so the temperature remains unchanged." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Saving the solution\n", + "\n", + "The solution can be saved in a number of ways:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# to a pickle file (default)\n", + "solution.save_data(\n", + " \"outputs.pickle\", [\"Time [h]\", \"Current [A]\", \"Voltage [V]\", \"Electrolyte concentration [mol.m-3]\"]\n", + ")\n", + "# to a matlab file\n", + "# need to give variable names without space\n", + "solution.save_data(\n", + " \"outputs.mat\", \n", + " [\"Time [h]\", \"Current [A]\", \"Voltage [V]\", \"Electrolyte concentration [mol.m-3]\"], \n", + " to_format=\"matlab\",\n", + " short_names={\n", + " \"Time [h]\": \"t\", \"Current [A]\": \"I\", \"Voltage [V]\": \"V\", \"Electrolyte concentration [mol.m-3]\": \"c_e\",\n", + " }\n", + ")\n", + "# to a csv file (time-dependent outputs only, no spatial dependence allowed)\n", + "solution.save_data(\n", + " \"outputs.csv\", [\"Time [h]\", \"Current [A]\", \"Voltage [V]\"], to_format=\"csv\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stepping the solver\n", + "\n", + "The previous solution was created in one go with the solve method, but it is also possible to step the solution and look at the results as we go. In doing so, the results are automatically updated at each step." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "voltage = solution[\"Voltage [V]\"].entries\n", - "step_voltage = step_solution[\"Voltage [V]\"].entries\n", - "plt.figure()\n", - "plt.plot(solution[\"Time [h]\"].entries, voltage, \"b-\", label=\"SPMe (continuous solve)\")\n", - "plt.plot(\n", - " step_solution[\"Time [h]\"].entries, step_voltage, \"ro\", label=\"SPMe (stepped solve)\"\n", - ")\n", - "plt.legend()" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Time 0\n", + "[3.77047806 3.71250693]\n", + "Time 360\n", + "[3.77047806 3.71250693 3.68215218]\n", + "Time 720\n", + "[3.77047806 3.71250693 3.68215218 3.66125574]\n", + "Time 1080\n", + "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942]\n", + "Time 1440\n", + "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857]\n", + "Time 1800\n", + "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n", + " 3.59709451]\n", + "Time 2160\n", + "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n", + " 3.59709451 3.58821334]\n", + "Time 2520\n", + "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n", + " 3.59709451 3.58821334 3.58056055]\n", + "Time 2880\n", + "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n", + " 3.59709451 3.58821334 3.58056055 3.55158694]\n", + "Time 3240\n", + "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n", + " 3.59709451 3.58821334 3.58056055 3.55158694 3.16842636]\n" + ] + } + ], + "source": [ + "dt = 360\n", + "time = 0\n", + "end_time = solution[\"Time [s]\"].entries[-1]\n", + "step_simulation = pybamm.Simulation(model)\n", + "while time < end_time:\n", + " step_solution = step_simulation.step(dt)\n", + " print('Time', time)\n", + " print(step_solution[\"Voltage [V]\"].entries)\n", + " time += dt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot the voltages and see that the solutions are the same" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "The relevant papers for this notebook are:" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": 16, - "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] 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", - "\n" - ] - } - ], - "source": [ - "pybamm.print_citations()" + "data": { + "image/png": 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\n", + "text/plain": [ + "
" ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" + ], + "source": [ + "voltage = solution[\"Voltage [V]\"].entries\n", + "step_voltage = step_solution[\"Voltage [V]\"].entries\n", + "plt.figure()\n", + "plt.plot(solution[\"Time [h]\"].entries, voltage, \"b-\", label=\"SPMe (continuous solve)\")\n", + "plt.plot(\n", + " step_solution[\"Time [h]\"].entries, step_voltage, \"ro\", label=\"SPMe (stepped solve)\"\n", + ")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "source": [ + "As a final step, we will clean up the output files created by this notebook:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "os.remove(\"outputs.csv\")\n", + "os.remove(\"outputs.mat\")\n", + "os.remove(\"outputs.pickle\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "The relevant papers for this notebook are:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "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] 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" + ] } + ], + "source": [ + "pybamm.print_citations()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 2 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } From eeb08564e4303a5289ec17edf96ebe945f7c971b Mon Sep 17 00:00:00 2001 From: Akhilender Bongirwar <112749383+akhilender-bongirwar@users.noreply.github.com> Date: Wed, 11 Oct 2023 15:54:50 +0530 Subject: [PATCH 25/75] Updated noxfile.py - Removed redundant code Co-authored-by: Saransh Chopra --- noxfile.py | 1 - 1 file changed, 1 deletion(-) diff --git a/noxfile.py b/noxfile.py index 9d13656894..b9cc6366e2 100644 --- a/noxfile.py +++ b/noxfile.py @@ -121,7 +121,6 @@ def set_dev(session): session.install("virtualenv", "cmake") session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True) python = os.fsdecode(VENV_DIR.joinpath("bin/python")) - session.run(python, "-m", "pip", "install", "-e", ".[all,dev]", external=True) if sys.platform == "linux" or sys.platform == "darwin": session.run(python, "-m", "pip", "install", ".[jax,odes]", external=True) From 3cdc937b6f02e9a117e9c8c729e89020ea8a645b Mon Sep 17 00:00:00 2001 From: Akhilender Bongirwar <112749383+akhilender-bongirwar@users.noreply.github.com> Date: Wed, 11 Oct 2023 15:55:39 +0530 Subject: [PATCH 26/75] Updated noxfile.py - Added all the installation in a single line Co-authored-by: Saransh Chopra --- noxfile.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/noxfile.py b/noxfile.py index b9cc6366e2..e338ecf7cf 100644 --- a/noxfile.py +++ b/noxfile.py @@ -122,7 +122,7 @@ def set_dev(session): session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True) python = os.fsdecode(VENV_DIR.joinpath("bin/python")) if sys.platform == "linux" or sys.platform == "darwin": - session.run(python, "-m", "pip", "install", ".[jax,odes]", external=True) + session.run(python, "-m", "pip", "install", ".[all,dev,jax,odes]", external=True) From 94e3fdc3f05a4c53ef225533efdbdfd1d48fe7f5 Mon Sep 17 00:00:00 2001 From: Akhilender Bongirwar <112749383+akhilender-bongirwar@users.noreply.github.com> Date: Wed, 11 Oct 2023 15:56:25 +0530 Subject: [PATCH 27/75] Updated noxfile.py - Added else block for efficient running of the code Co-authored-by: Saransh Chopra --- noxfile.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/noxfile.py b/noxfile.py index e338ecf7cf..262af5892a 100644 --- a/noxfile.py +++ b/noxfile.py @@ -123,7 +123,8 @@ def set_dev(session): python = os.fsdecode(VENV_DIR.joinpath("bin/python")) if sys.platform == "linux" or sys.platform == "darwin": session.run(python, "-m", "pip", "install", ".[all,dev,jax,odes]", external=True) - + else: + session.run(python, "-m", "pip", "install", "-e", ".[all,dev]", external=True) @nox.session(name="tests") From ce8e56b52e4598c5183ee2492b5744d16d9299a1 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Wed, 11 Oct 2023 11:42:00 +0100 Subject: [PATCH 28/75] simplify expression of cooling terms in x-lumped thermal models --- .../notebooks/models/pouch-cell-model.ipynb | 12 ++-- pybamm/models/submodels/thermal/lumped.py | 5 +- .../pouch_cell_1D_current_collectors.py | 64 ++++++++----------- .../pouch_cell_2D_current_collectors.py | 47 +++++++------- 4 files changed, 60 insertions(+), 68 deletions(-) diff --git a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb index 8e84374fbe..a9431211af 100644 --- a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb +++ b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb @@ -49,7 +49,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "zsh:1: no matches found: pybamm[plot,cite]\n", + "\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;49m23.1.2\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" ] } @@ -82,7 +86,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/robertwtimms/Documents/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:835: OptionWarning: The 'lumped' thermal option with 'dimensionality' 0 now uses the parameters 'Cell cooling surface area [m2]', 'Cell volume [m3]' and 'Total heat transfer coefficient [W.m-2.K-1]' to compute the cell cooling term, regardless of the value of the the 'cell geometry' option. Please update your parameters accordingly.\n", + "/Users/robertwtimms/Documents/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:910: OptionWarning: The 'lumped' thermal option with 'dimensionality' 0 now uses the parameters 'Cell cooling surface area [m2]', 'Cell volume [m3]' and 'Total heat transfer coefficient [W.m-2.K-1]' to compute the cell cooling term, regardless of the value of the the 'cell geometry' option. Please update your parameters accordingly.\n", " options = BatteryModelOptions(extra_options)\n" ] } @@ -619,7 +623,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -683,7 +687,7 @@ "outputs": [ { "data": { - "image/png": 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" ] diff --git a/pybamm/models/submodels/thermal/lumped.py b/pybamm/models/submodels/thermal/lumped.py index 62c147755b..0f396a3f77 100644 --- a/pybamm/models/submodels/thermal/lumped.py +++ b/pybamm/models/submodels/thermal/lumped.py @@ -56,10 +56,9 @@ def set_rhs(self, variables): # Newton cooling, accounting for surface area to volume ratio cell_surface_area = self.param.A_cooling cell_volume = self.param.V_cell - total_cooling_coefficient = ( - -self.param.h_total * cell_surface_area / cell_volume + Q_cool_vol_av = ( + -self.param.h_total * (T_vol_av - T_amb) * cell_surface_area / cell_volume ) - Q_cool_vol_av = total_cooling_coefficient * (T_vol_av - T_amb) self.rhs = { T_vol_av: (Q_vol_av + Q_cool_vol_av) / self.param.rho_c_p_eff(T_vol_av) 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 a6555170fc..2611dbafdc 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 @@ -58,33 +58,29 @@ def set_rhs(self, variables): y = pybamm.standard_spatial_vars.y z = pybamm.standard_spatial_vars.z - # Account for surface area to volume ratio of pouch cell in surface and side - # cooling terms - cell_volume = self.param.L * self.param.L_y * self.param.L_z - + # Calculate cooling, accounting for surface area to volume ratio of pouch cell + edge_area = self.param.L_z * self.param.L yz_surface_area = self.param.L_y * self.param.L_z - yz_surface_cooling_coefficient = ( + cell_volume = self.param.L * self.param.L_y * self.param.L_z + Q_yz_surface = ( -(self.param.n.h_cc(y, z) + self.param.p.h_cc(y, z)) + * (T_av - T_amb) * yz_surface_area / cell_volume ) - - side_edge_area = self.param.L_z * self.param.L - side_edge_cooling_coefficient = ( + Q_edge = ( -(self.param.h_edge(0, z) + self.param.h_edge(self.param.L_y, z)) - * side_edge_area + * (T_av - T_amb) + * edge_area / cell_volume ) - - total_cooling_coefficient = ( - yz_surface_cooling_coefficient + side_edge_cooling_coefficient - ) + Q_cool_total = Q_yz_surface + Q_edge self.rhs = { T_av: ( pybamm.div(self.param.lambda_eff(T_av) * pybamm.grad(T_av)) + Q_av - + total_cooling_coefficient * (T_av - T_amb) + + Q_cool_total ) / self.param.rho_c_p_eff(T_av) } @@ -94,7 +90,7 @@ def set_boundary_conditions(self, variables): T_amb = variables["Ambient temperature [K]"] T_av = variables["X-averaged cell temperature [K]"] - # find tab locations (top vs bottom) + # Find tab locations (top vs bottom) L_y = param.L_y L_z = param.L_z neg_tab_z = param.n.centre_z_tab @@ -104,11 +100,10 @@ def set_boundary_conditions(self, variables): pos_tab_top_bool = pybamm.Equality(pos_tab_z, L_z) pos_tab_bottom_bool = pybamm.Equality(pos_tab_z, 0) - # calculate tab vs non-tab area on top and bottom + # Calculate tab vs non-tab area on top and bottom neg_tab_area = param.n.L_tab * param.n.L_cc pos_tab_area = param.p.L_tab * param.p.L_cc total_area = param.L * param.L_y - non_tab_top_area = ( total_area - neg_tab_area * neg_tab_top_bool @@ -120,18 +115,22 @@ def set_boundary_conditions(self, variables): - pos_tab_area * pos_tab_bottom_bool ) - # calculate effective cooling coefficients + # Calculate heat fluxes weighted by area # Note: can't do y-average of h_edge here since y isn't meshed. Evaluate at # midpoint. - top_cooling_coefficient = ( - param.n.h_tab * neg_tab_area * neg_tab_top_bool - + param.p.h_tab * pos_tab_area * pos_tab_top_bool - + param.h_edge(L_y / 2, L_z) * non_tab_top_area + q_tab_n = -param.n.h_tab * (T_av - T_amb) + q_tab_p = -param.p.h_tab * (T_av - T_amb) + q_edge_top = -param.h_edge(L_y / 2, L_z) * (T_av - T_amb) + q_edge_bottom = -param.h_edge(L_y / 2, 0) * (T_av - T_amb) + q_top = ( + q_tab_n * neg_tab_area * neg_tab_top_bool + + q_tab_p * pos_tab_area * pos_tab_top_bool + + q_edge_top * non_tab_top_area ) / total_area - bottom_cooling_coefficient = ( - param.n.h_tab * neg_tab_area * neg_tab_bottom_bool - + param.p.h_tab * pos_tab_area * pos_tab_bottom_bool - + param.h_edge(L_y / 2, 0) * non_tab_bottom_area + q_bottom = ( + q_tab_n * neg_tab_area * neg_tab_bottom_bool + + q_tab_p * pos_tab_area * pos_tab_bottom_bool + + q_edge_bottom * non_tab_bottom_area ) / total_area # just use left and right for clarity @@ -141,21 +140,14 @@ def set_boundary_conditions(self, variables): self.boundary_conditions = { T_av: { "left": ( - pybamm.boundary_value( - bottom_cooling_coefficient * (T_av - T_amb), - "left", - ) - / pybamm.boundary_value(lambda_eff, "left"), + pybamm.boundary_value(-q_bottom / lambda_eff, "left"), "Neumann", ), "right": ( - pybamm.boundary_value( - -top_cooling_coefficient * (T_av - T_amb), "right" - ) - / pybamm.boundary_value(lambda_eff, "right"), + pybamm.boundary_value(q_top / lambda_eff, "right"), "Neumann", ), - } + }, } def set_initial_conditions(self, variables): 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 eb8e1b7e49..a5c7c42b17 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 @@ -58,20 +58,22 @@ def set_rhs(self, variables): y = pybamm.standard_spatial_vars.y z = pybamm.standard_spatial_vars.z + # Calculate cooling + Q_yz_surface_W_per_m2 = -(self.param.n.h_cc(y, z) + self.param.p.h_cc(y, z)) * ( + T_av - T_amb + ) + Q_edge_W_per_m2 = -self.param.h_edge(y, z) * (T_av - T_amb) + # Account for surface area to volume ratio of pouch cell in surface cooling # term - cell_volume = self.param.L * self.param.L_y * self.param.L_z - yz_surface_area = self.param.L_y * self.param.L_z - yz_surface_cooling_coefficient = ( - -(self.param.n.h_cc(y, z) + self.param.p.h_cc(y, z)) - * yz_surface_area - / cell_volume + cell_volume = self.param.L * self.param.L_y * self.param.L_z + Q_yz_surface = pybamm.source( + Q_yz_surface_W_per_m2 * yz_surface_area / cell_volume, T_av ) - # Edge cooling appears as a boundary term, so no need to account for surface # area to volume ratio - edge_cooling_coefficient = -self.param.h_edge(y, z) + Q_edge = pybamm.source(Q_edge_W_per_m2, T_av, boundary=True) # Governing equations contain: # - source term for y-z surface cooling @@ -88,10 +90,8 @@ def set_rhs(self, variables): T_av: ( self.param.lambda_eff(T_av) * pybamm.laplacian(T_av) + pybamm.source(Q_av, T_av) - + pybamm.source(yz_surface_cooling_coefficient * (T_av - T_amb), T_av) - + pybamm.source( - edge_cooling_coefficient * (T_av - T_amb), T_av, boundary=True - ) + + Q_yz_surface + + Q_edge ) / self.param.rho_c_p_eff(T_av) } @@ -102,24 +102,21 @@ def set_boundary_conditions(self, variables): y = pybamm.standard_spatial_vars.y z = pybamm.standard_spatial_vars.z + # Calculate heat fluxes + q_tab_n = -self.param.n.h_tab * (T_av - T_amb) + q_tab_p = -self.param.p.h_tab * (T_av - T_amb) + q_edge = -self.param.h_edge(y, z) * (T_av - T_amb) + # Subtract the edge cooling from the tab portion so as to not double count # Note: tab cooling is also only applied on the current collector hence - # the (l_cn / l) and (l_cp / l) prefactors. We also still have edge cooling + # the (l_cn / l) and (l_cp / l) prefactors. We still have edge cooling # in the region: x in (0, 1) - h_tab_n_corrected = (self.param.n.L_cc / self.param.L) * ( - self.param.n.h_tab - self.param.h_edge(y, z) - ) - h_tab_p_corrected = (self.param.p.L_cc / self.param.L) * ( - self.param.p.h_tab - self.param.h_edge(y, z) - ) - - negative_tab_bc = pybamm.boundary_value( - -h_tab_n_corrected * (T_av - T_amb) / self.param.n.lambda_cc(T_av), + negative_tab_bc = (self.param.n.L_cc / self.param.L) * pybamm.boundary_value( + (q_tab_n - q_edge) / self.param.n.lambda_cc(T_av), "negative tab", ) - positive_tab_bc = pybamm.boundary_value( - -h_tab_p_corrected * (T_av - T_amb) / self.param.p.lambda_cc(T_av), - "positive tab", + positive_tab_bc = (self.param.p.L_cc / self.param.L) * pybamm.boundary_value( + (q_tab_p - q_edge) / self.param.p.lambda_cc(T_av), "positive tab" ) self.boundary_conditions = { From e8610dd3acabaed3185643a9424be9181c647146 Mon Sep 17 00:00:00 2001 From: Akhilender Bongirwar <112749383+akhilender-bongirwar@users.noreply.github.com> Date: Thu, 12 Oct 2023 00:30:57 +0530 Subject: [PATCH 29/75] Update noxfile.py - Altered and removed the darwin platform --- noxfile.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/noxfile.py b/noxfile.py index 262af5892a..3f14381886 100644 --- a/noxfile.py +++ b/noxfile.py @@ -121,7 +121,7 @@ def set_dev(session): session.install("virtualenv", "cmake") session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True) python = os.fsdecode(VENV_DIR.joinpath("bin/python")) - if sys.platform == "linux" or sys.platform == "darwin": + if sys.platform == "linux": session.run(python, "-m", "pip", "install", ".[all,dev,jax,odes]", external=True) else: session.run(python, "-m", "pip", "install", "-e", ".[all,dev]", external=True) From 7e1930cdd129e5cb36e668c18bd93591f01da018 Mon Sep 17 00:00:00 2001 From: martinjrobins Date: Thu, 12 Oct 2023 15:42:30 +0000 Subject: [PATCH 30/75] #3431 fix some vstudio compile errors --- pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP.cpp | 2 +- pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP.cpp b/pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP.cpp index c1c71a967d..ad51eda4e1 100644 --- a/pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP.cpp +++ b/pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP.cpp @@ -275,7 +275,7 @@ Solution CasadiSolverOpenMP::solve( // set inputs auto p_inputs = inputs.unchecked<2>(); - for (uint i = 0; i < functions->inputs.size(); i++) + for (int i = 0; i < functions->inputs.size(); i++) functions->inputs[i] = p_inputs(i, 0); // set initial conditions diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp index 4f31dbe57d..0c188f6304 100644 --- a/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp +++ b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp @@ -219,7 +219,8 @@ int jacobian_casadi(realtype tt, realtype cj, N_Vector yy, N_Vector yp, jac_colptrs[i] = p_jac_times_cjmass_colptrs[i]; } } else if (SUNSparseMatrix_SparseType(JJ) == CSR_MAT) { - realtype newjac[SUNSparseMatrix_NNZ(JJ)]; + const int JJ_nnz = SUNSparseMatrix_NNZ(JJ); + realtype newjac[JJ_nnz]; sunindextype *jac_ptrs = SUNSparseMatrix_IndexPointers(JJ); sunindextype *jac_vals = SUNSparseMatrix_IndexValues(JJ); From 75f87071b42b5430f943c671b511473ac755aa23 Mon Sep 17 00:00:00 2001 From: martinjrobins Date: Thu, 12 Oct 2023 16:29:00 +0000 Subject: [PATCH 31/75] #3431 make nnz a constant --- pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp index 0c188f6304..b3eb5d9f66 100644 --- a/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp +++ b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp @@ -219,8 +219,7 @@ int jacobian_casadi(realtype tt, realtype cj, N_Vector yy, N_Vector yp, jac_colptrs[i] = p_jac_times_cjmass_colptrs[i]; } } else if (SUNSparseMatrix_SparseType(JJ) == CSR_MAT) { - const int JJ_nnz = SUNSparseMatrix_NNZ(JJ); - realtype newjac[JJ_nnz]; + realtype newjac[SM_NNZ_S(JJ)]; sunindextype *jac_ptrs = SUNSparseMatrix_IndexPointers(JJ); sunindextype *jac_vals = SUNSparseMatrix_IndexValues(JJ); From 10e94f7c905bc07f8d83b82b5601a7e2eef35dcd Mon Sep 17 00:00:00 2001 From: martinjrobins Date: Thu, 12 Oct 2023 16:31:43 +0000 Subject: [PATCH 32/75] #3431 revert to SUNSparseMatrix_NNZ --- pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp index b3eb5d9f66..4f31dbe57d 100644 --- a/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp +++ b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp @@ -219,7 +219,7 @@ int jacobian_casadi(realtype tt, realtype cj, N_Vector yy, N_Vector yp, jac_colptrs[i] = p_jac_times_cjmass_colptrs[i]; } } else if (SUNSparseMatrix_SparseType(JJ) == CSR_MAT) { - realtype newjac[SM_NNZ_S(JJ)]; + realtype newjac[SUNSparseMatrix_NNZ(JJ)]; sunindextype *jac_ptrs = SUNSparseMatrix_IndexPointers(JJ); sunindextype *jac_vals = SUNSparseMatrix_IndexValues(JJ); From a064a8a2835acb7cf11e316595799bb9c7df089c Mon Sep 17 00:00:00 2001 From: Valentin Sulzer Date: Thu, 12 Oct 2023 14:17:13 -0400 Subject: [PATCH 33/75] #3428 exchange-current density error --- pybamm/parameters/parameter_values.py | 20 ++++++++++++++++++- .../test_parameters/test_parameter_values.py | 13 ++++++++++++ 2 files changed, 32 insertions(+), 1 deletion(-) diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py index 136d9737aa..e69291035d 100644 --- a/pybamm/parameters/parameter_values.py +++ b/pybamm/parameters/parameter_values.py @@ -119,7 +119,25 @@ def create_from_bpx(filename, target_soc=1): return pybamm.ParameterValues(pybamm_dict) def __getitem__(self, key): - return self._dict_items[key] + try: + return self._dict_items[key] + except KeyError as err: + if ( + "Exchange-current density for lithium metal electrode [A.m-2]" + in err.args[0] + and "Exchange-current density for plating [A.m-2]" in self._dict_items + ): + raise KeyError( + "'Exchange-current density for plating [A.m-2]' has been renamed " + "to 'Exchange-current density for lithium metal electrode [A.m-2]' " + "when referring to the reaction at the surface of a lithium metal " + "electrode. This is to avoid confusion with the exchange-current " + "density for the lithium plating reaction in a porous negative " + "electrode. To avoid this error, change your parameter file to use " + "the new name." + ) + else: + raise err def get(self, key, default=None): """Return item corresponding to key if it exists, otherwise return default""" diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py index c6a4831e86..cc1f954686 100644 --- a/tests/unit/test_parameters/test_parameter_values.py +++ b/tests/unit/test_parameters/test_parameter_values.py @@ -977,6 +977,19 @@ def test_evaluate(self): with self.assertRaises(ValueError): parameter_values.evaluate(y) + def test_exchange_current_density_plating(self): + parameter_values = pybamm.ParameterValues( + {"Exchange-current density for plating [A.m-2]": 1} + ) + param = pybamm.Parameter( + "Exchange-current density for lithium metal electrode [A.m-2]" + ) + with self.assertRaisesRegex( + KeyError, + "referring to the reaction at the surface of a lithium metal electrode", + ): + parameter_values.evaluate(param) + if __name__ == "__main__": print("Add -v for more debug output") From f7e59ad0bcc8c2feebd409eb88d7f92f5746576d Mon Sep 17 00:00:00 2001 From: Akhilender Bongirwar <112749383+akhilender-bongirwar@users.noreply.github.com> Date: Fri, 13 Oct 2023 12:00:29 +0530 Subject: [PATCH 34/75] Updated styles - Made a single line of 89 characters into few smaller lines --- noxfile.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/noxfile.py b/noxfile.py index 3f14381886..615da67ef4 100644 --- a/noxfile.py +++ b/noxfile.py @@ -122,7 +122,13 @@ def set_dev(session): session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True) python = os.fsdecode(VENV_DIR.joinpath("bin/python")) if sys.platform == "linux": - session.run(python, "-m", "pip", "install", ".[all,dev,jax,odes]", external=True) + session.run(python, + "-m", + "pip", + "install", + ".[all,dev,jax,odes]", + external=True, + ) else: session.run(python, "-m", "pip", "install", "-e", ".[all,dev]", external=True) From 762941481d96a74bd0807633dc3fafb10ee673ff Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 13 Oct 2023 06:30:42 +0000 Subject: [PATCH 35/75] style: pre-commit fixes --- noxfile.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/noxfile.py b/noxfile.py index 615da67ef4..430ad59659 100644 --- a/noxfile.py +++ b/noxfile.py @@ -122,11 +122,11 @@ def set_dev(session): session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True) python = os.fsdecode(VENV_DIR.joinpath("bin/python")) if sys.platform == "linux": - session.run(python, - "-m", - "pip", - "install", - ".[all,dev,jax,odes]", + session.run(python, + "-m", + "pip", + "install", + ".[all,dev,jax,odes]", external=True, ) else: From 9c474fb241d924e8a29b172b74ad1d5521589e65 Mon Sep 17 00:00:00 2001 From: John Brittain Date: Fri, 13 Oct 2023 09:31:03 +0100 Subject: [PATCH 36/75] #3431 refactor variable length array to std::vector --- .../solvers/c_solvers/idaklu/casadi_sundials_functions.cpp | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp index 4f31dbe57d..b0ea180641 100644 --- a/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp +++ b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp @@ -219,7 +219,7 @@ int jacobian_casadi(realtype tt, realtype cj, N_Vector yy, N_Vector yp, jac_colptrs[i] = p_jac_times_cjmass_colptrs[i]; } } else if (SUNSparseMatrix_SparseType(JJ) == CSR_MAT) { - realtype newjac[SUNSparseMatrix_NNZ(JJ)]; + std::vector newjac(SUNSparseMatrix_NNZ(JJ)); sunindextype *jac_ptrs = SUNSparseMatrix_IndexPointers(JJ); sunindextype *jac_vals = SUNSparseMatrix_IndexValues(JJ); @@ -229,7 +229,7 @@ int jacobian_casadi(realtype tt, realtype cj, N_Vector yy, N_Vector yp, p_python_functions->jac_times_cjmass.m_arg[2] = p_python_functions->inputs.data(); p_python_functions->jac_times_cjmass.m_arg[3] = &cj; - p_python_functions->jac_times_cjmass.m_res[0] = newjac; + p_python_functions->jac_times_cjmass.m_res[0] = newjac.data(); p_python_functions->jac_times_cjmass(); // convert (casadi's) CSC format to CSR @@ -237,7 +237,7 @@ int jacobian_casadi(realtype tt, realtype cj, N_Vector yy, N_Vector yp, std::remove_pointer_tjac_times_cjmass_rowvals.data())>, std::remove_pointer_t >( - newjac, + newjac.data(), p_python_functions->jac_times_cjmass_rowvals.data(), p_python_functions->jac_times_cjmass_colptrs.data(), jac_data, From dc41fbbc417e19f39b3ebc17efcf12bad34b19b5 Mon Sep 17 00:00:00 2001 From: John Brittain Date: Fri, 13 Oct 2023 10:30:12 +0100 Subject: [PATCH 37/75] another variable length array to std::vector --- pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp b/pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp index d29cb7c961..1a33b957f8 100644 --- a/pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp +++ b/pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp @@ -21,7 +21,7 @@ */ template void csc_csr(const realtype f[], const T1 c[], const T1 r[], realtype nf[], T2 nc[], T2 nr[], int N, int cols) { - int nn[cols+1]; + std::vector nn(cols+1); std::vector rr(N); for (int i=0; i Date: Fri, 13 Oct 2023 11:17:40 +0100 Subject: [PATCH 38/75] Make variable length arrays a compile error in cmake --- CMakeLists.txt | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 38e3d4976c..2a78ee9d62 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -24,7 +24,10 @@ set(CMAKE_CXX_STANDARD_REQUIRED ON) set(CMAKE_CXX_EXTENSIONS OFF) set(CMAKE_EXPORT_COMPILE_COMMANDS 1) set(CMAKE_POSITION_INDEPENDENT_CODE ON) - +if(NOT MSVC) + # MSVC does not support variable length arrays (vla) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Werror=vla") +endif() # casadi seems to compile without the newer versions of std::string add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0) From 9b0df12acc8351f91f8c7d9f68e9443b6ae23199 Mon Sep 17 00:00:00 2001 From: kratman Date: Tue, 3 Oct 2023 13:41:14 -0400 Subject: [PATCH 39/75] Minor cleanup before starting --- benchmarks/different_model_options.py | 1 + benchmarks/memory_unit_benchmarks.py | 6 +++--- benchmarks/time_solve_models.py | 6 ------ benchmarks/unit_benchmarks.py | 6 +++--- tests/testcase.py | 2 +- 5 files changed, 8 insertions(+), 13 deletions(-) diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py index 0e9e7bc23b..406ce3b105 100644 --- a/benchmarks/different_model_options.py +++ b/benchmarks/different_model_options.py @@ -1,5 +1,6 @@ import pybamm import numpy as np +import importlib.util def compute_discretisation(model, param): diff --git a/benchmarks/memory_unit_benchmarks.py b/benchmarks/memory_unit_benchmarks.py index 4b20996b75..867c089236 100644 --- a/benchmarks/memory_unit_benchmarks.py +++ b/benchmarks/memory_unit_benchmarks.py @@ -31,7 +31,7 @@ def mem_create_expression(self): return self.model -class MemParameteriseModel: +class MemParameteriseModel(MemCreateExpression): def setup(self): MemCreateExpression.mem_create_expression(self) @@ -58,7 +58,7 @@ def mem_parameterise(self): return param -class MemDiscretiseModel: +class MemDiscretiseModel(MemParameteriseModel): def setup(self): MemCreateExpression.mem_create_expression(self) MemParameteriseModel.mem_parameterise(self) @@ -76,7 +76,7 @@ def mem_discretise(self): return disc -class MemSolveModel: +class MemSolveModel(MemDiscretiseModel): def setup(self): MemCreateExpression.mem_create_expression(self) MemParameteriseModel.mem_parameterise(self) diff --git a/benchmarks/time_solve_models.py b/benchmarks/time_solve_models.py index f277769497..e17aed824e 100644 --- a/benchmarks/time_solve_models.py +++ b/benchmarks/time_solve_models.py @@ -18,9 +18,7 @@ class TimeSolveSPM: "ORegan2022", "NCA_Kim2011", "Prada2013", - # "Ai2020", "Ramadass2004", - # "Mohtat2020", "Chen2020", "Ecker2015", ], @@ -75,9 +73,7 @@ class TimeSolveSPMe: "ORegan2022", "NCA_Kim2011", "Prada2013", - # "Ai2020", "Ramadass2004", - # "Mohtat2020", "Chen2020", "Ecker2015", ], @@ -130,11 +126,9 @@ class TimeSolveDFN: [ "Marquis2019", "ORegan2022", - # "NCA_Kim2011", "Prada2013", "Ai2020", "Ramadass2004", - # "Mohtat2020", "Chen2020", "Ecker2015", ], diff --git a/benchmarks/unit_benchmarks.py b/benchmarks/unit_benchmarks.py index acee9c210a..9e743e0577 100644 --- a/benchmarks/unit_benchmarks.py +++ b/benchmarks/unit_benchmarks.py @@ -30,7 +30,7 @@ def time_create_expression(self): } -class TimeParameteriseModel: +class TimeParameteriseModel(TimeCreateExpression): def setup(self): TimeCreateExpression.time_create_expression(self) @@ -56,7 +56,7 @@ def time_parameterise(self): param.process_geometry(self.geometry) -class TimeDiscretiseModel: +class TimeDiscretiseModel(TimeParameteriseModel): def setup(self): TimeCreateExpression.time_create_expression(self) TimeParameteriseModel.time_parameterise(self) @@ -73,7 +73,7 @@ def time_discretise(self): disc.process_model(self.model) -class TimeSolveModel: +class TimeSolveModel(TimeDiscretiseModel): def setup(self): TimeCreateExpression.time_create_expression(self) TimeParameteriseModel.time_parameterise(self) diff --git a/tests/testcase.py b/tests/testcase.py index f2daa7ba9a..7438cec241 100644 --- a/tests/testcase.py +++ b/tests/testcase.py @@ -13,7 +13,7 @@ def FixRandomSeed(method): Wraps a method so that the random seed is set to a hash of the method name As the wrapper fixes the random seed before calling the method, tests can - explicitely reinstate the random seed within their method bodies as desired, + explicitly reinstate the random seed within their method bodies as desired, e.g. by calling np.random.seed(None) to restore normal behaviour. Generating a random seed from the method name allows particularly awkward From 64af493ecf710ef534d9d2f1bfc9744e338f03f8 Mon Sep 17 00:00:00 2001 From: kratman Date: Tue, 3 Oct 2023 16:01:31 -0400 Subject: [PATCH 40/75] Adding random seed to the setup functions --- benchmarks/different_model_options.py | 36 ++++++++++++++++++++---- benchmarks/memory_sims.py | 9 ++++++ benchmarks/memory_unit_benchmarks.py | 6 ++++ benchmarks/time_setup_models_and_sims.py | 18 ++++++++++++ benchmarks/time_sims_experiments.py | 1 + benchmarks/time_solve_models.py | 3 ++ benchmarks/unit_benchmarks.py | 6 ++++ pybamm/util.py | 4 +++ 8 files changed, 77 insertions(+), 6 deletions(-) diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py index 406ce3b105..ed21d9728b 100644 --- a/benchmarks/different_model_options.py +++ b/benchmarks/different_model_options.py @@ -83,11 +83,14 @@ class TimeBuildModelLossActiveMaterial: ["none", "stress-driven", "reaction-driven", "stress and reaction-driven"], ) + def setup(self): + pybamm.util.set_random_seed() + def time_setup_model(self, model, params): build_model("Ai2020", model, "loss of active material", params) -class TimeSolveLossActiveMaterial: +class TimeSolveLossActiveMaterial(SolveModel): param_names = ["model", "model option", "solver class"] params = ( [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN], @@ -96,6 +99,7 @@ class TimeSolveLossActiveMaterial: ) def setup(self, model, params, solver_class): + pybamm.util.set_random_seed() SolveModel.solve_setup( self, "Ai2020", model, "loss of active material", params, solver_class ) @@ -111,11 +115,14 @@ class TimeBuildModelLithiumPlating: ["none", "irreversible", "reversible", "partially reversible"], ) + def setup(self): + pybamm.util.set_random_seed() + def time_setup_model(self, model, params): build_model("OKane2022", model, "lithium plating", params) -class TimeSolveLithiumPlating: +class TimeSolveLithiumPlating(SolveModel): param_names = ["model", "model option", "solver class"] params = ( [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN], @@ -124,6 +131,7 @@ class TimeSolveLithiumPlating: ) def setup(self, model, params, solver_class): + pybamm.util.set_random_seed() SolveModel.solve_setup( self, "OKane2022", model, "lithium plating", params, solver_class ) @@ -147,11 +155,14 @@ class TimeBuildModelSEI: ], ) + def setup(self): + pybamm.util.set_random_seed() + def time_setup_model(self, model, params): build_model("Marquis2019", model, "SEI", params) -class TimeSolveSEI: +class TimeSolveSEI(SolveModel): param_names = ["model", "model option", "solver class"] params = ( [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN], @@ -168,6 +179,7 @@ class TimeSolveSEI: ) def setup(self, model, params, solver_class): + pybamm.util.set_random_seed() SolveModel.solve_setup(self, "Marquis2019", model, "SEI", params, solver_class) def time_solve_model(self, model, params, solver_class): @@ -186,11 +198,14 @@ class TimeBuildModelParticle: ], ) + def setup(self): + pybamm.util.set_random_seed() + def time_setup_model(self, model, params): build_model("Marquis2019", model, "particle", params) -class TimeSolveParticle: +class TimeSolveParticle(SolveModel): param_names = ["model", "model option", "solver class"] params = ( [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN], @@ -204,6 +219,7 @@ class TimeSolveParticle: ) def setup(self, model, params, solver_class): + pybamm.util.set_random_seed() SolveModel.solve_setup( self, "Marquis2019", model, "particle", params, solver_class ) @@ -219,11 +235,14 @@ class TimeBuildModelThermal: ["isothermal", "lumped", "x-full"], ) + def setup(self): + pybamm.util.set_random_seed() + def time_setup_model(self, model, params): build_model("Marquis2019", model, "thermal", params) -class TimeSolveThermal: +class TimeSolveThermal(SolveModel): param_names = ["model", "model option", "solver class"] params = ( [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN], @@ -232,6 +251,7 @@ class TimeSolveThermal: ) def setup(self, model, params, solver_class): + pybamm.util.set_random_seed() SolveModel.solve_setup( self, "Marquis2019", model, "thermal", params, solver_class ) @@ -247,11 +267,14 @@ class TimeBuildModelSurfaceForm: ["false", "differential", "algebraic"], ) + def setup(self): + pybamm.util.set_random_seed() + def time_setup_model(self, model, params): build_model("Marquis2019", model, "surface form", params) -class TimeSolveSurfaceForm: +class TimeSolveSurfaceForm(SolveModel): param_names = ["model", "model option", "solver class"] params = ( [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN], @@ -260,6 +283,7 @@ class TimeSolveSurfaceForm: ) def setup(self, model, params, solver_class): + pybamm.util.set_random_seed() if (model, params, solver_class) == ( pybamm.lithium_ion.SPM, "differential", diff --git a/benchmarks/memory_sims.py b/benchmarks/memory_sims.py index 1857873476..c58c005733 100644 --- a/benchmarks/memory_sims.py +++ b/benchmarks/memory_sims.py @@ -7,6 +7,9 @@ class MemSPMSimulationCCCV: param_names = ["parameter"] params = parameters + def setup(self): + pybamm.util.set_random_seed() + def mem_setup_SPM_simulationCCCV(self, parameters): self.param = pybamm.ParameterValues(parameters) self.model = pybamm.lithium_ion.SPM() @@ -51,6 +54,9 @@ class MemSPMSimulationGITT: param_names = ["parameter"] params = parameters + def setup(self): + pybamm.util.set_random_seed() + def mem_setup_SPM_simulationGITT(self, parameters): self.param = pybamm.ParameterValues(parameters) self.model = pybamm.lithium_ion.SPM() @@ -67,6 +73,9 @@ class MemDFNSimulationGITT: param_names = ["parameter"] params = parameters + def setup(self): + pybamm.util.set_random_seed() + def mem_setup_DFN_simulationGITT(self, parameters): self.param = pybamm.ParameterValues(parameters) self.model = pybamm.lithium_ion.SPM() diff --git a/benchmarks/memory_unit_benchmarks.py b/benchmarks/memory_unit_benchmarks.py index 867c089236..55b7715c18 100644 --- a/benchmarks/memory_unit_benchmarks.py +++ b/benchmarks/memory_unit_benchmarks.py @@ -3,6 +3,9 @@ class MemCreateExpression: + def setup(self): + pybamm.util.set_random_seed() + def mem_create_expression(self): self.R = pybamm.Parameter("Particle radius [m]") D = pybamm.Parameter("Diffusion coefficient [m2.s-1]") @@ -33,6 +36,7 @@ def mem_create_expression(self): class MemParameteriseModel(MemCreateExpression): def setup(self): + pybamm.util.set_random_seed() MemCreateExpression.mem_create_expression(self) def mem_parameterise(self): @@ -60,6 +64,7 @@ def mem_parameterise(self): class MemDiscretiseModel(MemParameteriseModel): def setup(self): + pybamm.util.set_random_seed() MemCreateExpression.mem_create_expression(self) MemParameteriseModel.mem_parameterise(self) @@ -78,6 +83,7 @@ def mem_discretise(self): class MemSolveModel(MemDiscretiseModel): def setup(self): + pybamm.util.set_random_seed() MemCreateExpression.mem_create_expression(self) MemParameteriseModel.mem_parameterise(self) MemDiscretiseModel.mem_discretise(self) diff --git a/benchmarks/time_setup_models_and_sims.py b/benchmarks/time_setup_models_and_sims.py index 4e9b2423a1..4f77378214 100644 --- a/benchmarks/time_setup_models_and_sims.py +++ b/benchmarks/time_setup_models_and_sims.py @@ -34,6 +34,9 @@ class TimeBuildSPM: param_names = ["parameter"] params = parameters + def setup(self): + pybamm.util.set_random_seed() + def time_setup_SPM(self, parameters): self.param = pybamm.ParameterValues(parameters) self.model = pybamm.lithium_ion.SPM() @@ -45,6 +48,9 @@ class TimeBuildSPMe: param_names = ["parameter"] params = parameters + def setup(self): + pybamm.util.set_random_seed() + def time_setup_SPMe(self, parameters): self.param = pybamm.ParameterValues(parameters) self.model = pybamm.lithium_ion.SPMe() @@ -56,6 +62,9 @@ class TimeBuildDFN: param_names = ["parameter"] params = parameters + def setup(self): + pybamm.util.set_random_seed() + def time_setup_DFN(self, parameters): self.param = pybamm.ParameterValues(parameters) self.model = pybamm.lithium_ion.DFN() @@ -67,6 +76,9 @@ class TimeBuildSPMSimulation: param_names = ["with experiment", "parameter"] params = ([False, True], parameters) + def setup(self): + pybamm.util.set_random_seed() + def time_setup_SPM_simulation(self, with_experiment, parameters): self.param = pybamm.ParameterValues(parameters) self.model = pybamm.lithium_ion.SPM() @@ -85,6 +97,9 @@ class TimeBuildSPMeSimulation: param_names = ["with experiment", "parameter"] params = ([False, True], parameters) + def setup(self): + pybamm.util.set_random_seed() + def time_setup_SPMe_simulation(self, with_experiment, parameters): self.param = pybamm.ParameterValues(parameters) self.model = pybamm.lithium_ion.SPMe() @@ -103,6 +118,9 @@ class TimeBuildDFNSimulation: param_names = ["with experiment", "parameter"] params = ([False, True], parameters) + def setup(self): + pybamm.util.set_random_seed() + def time_setup_DFN_simulation(self, with_experiment, parameters): self.param = pybamm.ParameterValues(parameters) self.model = pybamm.lithium_ion.DFN() diff --git a/benchmarks/time_sims_experiments.py b/benchmarks/time_sims_experiments.py index 5e05470734..c82f4eabca 100644 --- a/benchmarks/time_sims_experiments.py +++ b/benchmarks/time_sims_experiments.py @@ -21,6 +21,7 @@ class TimeSimulation: } def setup(self, experiment, parameters, model_class, solver_class): + pybamm.util.set_random_seed() if (experiment, parameters, model_class, solver_class) == ( "GITT", "Marquis2019", diff --git a/benchmarks/time_solve_models.py b/benchmarks/time_solve_models.py index e17aed824e..7f64e92553 100644 --- a/benchmarks/time_solve_models.py +++ b/benchmarks/time_solve_models.py @@ -29,6 +29,7 @@ class TimeSolveSPM: ) def setup(self, solve_first, parameters, solver_class): + pybamm.util.set_random_seed() self.solver = solver_class() self.model = pybamm.lithium_ion.SPM() c_rate = 1 @@ -84,6 +85,7 @@ class TimeSolveSPMe: ) def setup(self, solve_first, parameters, solver_class): + pybamm.util.set_random_seed() self.solver = solver_class() self.model = pybamm.lithium_ion.SPMe() c_rate = 1 @@ -139,6 +141,7 @@ class TimeSolveDFN: ) def setup(self, solve_first, parameters, solver_class): + pybamm.util.set_random_seed() if (parameters, solver_class) == ( "ORegan2022", pybamm.CasadiSolver, diff --git a/benchmarks/unit_benchmarks.py b/benchmarks/unit_benchmarks.py index 9e743e0577..0af55a1bd8 100644 --- a/benchmarks/unit_benchmarks.py +++ b/benchmarks/unit_benchmarks.py @@ -3,6 +3,9 @@ class TimeCreateExpression: + def setup(self): + pybamm.util.set_random_seed() + def time_create_expression(self): self.R = pybamm.Parameter("Particle radius [m]") D = pybamm.Parameter("Diffusion coefficient [m2.s-1]") @@ -32,6 +35,7 @@ def time_create_expression(self): class TimeParameteriseModel(TimeCreateExpression): def setup(self): + pybamm.util.set_random_seed() TimeCreateExpression.time_create_expression(self) def time_parameterise(self): @@ -58,6 +62,7 @@ def time_parameterise(self): class TimeDiscretiseModel(TimeParameteriseModel): def setup(self): + pybamm.util.set_random_seed() TimeCreateExpression.time_create_expression(self) TimeParameteriseModel.time_parameterise(self) @@ -75,6 +80,7 @@ def time_discretise(self): class TimeSolveModel(TimeDiscretiseModel): def setup(self): + pybamm.util.set_random_seed() TimeCreateExpression.time_create_expression(self) TimeParameteriseModel.time_parameterise(self) TimeDiscretiseModel.time_discretise(self) diff --git a/pybamm/util.py b/pybamm/util.py index 562352bfac..48819f118d 100644 --- a/pybamm/util.py +++ b/pybamm/util.py @@ -27,6 +27,10 @@ JAXLIB_VERSION = "0.4" +def set_random_seed(seed_value=42): + np.random.seed(seed_value) + + def root_dir(): """return the root directory of the PyBaMM install directory""" return str(pathlib.Path(pybamm.__path__[0]).parent) From 6adee6bbf1d306cb6dcf24cbb9a5f71745b4dc88 Mon Sep 17 00:00:00 2001 From: kratman Date: Tue, 3 Oct 2023 16:03:13 -0400 Subject: [PATCH 41/75] Pre-commit changes --- benchmarks/different_model_options.py | 1 - 1 file changed, 1 deletion(-) diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py index ed21d9728b..b3d5a19488 100644 --- a/benchmarks/different_model_options.py +++ b/benchmarks/different_model_options.py @@ -1,6 +1,5 @@ import pybamm import numpy as np -import importlib.util def compute_discretisation(model, param): From fb44d43af2f73102c2a86625f4ce55b836388379 Mon Sep 17 00:00:00 2001 From: kratman Date: Tue, 3 Oct 2023 16:29:30 -0400 Subject: [PATCH 42/75] Make unit tests use a common seed function --- tests/testcase.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tests/testcase.py b/tests/testcase.py index 7438cec241..3d931659bd 100644 --- a/tests/testcase.py +++ b/tests/testcase.py @@ -3,10 +3,11 @@ # import unittest import hashlib -import numpy as np from functools import wraps from types import FunctionType +import pybamm + def FixRandomSeed(method): """ @@ -23,7 +24,7 @@ def FixRandomSeed(method): @wraps(method) def wrapped(*args, **kwargs): - np.random.seed( + pybamm.util.set_random_seed( int(hashlib.sha256(method.__name__.encode()).hexdigest(), 16) % (2**32) ) return method(*args, **kwargs) From cbb0d209f40f7b43e04bf06762eac98be124921f Mon Sep 17 00:00:00 2001 From: kratman Date: Wed, 4 Oct 2023 13:37:26 -0400 Subject: [PATCH 43/75] Change imports --- benchmarks/different_model_options.py | 1 + benchmarks/memory_sims.py | 1 + benchmarks/memory_unit_benchmarks.py | 1 + benchmarks/time_setup_models_and_sims.py | 1 + benchmarks/time_sims_experiments.py | 1 + benchmarks/time_solve_models.py | 1 + benchmarks/unit_benchmarks.py | 1 + 7 files changed, 7 insertions(+) diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py index b3d5a19488..218dfaa4fb 100644 --- a/benchmarks/different_model_options.py +++ b/benchmarks/different_model_options.py @@ -1,4 +1,5 @@ import pybamm +import pybamm.util import numpy as np diff --git a/benchmarks/memory_sims.py b/benchmarks/memory_sims.py index c58c005733..c0873ed2b9 100644 --- a/benchmarks/memory_sims.py +++ b/benchmarks/memory_sims.py @@ -1,4 +1,5 @@ import pybamm +import pybamm.util parameters = ["Marquis2019", "Chen2020"] diff --git a/benchmarks/memory_unit_benchmarks.py b/benchmarks/memory_unit_benchmarks.py index 55b7715c18..bbef0dbf75 100644 --- a/benchmarks/memory_unit_benchmarks.py +++ b/benchmarks/memory_unit_benchmarks.py @@ -1,4 +1,5 @@ import pybamm +import pybamm.util import numpy as np diff --git a/benchmarks/time_setup_models_and_sims.py b/benchmarks/time_setup_models_and_sims.py index 4f77378214..9ff3a2c50d 100644 --- a/benchmarks/time_setup_models_and_sims.py +++ b/benchmarks/time_setup_models_and_sims.py @@ -1,4 +1,5 @@ import pybamm +import pybamm.util parameters = [ "Marquis2019", diff --git a/benchmarks/time_sims_experiments.py b/benchmarks/time_sims_experiments.py index c82f4eabca..16dfb4d488 100644 --- a/benchmarks/time_sims_experiments.py +++ b/benchmarks/time_sims_experiments.py @@ -1,4 +1,5 @@ import pybamm +import pybamm.util class TimeSimulation: diff --git a/benchmarks/time_solve_models.py b/benchmarks/time_solve_models.py index 7f64e92553..7472100b07 100644 --- a/benchmarks/time_solve_models.py +++ b/benchmarks/time_solve_models.py @@ -2,6 +2,7 @@ # See "Writing benchmarks" in the asv docs for more information. import pybamm +import pybamm.util import numpy as np diff --git a/benchmarks/unit_benchmarks.py b/benchmarks/unit_benchmarks.py index 0af55a1bd8..8342b02a18 100644 --- a/benchmarks/unit_benchmarks.py +++ b/benchmarks/unit_benchmarks.py @@ -1,4 +1,5 @@ import pybamm +import pybamm.util import numpy as np From a18cf42d782450986416b94ad912e4a351e59741 Mon Sep 17 00:00:00 2001 From: kratman Date: Wed, 4 Oct 2023 13:56:56 -0400 Subject: [PATCH 44/75] Changing imports again --- benchmarks/different_model_options.py | 26 ++++++++++++------------ benchmarks/memory_sims.py | 8 ++++---- benchmarks/memory_unit_benchmarks.py | 10 ++++----- benchmarks/time_setup_models_and_sims.py | 14 ++++++------- benchmarks/time_sims_experiments.py | 4 ++-- benchmarks/time_solve_models.py | 8 ++++---- benchmarks/unit_benchmarks.py | 10 ++++----- 7 files changed, 40 insertions(+), 40 deletions(-) diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py index 218dfaa4fb..cc1b2e0191 100644 --- a/benchmarks/different_model_options.py +++ b/benchmarks/different_model_options.py @@ -1,5 +1,5 @@ import pybamm -import pybamm.util +from pybamm.util import set_random_seed import numpy as np @@ -84,7 +84,7 @@ class TimeBuildModelLossActiveMaterial: ) def setup(self): - pybamm.util.set_random_seed() + set_random_seed() def time_setup_model(self, model, params): build_model("Ai2020", model, "loss of active material", params) @@ -99,7 +99,7 @@ class TimeSolveLossActiveMaterial(SolveModel): ) def setup(self, model, params, solver_class): - pybamm.util.set_random_seed() + set_random_seed() SolveModel.solve_setup( self, "Ai2020", model, "loss of active material", params, solver_class ) @@ -116,7 +116,7 @@ class TimeBuildModelLithiumPlating: ) def setup(self): - pybamm.util.set_random_seed() + set_random_seed() def time_setup_model(self, model, params): build_model("OKane2022", model, "lithium plating", params) @@ -131,7 +131,7 @@ class TimeSolveLithiumPlating(SolveModel): ) def setup(self, model, params, solver_class): - pybamm.util.set_random_seed() + set_random_seed() SolveModel.solve_setup( self, "OKane2022", model, "lithium plating", params, solver_class ) @@ -156,7 +156,7 @@ class TimeBuildModelSEI: ) def setup(self): - pybamm.util.set_random_seed() + set_random_seed() def time_setup_model(self, model, params): build_model("Marquis2019", model, "SEI", params) @@ -179,7 +179,7 @@ class TimeSolveSEI(SolveModel): ) def setup(self, model, params, solver_class): - pybamm.util.set_random_seed() + set_random_seed() SolveModel.solve_setup(self, "Marquis2019", model, "SEI", params, solver_class) def time_solve_model(self, model, params, solver_class): @@ -199,7 +199,7 @@ class TimeBuildModelParticle: ) def setup(self): - pybamm.util.set_random_seed() + set_random_seed() def time_setup_model(self, model, params): build_model("Marquis2019", model, "particle", params) @@ -219,7 +219,7 @@ class TimeSolveParticle(SolveModel): ) def setup(self, model, params, solver_class): - pybamm.util.set_random_seed() + set_random_seed() SolveModel.solve_setup( self, "Marquis2019", model, "particle", params, solver_class ) @@ -236,7 +236,7 @@ class TimeBuildModelThermal: ) def setup(self): - pybamm.util.set_random_seed() + set_random_seed() def time_setup_model(self, model, params): build_model("Marquis2019", model, "thermal", params) @@ -251,7 +251,7 @@ class TimeSolveThermal(SolveModel): ) def setup(self, model, params, solver_class): - pybamm.util.set_random_seed() + set_random_seed() SolveModel.solve_setup( self, "Marquis2019", model, "thermal", params, solver_class ) @@ -268,7 +268,7 @@ class TimeBuildModelSurfaceForm: ) def setup(self): - pybamm.util.set_random_seed() + set_random_seed() def time_setup_model(self, model, params): build_model("Marquis2019", model, "surface form", params) @@ -283,7 +283,7 @@ class TimeSolveSurfaceForm(SolveModel): ) def setup(self, model, params, solver_class): - pybamm.util.set_random_seed() + set_random_seed() if (model, params, solver_class) == ( pybamm.lithium_ion.SPM, "differential", diff --git a/benchmarks/memory_sims.py b/benchmarks/memory_sims.py index c0873ed2b9..edee2c9f11 100644 --- a/benchmarks/memory_sims.py +++ b/benchmarks/memory_sims.py @@ -1,5 +1,5 @@ import pybamm -import pybamm.util +from pybamm.util import set_random_seed parameters = ["Marquis2019", "Chen2020"] @@ -9,7 +9,7 @@ class MemSPMSimulationCCCV: params = parameters def setup(self): - pybamm.util.set_random_seed() + set_random_seed() def mem_setup_SPM_simulationCCCV(self, parameters): self.param = pybamm.ParameterValues(parameters) @@ -56,7 +56,7 @@ class MemSPMSimulationGITT: params = parameters def setup(self): - pybamm.util.set_random_seed() + set_random_seed() def mem_setup_SPM_simulationGITT(self, parameters): self.param = pybamm.ParameterValues(parameters) @@ -75,7 +75,7 @@ class MemDFNSimulationGITT: params = parameters def setup(self): - pybamm.util.set_random_seed() + set_random_seed() def mem_setup_DFN_simulationGITT(self, parameters): self.param = pybamm.ParameterValues(parameters) diff --git a/benchmarks/memory_unit_benchmarks.py b/benchmarks/memory_unit_benchmarks.py index bbef0dbf75..218ff8ea0a 100644 --- a/benchmarks/memory_unit_benchmarks.py +++ b/benchmarks/memory_unit_benchmarks.py @@ -1,11 +1,11 @@ import pybamm -import pybamm.util +from pybamm.util import set_random_seed import numpy as np class MemCreateExpression: def setup(self): - pybamm.util.set_random_seed() + set_random_seed() def mem_create_expression(self): self.R = pybamm.Parameter("Particle radius [m]") @@ -37,7 +37,7 @@ def mem_create_expression(self): class MemParameteriseModel(MemCreateExpression): def setup(self): - pybamm.util.set_random_seed() + set_random_seed() MemCreateExpression.mem_create_expression(self) def mem_parameterise(self): @@ -65,7 +65,7 @@ def mem_parameterise(self): class MemDiscretiseModel(MemParameteriseModel): def setup(self): - pybamm.util.set_random_seed() + set_random_seed() MemCreateExpression.mem_create_expression(self) MemParameteriseModel.mem_parameterise(self) @@ -84,7 +84,7 @@ def mem_discretise(self): class MemSolveModel(MemDiscretiseModel): def setup(self): - pybamm.util.set_random_seed() + set_random_seed() MemCreateExpression.mem_create_expression(self) MemParameteriseModel.mem_parameterise(self) MemDiscretiseModel.mem_discretise(self) diff --git a/benchmarks/time_setup_models_and_sims.py b/benchmarks/time_setup_models_and_sims.py index 9ff3a2c50d..96c61da436 100644 --- a/benchmarks/time_setup_models_and_sims.py +++ b/benchmarks/time_setup_models_and_sims.py @@ -1,5 +1,5 @@ import pybamm -import pybamm.util +from pybamm.util import set_random_seed parameters = [ "Marquis2019", @@ -36,7 +36,7 @@ class TimeBuildSPM: params = parameters def setup(self): - pybamm.util.set_random_seed() + set_random_seed() def time_setup_SPM(self, parameters): self.param = pybamm.ParameterValues(parameters) @@ -50,7 +50,7 @@ class TimeBuildSPMe: params = parameters def setup(self): - pybamm.util.set_random_seed() + set_random_seed() def time_setup_SPMe(self, parameters): self.param = pybamm.ParameterValues(parameters) @@ -64,7 +64,7 @@ class TimeBuildDFN: params = parameters def setup(self): - pybamm.util.set_random_seed() + set_random_seed() def time_setup_DFN(self, parameters): self.param = pybamm.ParameterValues(parameters) @@ -78,7 +78,7 @@ class TimeBuildSPMSimulation: params = ([False, True], parameters) def setup(self): - pybamm.util.set_random_seed() + set_random_seed() def time_setup_SPM_simulation(self, with_experiment, parameters): self.param = pybamm.ParameterValues(parameters) @@ -99,7 +99,7 @@ class TimeBuildSPMeSimulation: params = ([False, True], parameters) def setup(self): - pybamm.util.set_random_seed() + set_random_seed() def time_setup_SPMe_simulation(self, with_experiment, parameters): self.param = pybamm.ParameterValues(parameters) @@ -120,7 +120,7 @@ class TimeBuildDFNSimulation: params = ([False, True], parameters) def setup(self): - pybamm.util.set_random_seed() + set_random_seed() def time_setup_DFN_simulation(self, with_experiment, parameters): self.param = pybamm.ParameterValues(parameters) diff --git a/benchmarks/time_sims_experiments.py b/benchmarks/time_sims_experiments.py index 16dfb4d488..a8a8476939 100644 --- a/benchmarks/time_sims_experiments.py +++ b/benchmarks/time_sims_experiments.py @@ -1,5 +1,5 @@ import pybamm -import pybamm.util +from pybamm.util import set_random_seed class TimeSimulation: @@ -22,7 +22,7 @@ class TimeSimulation: } def setup(self, experiment, parameters, model_class, solver_class): - pybamm.util.set_random_seed() + set_random_seed() if (experiment, parameters, model_class, solver_class) == ( "GITT", "Marquis2019", diff --git a/benchmarks/time_solve_models.py b/benchmarks/time_solve_models.py index 7472100b07..79706477d0 100644 --- a/benchmarks/time_solve_models.py +++ b/benchmarks/time_solve_models.py @@ -2,7 +2,7 @@ # See "Writing benchmarks" in the asv docs for more information. import pybamm -import pybamm.util +from pybamm.util import set_random_seed import numpy as np @@ -30,7 +30,7 @@ class TimeSolveSPM: ) def setup(self, solve_first, parameters, solver_class): - pybamm.util.set_random_seed() + set_random_seed() self.solver = solver_class() self.model = pybamm.lithium_ion.SPM() c_rate = 1 @@ -86,7 +86,7 @@ class TimeSolveSPMe: ) def setup(self, solve_first, parameters, solver_class): - pybamm.util.set_random_seed() + set_random_seed() self.solver = solver_class() self.model = pybamm.lithium_ion.SPMe() c_rate = 1 @@ -142,7 +142,7 @@ class TimeSolveDFN: ) def setup(self, solve_first, parameters, solver_class): - pybamm.util.set_random_seed() + set_random_seed() if (parameters, solver_class) == ( "ORegan2022", pybamm.CasadiSolver, diff --git a/benchmarks/unit_benchmarks.py b/benchmarks/unit_benchmarks.py index 8342b02a18..8995f9a7b8 100644 --- a/benchmarks/unit_benchmarks.py +++ b/benchmarks/unit_benchmarks.py @@ -1,11 +1,11 @@ import pybamm -import pybamm.util +from pybamm.util import set_random_seed import numpy as np class TimeCreateExpression: def setup(self): - pybamm.util.set_random_seed() + set_random_seed() def time_create_expression(self): self.R = pybamm.Parameter("Particle radius [m]") @@ -36,7 +36,7 @@ def time_create_expression(self): class TimeParameteriseModel(TimeCreateExpression): def setup(self): - pybamm.util.set_random_seed() + set_random_seed() TimeCreateExpression.time_create_expression(self) def time_parameterise(self): @@ -63,7 +63,7 @@ def time_parameterise(self): class TimeDiscretiseModel(TimeParameteriseModel): def setup(self): - pybamm.util.set_random_seed() + set_random_seed() TimeCreateExpression.time_create_expression(self) TimeParameteriseModel.time_parameterise(self) @@ -81,7 +81,7 @@ def time_discretise(self): class TimeSolveModel(TimeDiscretiseModel): def setup(self): - pybamm.util.set_random_seed() + set_random_seed() TimeCreateExpression.time_create_expression(self) TimeParameteriseModel.time_parameterise(self) TimeDiscretiseModel.time_discretise(self) From 18699c00595b0ae3cbb392bbc567051cee5813ef Mon Sep 17 00:00:00 2001 From: kratman Date: Wed, 4 Oct 2023 14:02:54 -0400 Subject: [PATCH 45/75] Moving seed function --- benchmarks/benchmark_utils.py | 5 +++++ benchmarks/different_model_options.py | 2 +- benchmarks/memory_sims.py | 2 +- benchmarks/memory_unit_benchmarks.py | 2 +- benchmarks/time_setup_models_and_sims.py | 2 +- benchmarks/time_sims_experiments.py | 2 +- benchmarks/time_solve_models.py | 2 +- benchmarks/unit_benchmarks.py | 2 +- pybamm/util.py | 4 ---- 9 files changed, 12 insertions(+), 11 deletions(-) create mode 100644 benchmarks/benchmark_utils.py diff --git a/benchmarks/benchmark_utils.py b/benchmarks/benchmark_utils.py new file mode 100644 index 0000000000..e5431ff4ea --- /dev/null +++ b/benchmarks/benchmark_utils.py @@ -0,0 +1,5 @@ +import numpy as np + + +def set_random_seed(seed_value=42): + np.random.seed(seed_value) diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py index cc1b2e0191..50755d67c0 100644 --- a/benchmarks/different_model_options.py +++ b/benchmarks/different_model_options.py @@ -1,5 +1,5 @@ import pybamm -from pybamm.util import set_random_seed +from benchmark_utils import set_random_seed import numpy as np diff --git a/benchmarks/memory_sims.py b/benchmarks/memory_sims.py index edee2c9f11..d2e06f545e 100644 --- a/benchmarks/memory_sims.py +++ b/benchmarks/memory_sims.py @@ -1,5 +1,5 @@ import pybamm -from pybamm.util import set_random_seed +from benchmark_utils import set_random_seed parameters = ["Marquis2019", "Chen2020"] diff --git a/benchmarks/memory_unit_benchmarks.py b/benchmarks/memory_unit_benchmarks.py index 218ff8ea0a..6c2014d6a3 100644 --- a/benchmarks/memory_unit_benchmarks.py +++ b/benchmarks/memory_unit_benchmarks.py @@ -1,5 +1,5 @@ import pybamm -from pybamm.util import set_random_seed +from benchmark_utils import set_random_seed import numpy as np diff --git a/benchmarks/time_setup_models_and_sims.py b/benchmarks/time_setup_models_and_sims.py index 96c61da436..d7d5e6c6eb 100644 --- a/benchmarks/time_setup_models_and_sims.py +++ b/benchmarks/time_setup_models_and_sims.py @@ -1,5 +1,5 @@ import pybamm -from pybamm.util import set_random_seed +from benchmark_utils import set_random_seed parameters = [ "Marquis2019", diff --git a/benchmarks/time_sims_experiments.py b/benchmarks/time_sims_experiments.py index a8a8476939..8c0f4348ea 100644 --- a/benchmarks/time_sims_experiments.py +++ b/benchmarks/time_sims_experiments.py @@ -1,5 +1,5 @@ import pybamm -from pybamm.util import set_random_seed +from benchmark_utils import set_random_seed class TimeSimulation: diff --git a/benchmarks/time_solve_models.py b/benchmarks/time_solve_models.py index 79706477d0..feebf6ac09 100644 --- a/benchmarks/time_solve_models.py +++ b/benchmarks/time_solve_models.py @@ -2,7 +2,7 @@ # See "Writing benchmarks" in the asv docs for more information. import pybamm -from pybamm.util import set_random_seed +from benchmark_utils import set_random_seed import numpy as np diff --git a/benchmarks/unit_benchmarks.py b/benchmarks/unit_benchmarks.py index 8995f9a7b8..318c215b73 100644 --- a/benchmarks/unit_benchmarks.py +++ b/benchmarks/unit_benchmarks.py @@ -1,6 +1,6 @@ import pybamm -from pybamm.util import set_random_seed import numpy as np +from benchmark_utils import set_random_seed class TimeCreateExpression: diff --git a/pybamm/util.py b/pybamm/util.py index 48819f118d..562352bfac 100644 --- a/pybamm/util.py +++ b/pybamm/util.py @@ -27,10 +27,6 @@ JAXLIB_VERSION = "0.4" -def set_random_seed(seed_value=42): - np.random.seed(seed_value) - - def root_dir(): """return the root directory of the PyBaMM install directory""" return str(pathlib.Path(pybamm.__path__[0]).parent) From 64602dbc8bfdb125a9fc3b7aac39a10fc7060277 Mon Sep 17 00:00:00 2001 From: kratman Date: Wed, 4 Oct 2023 14:09:41 -0400 Subject: [PATCH 46/75] Moving function again --- benchmarks/different_model_options.py | 2 +- benchmarks/memory_sims.py | 2 +- benchmarks/memory_unit_benchmarks.py | 2 +- benchmarks/time_setup_models_and_sims.py | 2 +- benchmarks/time_sims_experiments.py | 2 +- benchmarks/time_solve_models.py | 2 +- benchmarks/unit_benchmarks.py | 2 +- tests/testcase.py | 5 ++--- 8 files changed, 9 insertions(+), 10 deletions(-) diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py index 50755d67c0..81bf2930f8 100644 --- a/benchmarks/different_model_options.py +++ b/benchmarks/different_model_options.py @@ -1,5 +1,5 @@ import pybamm -from benchmark_utils import set_random_seed +from benchmarks.benchmark_utils import set_random_seed import numpy as np diff --git a/benchmarks/memory_sims.py b/benchmarks/memory_sims.py index d2e06f545e..0c7a030fde 100644 --- a/benchmarks/memory_sims.py +++ b/benchmarks/memory_sims.py @@ -1,5 +1,5 @@ import pybamm -from benchmark_utils import set_random_seed +from benchmarks.benchmark_utils import set_random_seed parameters = ["Marquis2019", "Chen2020"] diff --git a/benchmarks/memory_unit_benchmarks.py b/benchmarks/memory_unit_benchmarks.py index 6c2014d6a3..5f2c360280 100644 --- a/benchmarks/memory_unit_benchmarks.py +++ b/benchmarks/memory_unit_benchmarks.py @@ -1,5 +1,5 @@ import pybamm -from benchmark_utils import set_random_seed +from benchmarks.benchmark_utils import set_random_seed import numpy as np diff --git a/benchmarks/time_setup_models_and_sims.py b/benchmarks/time_setup_models_and_sims.py index d7d5e6c6eb..498086f7f0 100644 --- a/benchmarks/time_setup_models_and_sims.py +++ b/benchmarks/time_setup_models_and_sims.py @@ -1,5 +1,5 @@ import pybamm -from benchmark_utils import set_random_seed +from benchmarks.benchmark_utils import set_random_seed parameters = [ "Marquis2019", diff --git a/benchmarks/time_sims_experiments.py b/benchmarks/time_sims_experiments.py index 8c0f4348ea..13bdc3432e 100644 --- a/benchmarks/time_sims_experiments.py +++ b/benchmarks/time_sims_experiments.py @@ -1,5 +1,5 @@ import pybamm -from benchmark_utils import set_random_seed +from benchmarks.benchmark_utils import set_random_seed class TimeSimulation: diff --git a/benchmarks/time_solve_models.py b/benchmarks/time_solve_models.py index feebf6ac09..15a78dde64 100644 --- a/benchmarks/time_solve_models.py +++ b/benchmarks/time_solve_models.py @@ -2,7 +2,7 @@ # See "Writing benchmarks" in the asv docs for more information. import pybamm -from benchmark_utils import set_random_seed +from benchmarks.benchmark_utils import set_random_seed import numpy as np diff --git a/benchmarks/unit_benchmarks.py b/benchmarks/unit_benchmarks.py index 318c215b73..432fb849b7 100644 --- a/benchmarks/unit_benchmarks.py +++ b/benchmarks/unit_benchmarks.py @@ -1,6 +1,6 @@ import pybamm import numpy as np -from benchmark_utils import set_random_seed +from benchmarks.benchmark_utils import set_random_seed class TimeCreateExpression: diff --git a/tests/testcase.py b/tests/testcase.py index 3d931659bd..ae4019bcb3 100644 --- a/tests/testcase.py +++ b/tests/testcase.py @@ -5,8 +5,7 @@ import hashlib from functools import wraps from types import FunctionType - -import pybamm +import numpy as np def FixRandomSeed(method): @@ -24,7 +23,7 @@ def FixRandomSeed(method): @wraps(method) def wrapped(*args, **kwargs): - pybamm.util.set_random_seed( + np.random.seed( int(hashlib.sha256(method.__name__.encode()).hexdigest(), 16) % (2**32) ) return method(*args, **kwargs) From 0767211a5a932e53ae78411b5cdbe92c7cf8dc7a Mon Sep 17 00:00:00 2001 From: kratman Date: Sat, 14 Oct 2023 21:04:10 -0400 Subject: [PATCH 47/75] Change seed --- benchmarks/benchmark_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/benchmark_utils.py b/benchmarks/benchmark_utils.py index e5431ff4ea..1ccc98280e 100644 --- a/benchmarks/benchmark_utils.py +++ b/benchmarks/benchmark_utils.py @@ -1,5 +1,5 @@ import numpy as np -def set_random_seed(seed_value=42): +def set_random_seed(seed_value=142): np.random.seed(seed_value) From 4e622493a90f5fa7691adbe326246abb23516c6f Mon Sep 17 00:00:00 2001 From: kratman Date: Sat, 14 Oct 2023 21:43:16 -0400 Subject: [PATCH 48/75] Revert some changes --- benchmarks/benchmark_utils.py | 2 +- benchmarks/different_model_options.py | 12 ++++++------ benchmarks/memory_unit_benchmarks.py | 6 +++--- benchmarks/unit_benchmarks.py | 6 +++--- pybamm/solvers/casadi_solver.py | 2 +- 5 files changed, 14 insertions(+), 14 deletions(-) diff --git a/benchmarks/benchmark_utils.py b/benchmarks/benchmark_utils.py index 1ccc98280e..e5431ff4ea 100644 --- a/benchmarks/benchmark_utils.py +++ b/benchmarks/benchmark_utils.py @@ -1,5 +1,5 @@ import numpy as np -def set_random_seed(seed_value=142): +def set_random_seed(seed_value=42): np.random.seed(seed_value) diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py index 81bf2930f8..4a17a4fcef 100644 --- a/benchmarks/different_model_options.py +++ b/benchmarks/different_model_options.py @@ -90,7 +90,7 @@ def time_setup_model(self, model, params): build_model("Ai2020", model, "loss of active material", params) -class TimeSolveLossActiveMaterial(SolveModel): +class TimeSolveLossActiveMaterial: param_names = ["model", "model option", "solver class"] params = ( [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN], @@ -122,7 +122,7 @@ def time_setup_model(self, model, params): build_model("OKane2022", model, "lithium plating", params) -class TimeSolveLithiumPlating(SolveModel): +class TimeSolveLithiumPlating: param_names = ["model", "model option", "solver class"] params = ( [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN], @@ -162,7 +162,7 @@ def time_setup_model(self, model, params): build_model("Marquis2019", model, "SEI", params) -class TimeSolveSEI(SolveModel): +class TimeSolveSEI: param_names = ["model", "model option", "solver class"] params = ( [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN], @@ -205,7 +205,7 @@ def time_setup_model(self, model, params): build_model("Marquis2019", model, "particle", params) -class TimeSolveParticle(SolveModel): +class TimeSolveParticle: param_names = ["model", "model option", "solver class"] params = ( [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN], @@ -242,7 +242,7 @@ def time_setup_model(self, model, params): build_model("Marquis2019", model, "thermal", params) -class TimeSolveThermal(SolveModel): +class TimeSolveThermal: param_names = ["model", "model option", "solver class"] params = ( [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN], @@ -274,7 +274,7 @@ def time_setup_model(self, model, params): build_model("Marquis2019", model, "surface form", params) -class TimeSolveSurfaceForm(SolveModel): +class TimeSolveSurfaceForm: param_names = ["model", "model option", "solver class"] params = ( [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN], diff --git a/benchmarks/memory_unit_benchmarks.py b/benchmarks/memory_unit_benchmarks.py index 5f2c360280..a355442278 100644 --- a/benchmarks/memory_unit_benchmarks.py +++ b/benchmarks/memory_unit_benchmarks.py @@ -35,7 +35,7 @@ def mem_create_expression(self): return self.model -class MemParameteriseModel(MemCreateExpression): +class MemParameteriseModel: def setup(self): set_random_seed() MemCreateExpression.mem_create_expression(self) @@ -63,7 +63,7 @@ def mem_parameterise(self): return param -class MemDiscretiseModel(MemParameteriseModel): +class MemDiscretiseModel: def setup(self): set_random_seed() MemCreateExpression.mem_create_expression(self) @@ -82,7 +82,7 @@ def mem_discretise(self): return disc -class MemSolveModel(MemDiscretiseModel): +class MemSolveModel: def setup(self): set_random_seed() MemCreateExpression.mem_create_expression(self) diff --git a/benchmarks/unit_benchmarks.py b/benchmarks/unit_benchmarks.py index 432fb849b7..dd5c55a5d4 100644 --- a/benchmarks/unit_benchmarks.py +++ b/benchmarks/unit_benchmarks.py @@ -34,7 +34,7 @@ def time_create_expression(self): } -class TimeParameteriseModel(TimeCreateExpression): +class TimeParameteriseModel: def setup(self): set_random_seed() TimeCreateExpression.time_create_expression(self) @@ -61,7 +61,7 @@ def time_parameterise(self): param.process_geometry(self.geometry) -class TimeDiscretiseModel(TimeParameteriseModel): +class TimeDiscretiseModel: def setup(self): set_random_seed() TimeCreateExpression.time_create_expression(self) @@ -79,7 +79,7 @@ def time_discretise(self): disc.process_model(self.model) -class TimeSolveModel(TimeDiscretiseModel): +class TimeSolveModel: def setup(self): set_random_seed() TimeCreateExpression.time_create_expression(self) diff --git a/pybamm/solvers/casadi_solver.py b/pybamm/solvers/casadi_solver.py index 86246588e9..4cf863ede1 100644 --- a/pybamm/solvers/casadi_solver.py +++ b/pybamm/solvers/casadi_solver.py @@ -41,7 +41,7 @@ class CasadiSolver(pybamm.BaseSolver): specified by 'root_method' (e.g. "lm", "hybr", ...) root_tol : float, optional The tolerance for root-finding. Default is 1e-6. - max_step_decrease_counts : float, optional + max_step_decrease_count : float, optional The maximum number of times step size can be decreased before an error is raised. Default is 5. dt_max : float, optional From a8ce937f9d9f9ee490810470604c8d541e6fa9df Mon Sep 17 00:00:00 2001 From: kratman Date: Sat, 14 Oct 2023 22:44:59 -0400 Subject: [PATCH 49/75] Trying to adjust the setup method --- benchmarks/different_model_options.py | 24 ++++++++++++------------ benchmarks/memory_unit_benchmarks.py | 6 +++--- benchmarks/unit_benchmarks.py | 6 +++--- 3 files changed, 18 insertions(+), 18 deletions(-) diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py index 4a17a4fcef..6a1ca22448 100644 --- a/benchmarks/different_model_options.py +++ b/benchmarks/different_model_options.py @@ -83,14 +83,14 @@ class TimeBuildModelLossActiveMaterial: ["none", "stress-driven", "reaction-driven", "stress and reaction-driven"], ) - def setup(self): + def setup(self, model, params, solver_class): set_random_seed() def time_setup_model(self, model, params): build_model("Ai2020", model, "loss of active material", params) -class TimeSolveLossActiveMaterial: +class TimeSolveLossActiveMaterial(SolveModel): param_names = ["model", "model option", "solver class"] params = ( [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN], @@ -115,14 +115,14 @@ class TimeBuildModelLithiumPlating: ["none", "irreversible", "reversible", "partially reversible"], ) - def setup(self): + def setup(self, model, params, solver_class): set_random_seed() def time_setup_model(self, model, params): build_model("OKane2022", model, "lithium plating", params) -class TimeSolveLithiumPlating: +class TimeSolveLithiumPlating(SolveModel): param_names = ["model", "model option", "solver class"] params = ( [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN], @@ -155,14 +155,14 @@ class TimeBuildModelSEI: ], ) - def setup(self): + def setup(self, model, params, solver_class): set_random_seed() def time_setup_model(self, model, params): build_model("Marquis2019", model, "SEI", params) -class TimeSolveSEI: +class TimeSolveSEI(SolveModel): param_names = ["model", "model option", "solver class"] params = ( [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN], @@ -198,14 +198,14 @@ class TimeBuildModelParticle: ], ) - def setup(self): + def setup(self, model, params, solver_class): set_random_seed() def time_setup_model(self, model, params): build_model("Marquis2019", model, "particle", params) -class TimeSolveParticle: +class TimeSolveParticle(SolveModel): param_names = ["model", "model option", "solver class"] params = ( [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN], @@ -235,14 +235,14 @@ class TimeBuildModelThermal: ["isothermal", "lumped", "x-full"], ) - def setup(self): + def setup(self, model, params, solver_class): set_random_seed() def time_setup_model(self, model, params): build_model("Marquis2019", model, "thermal", params) -class TimeSolveThermal: +class TimeSolveThermal(SolveModel): param_names = ["model", "model option", "solver class"] params = ( [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN], @@ -267,14 +267,14 @@ class TimeBuildModelSurfaceForm: ["false", "differential", "algebraic"], ) - def setup(self): + def setup(self, model, params, solver_class): set_random_seed() def time_setup_model(self, model, params): build_model("Marquis2019", model, "surface form", params) -class TimeSolveSurfaceForm: +class TimeSolveSurfaceForm(SolveModel): param_names = ["model", "model option", "solver class"] params = ( [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN], diff --git a/benchmarks/memory_unit_benchmarks.py b/benchmarks/memory_unit_benchmarks.py index a355442278..5f2c360280 100644 --- a/benchmarks/memory_unit_benchmarks.py +++ b/benchmarks/memory_unit_benchmarks.py @@ -35,7 +35,7 @@ def mem_create_expression(self): return self.model -class MemParameteriseModel: +class MemParameteriseModel(MemCreateExpression): def setup(self): set_random_seed() MemCreateExpression.mem_create_expression(self) @@ -63,7 +63,7 @@ def mem_parameterise(self): return param -class MemDiscretiseModel: +class MemDiscretiseModel(MemParameteriseModel): def setup(self): set_random_seed() MemCreateExpression.mem_create_expression(self) @@ -82,7 +82,7 @@ def mem_discretise(self): return disc -class MemSolveModel: +class MemSolveModel(MemDiscretiseModel): def setup(self): set_random_seed() MemCreateExpression.mem_create_expression(self) diff --git a/benchmarks/unit_benchmarks.py b/benchmarks/unit_benchmarks.py index dd5c55a5d4..432fb849b7 100644 --- a/benchmarks/unit_benchmarks.py +++ b/benchmarks/unit_benchmarks.py @@ -34,7 +34,7 @@ def time_create_expression(self): } -class TimeParameteriseModel: +class TimeParameteriseModel(TimeCreateExpression): def setup(self): set_random_seed() TimeCreateExpression.time_create_expression(self) @@ -61,7 +61,7 @@ def time_parameterise(self): param.process_geometry(self.geometry) -class TimeDiscretiseModel: +class TimeDiscretiseModel(TimeParameteriseModel): def setup(self): set_random_seed() TimeCreateExpression.time_create_expression(self) @@ -79,7 +79,7 @@ def time_discretise(self): disc.process_model(self.model) -class TimeSolveModel: +class TimeSolveModel(TimeDiscretiseModel): def setup(self): set_random_seed() TimeCreateExpression.time_create_expression(self) From 5ad9cea749d771240fc9bf5770bc9a21e21c143d Mon Sep 17 00:00:00 2001 From: kratman Date: Sun, 15 Oct 2023 10:00:06 -0400 Subject: [PATCH 50/75] Adding unused parameters to setup functions --- benchmarks/different_model_options.py | 26 ++++++++--------- benchmarks/memory_sims.py | 25 ++++++++-------- benchmarks/time_setup_models_and_sims.py | 36 ++++++++++++------------ benchmarks/time_sims_experiments.py | 2 +- benchmarks/time_solve_models.py | 6 ++-- 5 files changed, 49 insertions(+), 46 deletions(-) diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py index 6a1ca22448..3010604072 100644 --- a/benchmarks/different_model_options.py +++ b/benchmarks/different_model_options.py @@ -72,7 +72,7 @@ def solve_setup(self, parameter, model_, option, value, solver_class): disc = pybamm.Discretisation(mesh, self.model.default_spatial_methods) disc.process_model(self.model) - def solve_model(self, model, params): + def solve_model(self, _model, _params): self.solver.solve(self.model, t_eval=self.t_eval) @@ -83,7 +83,7 @@ class TimeBuildModelLossActiveMaterial: ["none", "stress-driven", "reaction-driven", "stress and reaction-driven"], ) - def setup(self, model, params, solver_class): + def setup(self, _model, _params): set_random_seed() def time_setup_model(self, model, params): @@ -104,7 +104,7 @@ def setup(self, model, params, solver_class): self, "Ai2020", model, "loss of active material", params, solver_class ) - def time_solve_model(self, model, params, solver_class): + def time_solve_model(self, _model, _params, _solver_class): self.solver.solve(self.model, t_eval=self.t_eval) @@ -115,7 +115,7 @@ class TimeBuildModelLithiumPlating: ["none", "irreversible", "reversible", "partially reversible"], ) - def setup(self, model, params, solver_class): + def setup(self, _model, _params): set_random_seed() def time_setup_model(self, model, params): @@ -136,7 +136,7 @@ def setup(self, model, params, solver_class): self, "OKane2022", model, "lithium plating", params, solver_class ) - def time_solve_model(self, model, params, solver_class): + def time_solve_model(self, _model, _params, _solver_class): self.solver.solve(self.model, t_eval=self.t_eval) @@ -155,7 +155,7 @@ class TimeBuildModelSEI: ], ) - def setup(self, model, params, solver_class): + def setup(self, _model, _params): set_random_seed() def time_setup_model(self, model, params): @@ -182,7 +182,7 @@ def setup(self, model, params, solver_class): set_random_seed() SolveModel.solve_setup(self, "Marquis2019", model, "SEI", params, solver_class) - def time_solve_model(self, model, params, solver_class): + def time_solve_model(self, _model, _params, _solver_class): self.solver.solve(self.model, t_eval=self.t_eval) @@ -198,7 +198,7 @@ class TimeBuildModelParticle: ], ) - def setup(self, model, params, solver_class): + def setup(self, _model, _params): set_random_seed() def time_setup_model(self, model, params): @@ -224,7 +224,7 @@ def setup(self, model, params, solver_class): self, "Marquis2019", model, "particle", params, solver_class ) - def time_solve_model(self, model, params, solver_class): + def time_solve_model(self, _model, _params, _solver_class): self.solver.solve(self.model, t_eval=self.t_eval) @@ -235,7 +235,7 @@ class TimeBuildModelThermal: ["isothermal", "lumped", "x-full"], ) - def setup(self, model, params, solver_class): + def setup(self, _model, _params): set_random_seed() def time_setup_model(self, model, params): @@ -256,7 +256,7 @@ def setup(self, model, params, solver_class): self, "Marquis2019", model, "thermal", params, solver_class ) - def time_solve_model(self, model, params, solver_class): + def time_solve_model(self, _model, _params, _solver_class): self.solver.solve(self.model, t_eval=self.t_eval) @@ -267,7 +267,7 @@ class TimeBuildModelSurfaceForm: ["false", "differential", "algebraic"], ) - def setup(self, model, params, solver_class): + def setup(self, _model, _params): set_random_seed() def time_setup_model(self, model, params): @@ -294,5 +294,5 @@ def setup(self, model, params, solver_class): self, "Marquis2019", model, "surface form", params, solver_class ) - def time_solve_model(self, model, params, solver_class): + def time_solve_model(self, _model, _params, _solver_class): self.solver.solve(self.model, t_eval=self.t_eval) diff --git a/benchmarks/memory_sims.py b/benchmarks/memory_sims.py index 0c7a030fde..4d01246a7b 100644 --- a/benchmarks/memory_sims.py +++ b/benchmarks/memory_sims.py @@ -8,11 +8,11 @@ class MemSPMSimulationCCCV: param_names = ["parameter"] params = parameters - def setup(self): + def setup(self, _params): set_random_seed() - def mem_setup_SPM_simulationCCCV(self, parameters): - self.param = pybamm.ParameterValues(parameters) + def mem_setup_SPM_simulationCCCV(self, params): + self.param = pybamm.ParameterValues(params) self.model = pybamm.lithium_ion.SPM() exp = pybamm.Experiment( [ @@ -33,8 +33,11 @@ class MemDFNSimulationCCCV: param_names = ["parameter"] params = parameters - def mem_setup_DFN_simulationCCCV(self, parameters): - self.param = pybamm.ParameterValues(parameters) + def setup(self, _params): + set_random_seed() + + def mem_setup_DFN_simulationCCCV(self, params): + self.param = pybamm.ParameterValues(params) self.model = pybamm.lithium_ion.DFN() exp = pybamm.Experiment( [ @@ -55,11 +58,11 @@ class MemSPMSimulationGITT: param_names = ["parameter"] params = parameters - def setup(self): + def setup(self, _params): set_random_seed() - def mem_setup_SPM_simulationGITT(self, parameters): - self.param = pybamm.ParameterValues(parameters) + def mem_setup_SPM_simulationGITT(self, params): + self.param = pybamm.ParameterValues(params) self.model = pybamm.lithium_ion.SPM() exp = pybamm.Experiment( [("Discharge at C/20 for 1 hour", "Rest for 1 hour")] * 20 @@ -74,11 +77,11 @@ class MemDFNSimulationGITT: param_names = ["parameter"] params = parameters - def setup(self): + def setup(self, _params): set_random_seed() - def mem_setup_DFN_simulationGITT(self, parameters): - self.param = pybamm.ParameterValues(parameters) + def mem_setup_DFN_simulationGITT(self, params): + self.param = pybamm.ParameterValues(params) self.model = pybamm.lithium_ion.SPM() exp = pybamm.Experiment( [("Discharge at C/20 for 1 hour", "Rest for 1 hour")] * 20 diff --git a/benchmarks/time_setup_models_and_sims.py b/benchmarks/time_setup_models_and_sims.py index 498086f7f0..b6a1baca1c 100644 --- a/benchmarks/time_setup_models_and_sims.py +++ b/benchmarks/time_setup_models_and_sims.py @@ -35,11 +35,11 @@ class TimeBuildSPM: param_names = ["parameter"] params = parameters - def setup(self): + def setup(self, _params): set_random_seed() - def time_setup_SPM(self, parameters): - self.param = pybamm.ParameterValues(parameters) + def time_setup_SPM(self, params): + self.param = pybamm.ParameterValues(params) self.model = pybamm.lithium_ion.SPM() self.param.process_model(self.model) compute_discretisation(self.model, self.param).process_model(self.model) @@ -49,11 +49,11 @@ class TimeBuildSPMe: param_names = ["parameter"] params = parameters - def setup(self): + def setup(self, _params): set_random_seed() - def time_setup_SPMe(self, parameters): - self.param = pybamm.ParameterValues(parameters) + def time_setup_SPMe(self, params): + self.param = pybamm.ParameterValues(params) self.model = pybamm.lithium_ion.SPMe() self.param.process_model(self.model) compute_discretisation(self.model, self.param).process_model(self.model) @@ -63,11 +63,11 @@ class TimeBuildDFN: param_names = ["parameter"] params = parameters - def setup(self): + def setup(self, _params): set_random_seed() - def time_setup_DFN(self, parameters): - self.param = pybamm.ParameterValues(parameters) + def time_setup_DFN(self, params): + self.param = pybamm.ParameterValues(params) self.model = pybamm.lithium_ion.DFN() self.param.process_model(self.model) compute_discretisation(self.model, self.param).process_model(self.model) @@ -77,11 +77,11 @@ class TimeBuildSPMSimulation: param_names = ["with experiment", "parameter"] params = ([False, True], parameters) - def setup(self): + def setup(self, _with_experiment, _params): set_random_seed() - def time_setup_SPM_simulation(self, with_experiment, parameters): - self.param = pybamm.ParameterValues(parameters) + def time_setup_SPM_simulation(self, with_experiment, params): + self.param = pybamm.ParameterValues(params) self.model = pybamm.lithium_ion.SPM() if with_experiment: exp = pybamm.Experiment( @@ -98,11 +98,11 @@ class TimeBuildSPMeSimulation: param_names = ["with experiment", "parameter"] params = ([False, True], parameters) - def setup(self): + def setup(self, _with_experiment, _params): set_random_seed() - def time_setup_SPMe_simulation(self, with_experiment, parameters): - self.param = pybamm.ParameterValues(parameters) + def time_setup_SPMe_simulation(self, with_experiment, params): + self.param = pybamm.ParameterValues(params) self.model = pybamm.lithium_ion.SPMe() if with_experiment: exp = pybamm.Experiment( @@ -119,11 +119,11 @@ class TimeBuildDFNSimulation: param_names = ["with experiment", "parameter"] params = ([False, True], parameters) - def setup(self): + def setup(self, _with_experiment, _params): set_random_seed() - def time_setup_DFN_simulation(self, with_experiment, parameters): - self.param = pybamm.ParameterValues(parameters) + def time_setup_DFN_simulation(self, with_experiment, params): + self.param = pybamm.ParameterValues(params) self.model = pybamm.lithium_ion.DFN() if with_experiment: exp = pybamm.Experiment( diff --git a/benchmarks/time_sims_experiments.py b/benchmarks/time_sims_experiments.py index 13bdc3432e..d380999c99 100644 --- a/benchmarks/time_sims_experiments.py +++ b/benchmarks/time_sims_experiments.py @@ -48,5 +48,5 @@ def time_setup(self, experiment, parameters, model_class, solver_class): exp = pybamm.Experiment(self.experiment_descriptions[experiment]) pybamm.Simulation(model, parameter_values=param, experiment=exp, solver=solver) - def time_solve(self, experiment, parameters, model_class, solver_class): + def time_solve(self, _experiment, _parameters, _model_class, _solver_class): self.sim.solve() diff --git a/benchmarks/time_solve_models.py b/benchmarks/time_solve_models.py index 15a78dde64..efcf97b450 100644 --- a/benchmarks/time_solve_models.py +++ b/benchmarks/time_solve_models.py @@ -62,7 +62,7 @@ def setup(self, solve_first, parameters, solver_class): if solve_first: solve_model_once(self.model, self.solver, self.t_eval) - def time_solve_model(self, solve_first, parameters, solver_class): + def time_solve_model(self, _solve_first, _parameters, _solver_class): self.solver.solve(self.model, t_eval=self.t_eval) @@ -118,7 +118,7 @@ def setup(self, solve_first, parameters, solver_class): if solve_first: solve_model_once(self.model, self.solver, self.t_eval) - def time_solve_model(self, solve_first, parameters, solver_class): + def time_solve_model(self, _solve_first, _parameters, _solver_class): self.solver.solve(self.model, t_eval=self.t_eval) @@ -179,5 +179,5 @@ def setup(self, solve_first, parameters, solver_class): if solve_first: solve_model_once(self.model, self.solver, self.t_eval) - def time_solve_model(self, solve_first, parameters, solver_class): + def time_solve_model(self, _solve_first, _parameters, _solver_class): self.solver.solve(self.model, t_eval=self.t_eval) From 061f35d2ebefbde7210e884433a8f0611dd1199b Mon Sep 17 00:00:00 2001 From: kratman Date: Sun, 15 Oct 2023 13:07:59 -0400 Subject: [PATCH 51/75] Cleaning up member variables to reduce static analysis warnings --- benchmarks/different_model_options.py | 4 ++++ benchmarks/memory_sims.py | 12 ++++++++++++ benchmarks/memory_unit_benchmarks.py | 6 ++++++ benchmarks/time_setup_models_and_sims.py | 10 ++++++++++ benchmarks/time_sims_experiments.py | 5 +++++ benchmarks/time_solve_models.py | 9 +++++++++ benchmarks/unit_benchmarks.py | 6 ++++++ benchmarks/work_precision_sets/time_vs_dt_max.py | 1 - 8 files changed, 52 insertions(+), 1 deletion(-) diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py index 3010604072..a4cf787ad9 100644 --- a/benchmarks/different_model_options.py +++ b/benchmarks/different_model_options.py @@ -31,6 +31,10 @@ def build_model(parameter, model_, option, value): class SolveModel: + solver: pybamm.BaseSolver + model: pybamm.BaseModel + t_eval: np.ndarray + def solve_setup(self, parameter, model_, option, value, solver_class): import importlib diff --git a/benchmarks/memory_sims.py b/benchmarks/memory_sims.py index 4d01246a7b..45d3e41834 100644 --- a/benchmarks/memory_sims.py +++ b/benchmarks/memory_sims.py @@ -7,6 +7,9 @@ class MemSPMSimulationCCCV: param_names = ["parameter"] params = parameters + param: pybamm.ParameterValues + model: pybamm.BaseModel + sim: pybamm.Simulation def setup(self, _params): set_random_seed() @@ -32,6 +35,9 @@ def mem_setup_SPM_simulationCCCV(self, params): class MemDFNSimulationCCCV: param_names = ["parameter"] params = parameters + param: pybamm.ParameterValues + model: pybamm.BaseModel + sim: pybamm.Simulation def setup(self, _params): set_random_seed() @@ -57,6 +63,9 @@ def mem_setup_DFN_simulationCCCV(self, params): class MemSPMSimulationGITT: param_names = ["parameter"] params = parameters + param: pybamm.ParameterValues + model: pybamm.BaseModel + sim: pybamm.Simulation def setup(self, _params): set_random_seed() @@ -76,6 +85,9 @@ def mem_setup_SPM_simulationGITT(self, params): class MemDFNSimulationGITT: param_names = ["parameter"] params = parameters + param: pybamm.ParameterValues + model: pybamm.BaseModel + sim: pybamm.Simulation def setup(self, _params): set_random_seed() diff --git a/benchmarks/memory_unit_benchmarks.py b/benchmarks/memory_unit_benchmarks.py index 5f2c360280..79970c70ef 100644 --- a/benchmarks/memory_unit_benchmarks.py +++ b/benchmarks/memory_unit_benchmarks.py @@ -4,6 +4,9 @@ class MemCreateExpression: + R: pybamm.Parameter + model: pybamm.BaseModel + def setup(self): set_random_seed() @@ -36,6 +39,9 @@ def mem_create_expression(self): class MemParameteriseModel(MemCreateExpression): + r: pybamm.SpatialVariable + geometry: dict + def setup(self): set_random_seed() MemCreateExpression.mem_create_expression(self) diff --git a/benchmarks/time_setup_models_and_sims.py b/benchmarks/time_setup_models_and_sims.py index b6a1baca1c..2677c9936c 100644 --- a/benchmarks/time_setup_models_and_sims.py +++ b/benchmarks/time_setup_models_and_sims.py @@ -34,6 +34,8 @@ def compute_discretisation(model, param): class TimeBuildSPM: param_names = ["parameter"] params = parameters + param: pybamm.ParameterValues + model: pybamm.BaseModel def setup(self, _params): set_random_seed() @@ -62,6 +64,8 @@ def time_setup_SPMe(self, params): class TimeBuildDFN: param_names = ["parameter"] params = parameters + param: pybamm.ParameterValues + model: pybamm.BaseModel def setup(self, _params): set_random_seed() @@ -76,6 +80,8 @@ def time_setup_DFN(self, params): class TimeBuildSPMSimulation: param_names = ["with experiment", "parameter"] params = ([False, True], parameters) + param: pybamm.ParameterValues + model: pybamm.BaseModel def setup(self, _with_experiment, _params): set_random_seed() @@ -97,6 +103,8 @@ def time_setup_SPM_simulation(self, with_experiment, params): class TimeBuildSPMeSimulation: param_names = ["with experiment", "parameter"] params = ([False, True], parameters) + param: pybamm.ParameterValues + model: pybamm.BaseModel def setup(self, _with_experiment, _params): set_random_seed() @@ -118,6 +126,8 @@ def time_setup_SPMe_simulation(self, with_experiment, params): class TimeBuildDFNSimulation: param_names = ["with experiment", "parameter"] params = ([False, True], parameters) + param: pybamm.ParameterValues + model: pybamm.BaseModel def setup(self, _with_experiment, _params): set_random_seed() diff --git a/benchmarks/time_sims_experiments.py b/benchmarks/time_sims_experiments.py index d380999c99..bcd3e71f2f 100644 --- a/benchmarks/time_sims_experiments.py +++ b/benchmarks/time_sims_experiments.py @@ -20,6 +20,11 @@ class TimeSimulation: ], "GITT": [("Discharge at C/20 for 1 hour", "Rest for 1 hour")] * 10, } + param: pybamm.ParameterValues + model: pybamm.BaseModel + solver: pybamm.BaseSolver + exp: pybamm.Experiment + sim: pybamm.Simulation def setup(self, experiment, parameters, model_class, solver_class): set_random_seed() diff --git a/benchmarks/time_solve_models.py b/benchmarks/time_solve_models.py index efcf97b450..e41a7ccd16 100644 --- a/benchmarks/time_solve_models.py +++ b/benchmarks/time_solve_models.py @@ -28,6 +28,9 @@ class TimeSolveSPM: pybamm.IDAKLUSolver, ], ) + model: pybamm.BaseModel + solver: pybamm.BaseSolver + t_eval: np.ndarray def setup(self, solve_first, parameters, solver_class): set_random_seed() @@ -84,6 +87,9 @@ class TimeSolveSPMe: pybamm.IDAKLUSolver, ], ) + model: pybamm.BaseModel + solver: pybamm.BaseSolver + t_eval: np.ndarray def setup(self, solve_first, parameters, solver_class): set_random_seed() @@ -140,6 +146,9 @@ class TimeSolveDFN: pybamm.IDAKLUSolver, ], ) + model: pybamm.BaseModel + solver: pybamm.BaseSolver + t_eval: np.ndarray def setup(self, solve_first, parameters, solver_class): set_random_seed() diff --git a/benchmarks/unit_benchmarks.py b/benchmarks/unit_benchmarks.py index 432fb849b7..73af4dda26 100644 --- a/benchmarks/unit_benchmarks.py +++ b/benchmarks/unit_benchmarks.py @@ -4,6 +4,9 @@ class TimeCreateExpression: + R: pybamm.Parameter + model: pybamm.BaseModel + def setup(self): set_random_seed() @@ -35,6 +38,9 @@ def time_create_expression(self): class TimeParameteriseModel(TimeCreateExpression): + r: pybamm.SpatialVariable + geometry: dict + def setup(self): set_random_seed() TimeCreateExpression.time_create_expression(self) diff --git a/benchmarks/work_precision_sets/time_vs_dt_max.py b/benchmarks/work_precision_sets/time_vs_dt_max.py index 1368dce350..3926a4bcd6 100644 --- a/benchmarks/work_precision_sets/time_vs_dt_max.py +++ b/benchmarks/work_precision_sets/time_vs_dt_max.py @@ -41,7 +41,6 @@ ): for params in parameters: time_points = [] - # solver = pybamm.CasadiSolver() model = model_.new_copy() c_rate = 10 From 242b672466173539862147fe37da3daebe5be19d Mon Sep 17 00:00:00 2001 From: Valentin Sulzer Date: Mon, 16 Oct 2023 15:41:55 -0400 Subject: [PATCH 52/75] changelog --- CHANGELOG.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 421d3bfa29..d9583ee31c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,6 +3,7 @@ # [v23.9rc0](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-10-31 ## Features + - The parameter "Ambient temperature [K]" can now be given as a function of position `(y,z)` and time `t`. The "edge" and "current collector" heat transfer coefficient parameters can also depend on `(y,z)` ([#3257](https://github.com/pybamm-team/PyBaMM/pull/3257)) - Spherical and cylindrical shell domains can now be solved with any boundary conditions ([#3237](https://github.com/pybamm-team/PyBaMM/pull/3237)) - Processed variables now get the spatial variables automatically, allowing plotting of more generic models ([#3234](https://github.com/pybamm-team/PyBaMM/pull/3234)) @@ -44,6 +45,7 @@ ## Breaking changes +- The parameter "Exchange-current density for lithium plating [A.m-2]" has been renamed to "Exchange-current density for lithium metal electrode [A.m-2]" when referring to the lithium plating reaction on the surface of a lithium metal electrode ([#3445](https://github.com/pybamm-team/PyBaMM/pull/3445)) - Dropped support for i686 (32-bit) architectures on GNU/Linux distributions ([#3412](https://github.com/pybamm-team/PyBaMM/pull/3412)) - The class `pybamm.thermal.OneDimensionalX` has been moved to `pybamm.thermal.pouch_cell.OneDimensionalX` to reflect the fact that the model formulation implicitly assumes a pouch cell geometry ([#3257](https://github.com/pybamm-team/PyBaMM/pull/3257)) - The "lumped" thermal option now always used the parameters "Cell cooling surface area [m2]", "Cell volume [m3]" and "Total heat transfer coefficient [W.m-2.K-1]" to compute the cell cooling regardless of the chosen "cell geometry" option. The user must now specify the correct values for these parameters instead of them being calculated based on e.g. a pouch cell. An `OptionWarning` is raised to let users know to update their parameters ([#3257](https://github.com/pybamm-team/PyBaMM/pull/3257)) From a4cc36191f4c581c39bf40deb934cc5543b26aac Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Tue, 17 Oct 2023 18:00:38 +0530 Subject: [PATCH 53/75] Make testpypi the default option + update tag instructions --- .github/release_workflow.md | 2 +- .github/workflows/publish_pypi.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/release_workflow.md b/.github/release_workflow.md index 1af23fca25..7afa24a6d6 100644 --- a/.github/release_workflow.md +++ b/.github/release_workflow.md @@ -75,5 +75,5 @@ Some other essential things to check throughout the release process - - As the release workflow is initiated by the `release` event, it's important to note that the default `GITHUB_REF` used by `actions/checkout` during the checkout process will correspond to the tag created during the release process. Consequently, the workflows will consistently build PyBaMM based on the commit associated with this tag. Should new commits be introduced to the `vYY.MM` branch, such as those addressing build issues, it becomes necessary to manually update this tag to point to the most recent commit - ``` git tag -f - git push origin # can only be carried out by the maintainers + git push -f # can only be carried out by the maintainers ``` diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml index 25fbafc0af..7d01fe0bee 100644 --- a/.github/workflows/publish_pypi.yml +++ b/.github/workflows/publish_pypi.yml @@ -10,7 +10,7 @@ on: inputs: target: description: 'Deployment target. Can be "pypi" or "testpypi"' - default: "pypi" + default: "testpypi" debug_enabled: type: boolean description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)' From 9aeb6370bfb35d0eea4aec29994ff3747ac124e9 Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Tue, 17 Oct 2023 19:18:06 +0530 Subject: [PATCH 54/75] Use `pipx` to run `nox` in workflows --- .github/workflows/run_periodic_tests.yml | 20 +++++++------- .github/workflows/test_on_push.yml | 34 ++++++++++++------------ 2 files changed, 27 insertions(+), 27 deletions(-) diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml index 2f9b5d89a8..f6e51bc11b 100644 --- a/.github/workflows/run_periodic_tests.yml +++ b/.github/workflows/run_periodic_tests.yml @@ -84,44 +84,44 @@ jobs: if: matrix.os == 'windows-latest' run: choco install graphviz --version=2.38.0.20190211 - - name: Install standard python dependencies + - name: Install standard Python dependencies run: | - python -m pip install --upgrade pip wheel setuptools nox + python -m pip install --upgrade pip wheel setuptools - name: Install SuiteSparse and SUNDIALS on GNU/Linux if: matrix.os == 'ubuntu-latest' - run: nox -s pybamm-requires + run: pipx run nox -s pybamm-requires - name: Run unit tests for GNU/Linux with Python 3.8, 3.9, and 3.10, and for macOS and Windows with all Python versions if: (matrix.os == 'ubuntu-latest' && matrix.python-version != 3.11) || (matrix.os != 'ubuntu-latest') - run: nox -s unit + run: pipx run nox -s unit - 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: nox -s coverage + run: pipx run nox -s coverage - name: Upload coverage report if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.11 uses: codecov/codecov-action@v3.1.4 - name: Run integration tests - run: nox -s integration + run: pipx run nox -s integration - name: Install docs dependencies and run doctests if: matrix.os == 'ubuntu-latest' - run: nox -s doctests + run: pipx run nox -s doctests - name: Check if the documentation can be built if: matrix.os == 'ubuntu-latest' - run: nox -s docs + run: pipx run nox -s docs - name: Install dev dependencies and run example tests if: matrix.os == 'ubuntu-latest' - run: nox -s examples + run: pipx run nox -s examples - name: Run example scripts tests if: matrix.os == 'ubuntu-latest' - run: nox -s scripts + run: pipx run nox -s scripts #M-series Mac Mini build-apple-mseries: diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml index b740da2e1b..cb22fb87f7 100644 --- a/.github/workflows/test_on_push.yml +++ b/.github/workflows/test_on_push.yml @@ -92,7 +92,7 @@ jobs: - name: Install PyBaMM dependencies run: | - pip install --upgrade pip wheel setuptools nox + pip install --upgrade pip wheel setuptools pip install -e .[all,docs] - name: Cache pybamm-requires nox environment for GNU/Linux @@ -111,10 +111,10 @@ jobs: - name: Install SuiteSparse and SUNDIALS on GNU/Linux if: matrix.os == 'ubuntu-latest' - run: nox -s pybamm-requires + run: pipx run nox -s pybamm-requires - name: Run unit tests for ${{ matrix.os }} with Python ${{ matrix.python-version }} - run: nox -s unit + run: pipx run nox -s unit # Runs only on Ubuntu with Python 3.11 check_coverage: @@ -169,10 +169,10 @@ jobs: key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }} - name: Install SuiteSparse and SUNDIALS on GNU/Linux - run: nox -s pybamm-requires + run: pipx run nox -s pybamm-requires - name: Run unit tests for Ubuntu with Python 3.11 and generate coverage report - run: nox -s coverage + run: pipx run nox -s coverage - name: Upload coverage report uses: codecov/codecov-action@v3.1.4 @@ -235,7 +235,7 @@ jobs: - name: Install PyBaMM dependencies run: | - pip install --upgrade pip wheel setuptools nox + pip install --upgrade pip wheel setuptools pip install -e .[all,docs] - name: Cache pybamm-requires nox environment for GNU/Linux @@ -254,10 +254,10 @@ jobs: - name: Install SuiteSparse and SUNDIALS on GNU/Linux if: matrix.os == 'ubuntu-latest' - run: nox -s pybamm-requires + run: pipx run nox -s pybamm-requires - name: Run integration tests for ${{ matrix.os }} with Python ${{ matrix.python-version }} - run: nox -s integration + run: pipx run nox -s integration # Runs only on Ubuntu with Python 3.11. Skips IDAKLU module compilation # for speedups, which is already tested in other jobs. @@ -296,14 +296,14 @@ jobs: - name: Install PyBaMM dependencies run: | - pip install --upgrade pip wheel setuptools nox + pip install --upgrade pip wheel setuptools pip install -e .[all,docs] - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.11 - run: nox -s doctests + run: pipx run nox -s doctests - name: Check if the documentation can be built for GNU/Linux with Python 3.11 - run: nox -s docs + run: pipx run nox -s docs # Runs only on Ubuntu with Python 3.11 run_example_tests: @@ -341,7 +341,7 @@ jobs: - name: Install PyBaMM dependencies run: | - pip install --upgrade pip wheel setuptools nox + pip install --upgrade pip wheel setuptools pip install -e .[all,docs] - name: Cache pybamm-requires nox environment for GNU/Linux @@ -358,10 +358,10 @@ jobs: key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }} - name: Install SuiteSparse and SUNDIALS on GNU/Linux - run: nox -s pybamm-requires + run: pipx run nox -s pybamm-requires - name: Install dev dependencies and run example tests for GNU/Linux with Python 3.11 - run: nox -s examples + run: pipx run nox -s examples # Runs only on Ubuntu with Python 3.11 run_scripts_tests: @@ -399,7 +399,7 @@ jobs: - name: Install PyBaMM dependencies run: | - pip install --upgrade pip wheel setuptools nox + pip install --upgrade pip wheel setuptools pip install -e .[all,docs] - name: Cache pybamm-requires nox environment for GNU/Linux @@ -416,7 +416,7 @@ jobs: key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }} - name: Install SuiteSparse and SUNDIALS on GNU/Linux - run: nox -s pybamm-requires + run: pipx run nox -s pybamm-requires - name: Install dev dependencies and run example scripts tests for GNU/Linux with Python 3.11 - run: nox -s scripts + run: pipx run nox -s scripts From 35bafb775188ca9e07b841a1cae6139d3cdbd47c Mon Sep 17 00:00:00 2001 From: Alec Bills Date: Tue, 17 Oct 2023 11:51:30 -0700 Subject: [PATCH 55/75] hotfix make initial soc work with halfcell --- .../full_battery_models/base_battery_model.py | 2 +- .../lithium_ion/__init__.py | 5 +- .../lithium_ion/electrode_soh_half_cell.py | 91 +++++++++++++++++++ pybamm/parameters/parameter_values.py | 36 ++++++++ pybamm/simulation.py | 19 +++- tests/unit/test_simulation.py | 27 ++++++ 6 files changed, 173 insertions(+), 7 deletions(-) diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index 79e135123f..971bd1a880 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -551,7 +551,7 @@ def __init__(self, extra_options): ) if options["working electrode"] == "negative": raise pybamm.OptionError( - "The 'negative' working elecrtrode option has been removed because " + "The 'negative' working electrode option has been removed because " "the voltage - and therefore the energy stored - would be negative." "Use the 'positive' working electrode option instead and set whatever " "would normally be the negative electrode as the positive electrode." diff --git a/pybamm/models/full_battery_models/lithium_ion/__init__.py b/pybamm/models/full_battery_models/lithium_ion/__init__.py index 95a5059f5a..4afb23f493 100644 --- a/pybamm/models/full_battery_models/lithium_ion/__init__.py +++ b/pybamm/models/full_battery_models/lithium_ion/__init__.py @@ -9,7 +9,10 @@ get_initial_ocps, get_min_max_ocps, ) -from .electrode_soh_half_cell import ElectrodeSOHHalfCell +from .electrode_soh_half_cell import ( + ElectrodeSOHHalfCell, + get_initial_stoichiometry_half_cell +) from .spm import SPM from .spme import SPMe from .dfn import DFN 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 d938fdc769..1e237e73c8 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 @@ -56,3 +56,94 @@ def __init__(self, name="Electrode-specific SOH model"): def default_solver(self): # Use AlgebraicSolver as CasadiAlgebraicSolver gives unnecessary warnings return pybamm.AlgebraicSolver() + + +def get_initial_stoichiometry_half_cell( + initial_value, + parameter_values, + param=None, + known_value="cyclable lithium capacity", + options=None, +): + """ + Calculate initial stoichiometry to start off the simulation at a particular + state of charge, given voltage limits, open-circuit potential, etc defined by + parameter_values + + Parameters + ---------- + initial_value : float + Target 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. + parameter_values : pybamm.ParameterValues + The parameter values to use in the calculation + + Returns + ------- + x + 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) + + 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: + raise ValueError( + f"Initial voltage {V_init}V is outside the voltage limits " + f"({V_min}, {V_max})" + ) + + # Solve simple model for initial soc based on target voltage + soc_model = pybamm.BaseModel() + soc = pybamm.Variable("soc") + Up = param.p.prim.U + T_ref = parameter_values["Reference temperature [K]"] + x = x_0 + soc * (x_100 - x_0) + + soc_model.algebraic[soc] = Up(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] + elif isinstance(initial_value, (int, float)): + initial_soc = initial_value + if not 0 <= initial_soc <= 1: + raise ValueError("Initial SOC should be between 0 and 1") + + else: + raise ValueError( + "Initial value must be a float between 0 and 1, " + "or a string ending in 'V'" + ) + + x = x_0 + initial_soc * (x_100 - x_0) + + return x + + +def get_min_max_stoichiometries( + parameter_values, options={"working electrode": "positive"} +): + """ + Get the minimum and maximum stoichiometries from the parameter values + + Parameters + ---------- + parameter_values : pybamm.ParameterValues + The parameter values to use in the calculation + """ + esoh_model = pybamm.lithium_ion.ElectrodeSOHHalfCell(options) + param = pybamm.LithiumIonParameters(options) + 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}) + 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/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py index e69291035d..5e3dccfdef 100644 --- a/pybamm/parameters/parameter_values.py +++ b/pybamm/parameters/parameter_values.py @@ -271,6 +271,42 @@ def update(self, values, check_conflict=False, check_already_exists=True, path=" # reset processed symbols self._processed_symbols = {} + def set_initial_stoichiometry_half_cell( + self, + intial_value, + param=None, + known_value="cyclable lithium capacity", + inplace=True, + options=None, + ): + """ + Set the initial stoichiometry of the working electrode, based on the initial + SOC or voltage + """ + param = param or pybamm.LithiumIonParameters(options) + x = pybamm.lithium_ion.get_initial_stoichiometry_half_cell( + intial_value, self, param=param, known_value=known_value, options=options + ) + if inplace: + parameter_values = self + else: + parameter_values = self.copy() + + if options["working electrode"] == "positive": + c_max = self.evaluate(param.p.prim.c_max) + else: + c_max = self.evaluate(param.n.prim.c_max) + + parameter_values.update( + { + "Initial concentration in {} electrode [mol.m-3]".format( + options["working electrode"] + ): x + * c_max + } + ) + return parameter_values + def set_initial_stoichiometries( self, initial_value, diff --git a/pybamm/simulation.py b/pybamm/simulation.py index 04a373b436..8805e925d0 100644 --- a/pybamm/simulation.py +++ b/pybamm/simulation.py @@ -290,9 +290,10 @@ def update_new_model_events(self, new_model, op): # 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(op.value, pybamm.Interpolant) or - isinstance(op.value, pybamm.Multiplication)): - inpt = {"start time":0} + if isinstance(op.value, pybamm.Interpolant) or isinstance( + op.value, pybamm.Multiplication + ): + inpt = {"start time": 0} init_curr = op.value.evaluate(t=0, inputs=inpt).flatten()[0] sign = np.sign(init_curr) else: @@ -373,8 +374,16 @@ def set_initial_soc(self, initial_soc): options = self.model.options param = self._model.param if options["open-circuit potential"] == "MSMR": - self._parameter_values = self._unprocessed_parameter_values.set_initial_ocps( # noqa: E501 - initial_soc, param=param, inplace=False, options=options + self._parameter_values = ( + self._unprocessed_parameter_values.set_initial_ocps( # noqa: E501 + initial_soc, param=param, inplace=False, options=options + ) + ) + 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 + ) ) else: self._parameter_values = ( diff --git a/tests/unit/test_simulation.py b/tests/unit/test_simulation.py index c98586ee59..dac94a2538 100644 --- a/tests/unit/test_simulation.py +++ b/tests/unit/test_simulation.py @@ -204,6 +204,33 @@ def test_solve_with_initial_soc(self): sim.build(initial_soc=0.5) self.assertEqual(sim._built_initial_soc, 0.5) + # Test whether initial_soc works with half cell (solve) + options = {"working electrode": "positive"} + model = pybamm.lithium_ion.DFN(options) + sim = pybamm.Simulation(model) + sim.solve([0,1], initial_soc = 0.9) + self.assertEqual(sim._built_initial_soc, 0.9) + + # Test whether initial_soc works with half cell (build) + options = {"working electrode": "positive"} + model = pybamm.lithium_ion.DFN(options) + sim = pybamm.Simulation(model) + sim.build(initial_soc = 0.9) + self.assertEqual(sim._built_initial_soc, 0.9) + + # Test whether initial_soc works with half cell when it is a voltage + model = pybamm.lithium_ion.SPM({"working electrode": "positive"}) + parameter_values = model.default_parameter_values + ucv = parameter_values["Open-circuit voltage at 100% SOC [V]"] + parameter_values["Open-circuit voltage at 100% SOC [V]"] = ucv + 1e-12 + parameter_values["Upper voltage cut-off [V]"] = ucv + 1e-12 + options = {"working electrode": "positive"} + parameter_values["Current function [A]"] = 0.0 + sim = pybamm.Simulation(model, parameter_values=parameter_values) + sol = sim.solve([0,1], initial_soc = "{} V".format(ucv)) + voltage = sol["Terminal voltage [V]"].entries + self.assertAlmostEqual(voltage[0], ucv, places=5) + # test with MSMR model = pybamm.lithium_ion.MSMR({"number of MSMR reactions": ("6", "4")}) param = pybamm.ParameterValues("MSMR_Example") From d66e755728a70195544794fcc1b6084ca17e0dbe Mon Sep 17 00:00:00 2001 From: Alec Bills Date: Tue, 17 Oct 2023 14:47:28 -0700 Subject: [PATCH 56/75] log change in change log --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index d9583ee31c..72eda6ca97 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -37,6 +37,7 @@ - Error generated when invalid parameter values are passed ([#3132](https://github.com/pybamm-team/PyBaMM/pull/3132)) - Parameters in `Prada2013` have been updated to better match those given in the paper, which is a 2.3 Ah cell, instead of the mix-and-match with the 1.1 Ah cell from Lain2019 ([#3096](https://github.com/pybamm-team/PyBaMM/pull/3096)) - The `OneDimensionalX` thermal model has been updated to account for edge/tab cooling and account for the current collector volumetric heat capacity. It now gives the correct behaviour compared with a lumped model with the correct total heat transfer coefficient and surface area for cooling. ([#3042](https://github.com/pybamm-team/PyBaMM/pull/3042)) +- Fixed a bug where supplying an initial soc did not work with half cell models ([#3456]https://github.com/pybamm-team/PyBaMM/pull/3456) ## Optimizations From 098f1e8016f04077764784caba433c298da45571 Mon Sep 17 00:00:00 2001 From: Alec Bills Date: Tue, 17 Oct 2023 14:47:57 -0700 Subject: [PATCH 57/75] log change in change log --- CHANGELOG.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 72eda6ca97..008cad125f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -37,7 +37,7 @@ - Error generated when invalid parameter values are passed ([#3132](https://github.com/pybamm-team/PyBaMM/pull/3132)) - Parameters in `Prada2013` have been updated to better match those given in the paper, which is a 2.3 Ah cell, instead of the mix-and-match with the 1.1 Ah cell from Lain2019 ([#3096](https://github.com/pybamm-team/PyBaMM/pull/3096)) - The `OneDimensionalX` thermal model has been updated to account for edge/tab cooling and account for the current collector volumetric heat capacity. It now gives the correct behaviour compared with a lumped model with the correct total heat transfer coefficient and surface area for cooling. ([#3042](https://github.com/pybamm-team/PyBaMM/pull/3042)) -- Fixed a bug where supplying an initial soc did not work with half cell models ([#3456]https://github.com/pybamm-team/PyBaMM/pull/3456) +- Fixed a bug where supplying an initial soc did not work with half cell models ([#3456](https://github.com/pybamm-team/PyBaMM/pull/3456)) ## Optimizations From 3966a6198809f67ce47fe5e3556e9cdae22b5f5c Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Wed, 18 Oct 2023 20:28:28 +0530 Subject: [PATCH 58/75] Do not run image build workflow on forks --- .github/workflows/docker.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 9c16f95705..b6994795d6 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -9,7 +9,7 @@ on: jobs: build_docker_images: # This workflow is only of value to PyBaMM and would always be skipped in forks - # if: github.repository_owner == 'pybamm-team' + if: github.repository_owner == 'pybamm-team' name: Image (${{ matrix.build-args }}) runs-on: ubuntu-latest strategy: From e2e33c2d423859d850d755e37853fa15c6ad7a1b Mon Sep 17 00:00:00 2001 From: Alec Bills Date: Wed, 18 Oct 2023 11:14:21 -0700 Subject: [PATCH 59/75] add tests for inplace and errors --- pybamm/parameters/parameter_values.py | 4 ++-- .../test_lithium_ion/test_electrode_soh.py | 11 +++++++++ .../test_parameters/test_parameter_values.py | 23 +++++++++++++++++++ 3 files changed, 36 insertions(+), 2 deletions(-) diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py index 5e3dccfdef..254a0f6806 100644 --- a/pybamm/parameters/parameter_values.py +++ b/pybamm/parameters/parameter_values.py @@ -273,7 +273,7 @@ def update(self, values, check_conflict=False, check_already_exists=True, path=" def set_initial_stoichiometry_half_cell( self, - intial_value, + initial_value, param=None, known_value="cyclable lithium capacity", inplace=True, @@ -285,7 +285,7 @@ def set_initial_stoichiometry_half_cell( """ param = param or pybamm.LithiumIonParameters(options) x = pybamm.lithium_ion.get_initial_stoichiometry_half_cell( - intial_value, self, param=param, known_value=known_value, options=options + initial_value, self, param=param, known_value=known_value, options=options ) if inplace: parameter_values = self 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 49f7a5d855..7bd156d0bc 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 @@ -358,6 +358,17 @@ def test_error(self): with self.assertRaisesRegex(ValueError, "must be a float"): pybamm.lithium_ion.get_initial_stoichiometries("5 A", parameter_values) + with self.assertRaisesRegex(ValueError, "outside the voltage limits"): + pybamm.lithium_ion.get_initial_stoichiometry_half_cell("1 V", parameter_values) + + with self.assertRaisesRegex(ValueError, "must be a float"): + pybamm.lithium_ion.get_initial_stoichiometry_half_cell("5 A", parameter_values) + + with self.assertRaisesRegex( + ValueError, "Initial SOC should be between 0 and 1" + ): + pybamm.lithium_ion.get_initial_stoichiometry_half_cell(2, parameter_values) + class TestGetInitialOCP(TestCase): def test_get_initial_ocp(self): diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py index cc1f954686..ab699f0365 100644 --- a/tests/unit/test_parameters/test_parameter_values.py +++ b/tests/unit/test_parameters/test_parameter_values.py @@ -119,6 +119,29 @@ def test_set_initial_stoichiometries(self): y_100 = param_100["Initial concentration in positive electrode [mol.m-3]"] self.assertAlmostEqual(y, y_0 - 0.4 * (y_0 - y_100)) + def test_set_initial_stoichiometry_half_cell(self): + param = pybamm.lithium_ion.DFN({"working electrode": "positive"}).default_parameter_values + param = param.set_initial_stoichiometry_half_cell(0.4, inplace=False, options={"working electrode": "positive"}) + param_0 = param.set_initial_stoichiometry_half_cell(0, inplace=False, options={"working electrode": "positive"}) + param_100 = param.set_initial_stoichiometry_half_cell(1, inplace=False, options={"working electrode": "positive"}) + + y = param["Initial concentration in positive electrode [mol.m-3]"] + y_0 = param_0["Initial concentration in positive electrode [mol.m-3]"] + y_100 = param_100["Initial concentration in positive electrode [mol.m-3]"] + self.assertAlmostEqual(y, y_0 - 0.4 * (y_0 - y_100)) + + #inplace for 100% coverage + param_t = pybamm.lithium_ion.DFN({"working electrode": "positive"}).default_parameter_values + param_t.set_initial_stoichiometry_half_cell(0.4, inplace=True, options={"working electrode": "positive"}) + y = param_t["Initial concentration in positive electrode [mol.m-3]"] + param_0 = pybamm.lithium_ion.DFN({"working electrode": "positive"}).default_parameter_values + param_0.set_initial_stoichiometry_half_cell(0, inplace=True, options={"working electrode": "positive"}) + y_0 = param_0["Initial concentration in positive electrode [mol.m-3]"] + param_100 = pybamm.lithium_ion.DFN({"working electrode": "positive"}).default_parameter_values + param_100.set_initial_stoichiometry_half_cell(1, inplace=True, options={"working electrode": "positive"}) + y_100 = param_100["Initial concentration in positive electrode [mol.m-3]"] + self.assertAlmostEqual(y, y_0 - 0.4 * (y_0 - y_100)) + def test_set_initial_ocps(self): options = { "open-circuit potential": "MSMR", From 7f6d55ae054626571924b4e8395a0d1f08c19d6d Mon Sep 17 00:00:00 2001 From: Alec Bills Date: Wed, 18 Oct 2023 11:28:59 -0700 Subject: [PATCH 60/75] better handling of negative electrode case --- pybamm/parameters/parameter_values.py | 5 +- .../test_lithium_ion/test_electrode_soh.py | 15 ++++-- .../test_parameters/test_parameter_values.py | 50 +++++++++++++++---- 3 files changed, 52 insertions(+), 18 deletions(-) diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py index 254a0f6806..d5f12f362f 100644 --- a/pybamm/parameters/parameter_values.py +++ b/pybamm/parameters/parameter_values.py @@ -292,10 +292,7 @@ def set_initial_stoichiometry_half_cell( else: parameter_values = self.copy() - if options["working electrode"] == "positive": - c_max = self.evaluate(param.p.prim.c_max) - else: - c_max = self.evaluate(param.n.prim.c_max) + c_max = self.evaluate(param.p.prim.c_max) parameter_values.update( { 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 7bd156d0bc..628017d5d8 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 @@ -346,6 +346,9 @@ def test_initial_soc_cell_capacity(self): def test_error(self): parameter_values = pybamm.ParameterValues("Chen2020") + parameter_values_half_cell = pybamm.lithium_ion.DFN( + {"working electrode": "positive"} + ).default_parameter_values with self.assertRaisesRegex( ValueError, "Initial SOC should be between 0 and 1" @@ -359,15 +362,21 @@ def test_error(self): pybamm.lithium_ion.get_initial_stoichiometries("5 A", parameter_values) with self.assertRaisesRegex(ValueError, "outside the voltage limits"): - pybamm.lithium_ion.get_initial_stoichiometry_half_cell("1 V", parameter_values) + pybamm.lithium_ion.get_initial_stoichiometry_half_cell( + "1 V", parameter_values_half_cell + ) with self.assertRaisesRegex(ValueError, "must be a float"): - pybamm.lithium_ion.get_initial_stoichiometry_half_cell("5 A", parameter_values) + pybamm.lithium_ion.get_initial_stoichiometry_half_cell( + "5 A", parameter_values_half_cell + ) with self.assertRaisesRegex( ValueError, "Initial SOC should be between 0 and 1" ): - pybamm.lithium_ion.get_initial_stoichiometry_half_cell(2, parameter_values) + pybamm.lithium_ion.get_initial_stoichiometry_half_cell( + 2, parameter_values_half_cell + ) class TestGetInitialOCP(TestCase): diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py index ab699f0365..fa6e2398ee 100644 --- a/tests/unit/test_parameters/test_parameter_values.py +++ b/tests/unit/test_parameters/test_parameter_values.py @@ -15,6 +15,7 @@ lico2_ocp_Dualfoil1998, lico2_diffusivity_Dualfoil1998, ) +from pybamm.expression_tree.exceptions import OptionError import casadi @@ -120,28 +121,55 @@ def test_set_initial_stoichiometries(self): self.assertAlmostEqual(y, y_0 - 0.4 * (y_0 - y_100)) def test_set_initial_stoichiometry_half_cell(self): - param = pybamm.lithium_ion.DFN({"working electrode": "positive"}).default_parameter_values - param = param.set_initial_stoichiometry_half_cell(0.4, inplace=False, options={"working electrode": "positive"}) - param_0 = param.set_initial_stoichiometry_half_cell(0, inplace=False, options={"working electrode": "positive"}) - param_100 = param.set_initial_stoichiometry_half_cell(1, inplace=False, options={"working electrode": "positive"}) + param = pybamm.lithium_ion.DFN( + {"working electrode": "positive"} + ).default_parameter_values + param = param.set_initial_stoichiometry_half_cell( + 0.4, inplace=False, options={"working electrode": "positive"} + ) + param_0 = param.set_initial_stoichiometry_half_cell( + 0, inplace=False, options={"working electrode": "positive"} + ) + param_100 = param.set_initial_stoichiometry_half_cell( + 1, inplace=False, options={"working electrode": "positive"} + ) y = param["Initial concentration in positive electrode [mol.m-3]"] y_0 = param_0["Initial concentration in positive electrode [mol.m-3]"] y_100 = param_100["Initial concentration in positive electrode [mol.m-3]"] self.assertAlmostEqual(y, y_0 - 0.4 * (y_0 - y_100)) - #inplace for 100% coverage - param_t = pybamm.lithium_ion.DFN({"working electrode": "positive"}).default_parameter_values - param_t.set_initial_stoichiometry_half_cell(0.4, inplace=True, options={"working electrode": "positive"}) + # inplace for 100% coverage + param_t = pybamm.lithium_ion.DFN( + {"working electrode": "positive"} + ).default_parameter_values + param_t.set_initial_stoichiometry_half_cell( + 0.4, inplace=True, options={"working electrode": "positive"} + ) y = param_t["Initial concentration in positive electrode [mol.m-3]"] - param_0 = pybamm.lithium_ion.DFN({"working electrode": "positive"}).default_parameter_values - param_0.set_initial_stoichiometry_half_cell(0, inplace=True, options={"working electrode": "positive"}) + param_0 = pybamm.lithium_ion.DFN( + {"working electrode": "positive"} + ).default_parameter_values + param_0.set_initial_stoichiometry_half_cell( + 0, inplace=True, options={"working electrode": "positive"} + ) y_0 = param_0["Initial concentration in positive electrode [mol.m-3]"] - param_100 = pybamm.lithium_ion.DFN({"working electrode": "positive"}).default_parameter_values - param_100.set_initial_stoichiometry_half_cell(1, inplace=True, options={"working electrode": "positive"}) + param_100 = pybamm.lithium_ion.DFN( + {"working electrode": "positive"} + ).default_parameter_values + param_100.set_initial_stoichiometry_half_cell( + 1, inplace=True, options={"working electrode": "positive"} + ) y_100 = param_100["Initial concentration in positive electrode [mol.m-3]"] self.assertAlmostEqual(y, y_0 - 0.4 * (y_0 - y_100)) + # test error + param = pybamm.ParameterValues("Chen2020") + with self.assertRaisesRegex(OptionError, "working electrode"): + param.set_initial_stoichiometry_half_cell( + 0.1, options={"working electrode": "negative"} + ) + def test_set_initial_ocps(self): options = { "open-circuit potential": "MSMR", From ac5cabdfe5c85d7e3a291ef09af5944e5332a5c7 Mon Sep 17 00:00:00 2001 From: Alec Bills Date: Mon, 23 Oct 2023 12:13:21 -0700 Subject: [PATCH 61/75] fix esoh bug --- .../lithium_ion/electrode_soh_half_cell.py | 4 ++-- pybamm/simulation.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) 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 1e237e73c8..8c22cf2ada 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 @@ -21,7 +21,7 @@ class ElectrodeSOHHalfCell(pybamm.BaseModel): """ - def __init__(self, name="Electrode-specific SOH model"): + def __init__(self, name="ElectrodeSOH model"): pybamm.citations.register("Mohtat2019") super().__init__(name) param = pybamm.LithiumIonParameters({"working electrode": "positive"}) @@ -140,7 +140,7 @@ def get_min_max_stoichiometries( parameter_values : pybamm.ParameterValues The parameter values to use in the calculation """ - esoh_model = pybamm.lithium_ion.ElectrodeSOHHalfCell(options) + esoh_model = pybamm.lithium_ion.ElectrodeSOHHalfCell("ElectrodeSOH") param = pybamm.LithiumIonParameters(options) esoh_sim = pybamm.Simulation(esoh_model, parameter_values=parameter_values) Q_w = parameter_values.evaluate(param.p.Q_init) diff --git a/pybamm/simulation.py b/pybamm/simulation.py index 8805e925d0..380105d215 100644 --- a/pybamm/simulation.py +++ b/pybamm/simulation.py @@ -552,7 +552,7 @@ def solve( ) if ( self.operating_mode == "without experiment" - or self._model.name == "ElectrodeSOH model" + or "ElectrodeSOH" in self._model.name ): if t_eval is None: raise pybamm.SolverError( From 4535854b795468def4b9a5c108a83cfd20fef228 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 23 Oct 2023 19:39:25 +0000 Subject: [PATCH 62/75] chore: update pre-commit hooks MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit updates: - [github.com/astral-sh/ruff-pre-commit: v0.0.292 → v0.1.1](https://github.com/astral-sh/ruff-pre-commit/compare/v0.0.292...v0.1.1) --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 8d253a49ee..12e48d913b 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: "v0.0.292" + rev: "v0.1.1" hooks: - id: ruff args: [--fix, --ignore=E741, --exclude=__init__.py] From 1bca02ac2bcd9d2692ea789d8fef43ef09803e87 Mon Sep 17 00:00:00 2001 From: John Brittain Date: Wed, 25 Oct 2023 10:12:18 +0100 Subject: [PATCH 63/75] Amend changelog --- CHANGELOG.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 4d2034e945..42e1d90f91 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,7 @@ # [Unreleased](https://github.com/pybamm-team/PyBaMM/) +- Fixed a bug where the JaxSolver would fails when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423)) + # [v23.9rc0](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-10-31 ## Features @@ -19,7 +21,6 @@ ## Bug fixes -- Fixed a bug where the JaxSolver would fails when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423)) - Fixed a bug where empty lists passed to QuickPlot resulted in an IndexError and did not return a meaningful error message ([#3359](https://github.com/pybamm-team/PyBaMM/pull/3359)) - Fixed a bug where there was a missing thermal conductivity in the thermal pouch cell models ([#3330](https://github.com/pybamm-team/PyBaMM/pull/3330)) - Fixed a bug that caused incorrect results of “{Domain} electrode thickness change [m]” due to the absence of dimension for the variable `electrode_thickness_change`([#3329](https://github.com/pybamm-team/PyBaMM/pull/3329)). From 09e8cc3a65215d04fc416c1cfd578b4c845e7226 Mon Sep 17 00:00:00 2001 From: John Brittain Date: Wed, 25 Oct 2023 14:51:47 +0100 Subject: [PATCH 64/75] Disable MyST navigation by keys --- docs/conf.py | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/conf.py b/docs/conf.py index 74d1f76488..49e5cf3dc9 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -154,6 +154,7 @@ "navbar_end": ["theme-switcher", "navbar-icon-links"], # add Algolia to the persistent navbar, this removes the default search icon "navbar_persistent": "algolia-searchbox", + "navigation_with_keys": False, "use_edit_page_button": True, "analytics": { "plausible_analytics_domain": "docs.pybamm.org", From 6cc394076b4d63f19235f1c0c5471bb5b2837599 Mon Sep 17 00:00:00 2001 From: jsbrittain <98161205+jsbrittain@users.noreply.github.com> Date: Fri, 27 Oct 2023 10:20:04 +0100 Subject: [PATCH 65/75] Update CHANGELOG.md Co-authored-by: Saransh Chopra --- CHANGELOG.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 42e1d90f91..b02df8ed4c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,7 @@ # [Unreleased](https://github.com/pybamm-team/PyBaMM/) +## Bug fixes + - Fixed a bug where the JaxSolver would fails when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423)) # [v23.9rc0](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-10-31 From aeda214110049e0ff5af6a88e49a9a01f9182241 Mon Sep 17 00:00:00 2001 From: Rjchauhan18 Date: Sat, 28 Oct 2023 17:57:09 +0530 Subject: [PATCH 66/75] fix/Replace nbqa-ruff with ruff --- .pre-commit-config.yaml | 10 ++-------- ruff.toml | 6 ++++++ 2 files changed, 8 insertions(+), 8 deletions(-) create mode 100644 ruff.toml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 12e48d913b..01eb62bcd0 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -4,18 +4,12 @@ ci: repos: - repo: https://github.com/astral-sh/ruff-pre-commit - rev: "v0.1.1" + rev: "v0.1.3" hooks: - id: ruff + types_or: [python, pyi, jupyter] args: [--fix, --ignore=E741, --exclude=__init__.py] - - repo: https://github.com/nbQA-dev/nbQA - rev: 1.7.0 - hooks: - - id: nbqa-ruff - additional_dependencies: [ruff==0.0.284] - args: ["--fix","--ignore=E501,E402"] - - repo: https://github.com/adamchainz/blacken-docs rev: "1.16.0" hooks: diff --git a/ruff.toml b/ruff.toml new file mode 100644 index 0000000000..ea62900699 --- /dev/null +++ b/ruff.toml @@ -0,0 +1,6 @@ +[tool.ruff] +extend-include = ["*.ipynb"] + + +[tool.ruff.lint] +ignore = ["E402","E703"] From 641075298495c78cf5c18c02df60c9e6da95087f Mon Sep 17 00:00:00 2001 From: Rjchauhan18 Date: Sat, 28 Oct 2023 18:25:02 +0530 Subject: [PATCH 67/75] added pyproject.toml and removed ruff.toml because of failing pre-commit --- ruff.toml => pyproject.toml | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename ruff.toml => pyproject.toml (100%) diff --git a/ruff.toml b/pyproject.toml similarity index 100% rename from ruff.toml rename to pyproject.toml From 6cb6347b3adec8ddf5848bf0606586de12d6fe14 Mon Sep 17 00:00:00 2001 From: Rjchauhan18 Date: Sat, 28 Oct 2023 19:53:18 +0530 Subject: [PATCH 68/75] Added ruff.toml again and deleted pyproject.toml --- pyproject.toml | 6 ------ ruff.toml | 2 ++ 2 files changed, 2 insertions(+), 6 deletions(-) delete mode 100644 pyproject.toml create mode 100644 ruff.toml diff --git a/pyproject.toml b/pyproject.toml deleted file mode 100644 index ea62900699..0000000000 --- a/pyproject.toml +++ /dev/null @@ -1,6 +0,0 @@ -[tool.ruff] -extend-include = ["*.ipynb"] - - -[tool.ruff.lint] -ignore = ["E402","E703"] diff --git a/ruff.toml b/ruff.toml new file mode 100644 index 0000000000..a20f5ea464 --- /dev/null +++ b/ruff.toml @@ -0,0 +1,2 @@ +[lint] +ignore = ["E402","E703"] From 5431decb218cfe70b612e392d3c30cd117558d89 Mon Sep 17 00:00:00 2001 From: Rjchauhan18 Date: Sat, 28 Oct 2023 21:55:36 +0530 Subject: [PATCH 69/75] Added [lint.per-file-ignores] in ruff.toml --- ruff.toml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ruff.toml b/ruff.toml index a20f5ea464..56d383806f 100644 --- a/ruff.toml +++ b/ruff.toml @@ -1,2 +1,2 @@ -[lint] -ignore = ["E402","E703"] +[lint.per-file-ignores] +"**.ipynb" = ["E402", "E703"] From 1bc0ba788e3eef37138a8309b787bb3c173c83c6 Mon Sep 17 00:00:00 2001 From: Rjchauhan18 Date: Sun, 29 Oct 2023 19:07:51 +0530 Subject: [PATCH 70/75] trying to move jupyter nootebook configuration of pro-commit-config.yaml to ruff.toml --- .pre-commit-config.yaml | 1 - ruff.toml | 2 ++ 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 01eb62bcd0..46d6918d95 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -7,7 +7,6 @@ repos: rev: "v0.1.3" hooks: - id: ruff - types_or: [python, pyi, jupyter] args: [--fix, --ignore=E741, --exclude=__init__.py] - repo: https://github.com/adamchainz/blacken-docs diff --git a/ruff.toml b/ruff.toml index 56d383806f..7c1040e9d8 100644 --- a/ruff.toml +++ b/ruff.toml @@ -1,2 +1,4 @@ +extend-include = ["*.ipynb"] + [lint.per-file-ignores] "**.ipynb" = ["E402", "E703"] From e6eec081ea20cafbdf68cba46e27d83360f037bc Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 30 Oct 2023 19:28:27 +0000 Subject: [PATCH 71/75] chore: update pre-commit hooks MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit updates: - [github.com/astral-sh/ruff-pre-commit: v0.1.1 → v0.1.3](https://github.com/astral-sh/ruff-pre-commit/compare/v0.1.1...v0.1.3) --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 12e48d913b..88d0f264ee 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: "v0.1.1" + rev: "v0.1.3" hooks: - id: ruff args: [--fix, --ignore=E741, --exclude=__init__.py] From c0f7ead225d2b81ac394c63043a4473de2b4f10c Mon Sep 17 00:00:00 2001 From: Rjchauhan18 Date: Tue, 31 Oct 2023 15:57:41 +0530 Subject: [PATCH 72/75] fix changes as per review added to pre-commit-config.yaml "--show-fixes" and some ruff.toml changes --- .pre-commit-config.yaml | 2 +- ruff.toml | 4 ++++ 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 46d6918d95..76ff077177 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -7,7 +7,7 @@ repos: rev: "v0.1.3" hooks: - id: ruff - args: [--fix, --ignore=E741, --exclude=__init__.py] + args: [--fix, --show-fixes] - repo: https://github.com/adamchainz/blacken-docs rev: "1.16.0" diff --git a/ruff.toml b/ruff.toml index 7c1040e9d8..7304d64570 100644 --- a/ruff.toml +++ b/ruff.toml @@ -1,4 +1,8 @@ extend-include = ["*.ipynb"] +extend-exclude = ["__init__.py"] + +[lint] +ignore = ["E741"] [lint.per-file-ignores] "**.ipynb" = ["E402", "E703"] From f91fed90a16b59f7c1e3162f9fed4d904a813890 Mon Sep 17 00:00:00 2001 From: Rjchauhan18 Date: Tue, 31 Oct 2023 18:18:16 +0530 Subject: [PATCH 73/75] added types_or: [python, pyi, jupyter] in pre-commit-config.yaml --- .pre-commit-config.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 76ff077177..5d7c85492f 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -8,6 +8,7 @@ repos: hooks: - id: ruff args: [--fix, --show-fixes] + types_or: [python, pyi, jupyter] - repo: https://github.com/adamchainz/blacken-docs rev: "1.16.0" From 9f7121b984c4ba176aa9c6b0e3b88313c7d75232 Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Sat, 11 Nov 2023 17:13:14 +0530 Subject: [PATCH 74/75] #3442 Update release workflow instructions to add details about `conda-forge` --- .github/release_workflow.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/release_workflow.md b/.github/release_workflow.md index 7afa24a6d6..8334a1d5dc 100644 --- a/.github/release_workflow.md +++ b/.github/release_workflow.md @@ -1,6 +1,6 @@ # Release workflow -This file contains the workflow required to make a `PyBaMM` release on GitHub and PyPI by the maintainers. +This file contains the workflow required to make a `PyBaMM` release on GitHub, PyPI, and conda-forge by the maintainers. ## rc0 releases (automated) @@ -77,3 +77,5 @@ Some other essential things to check throughout the release process - git tag -f git push -f # can only be carried out by the maintainers ``` +- If changes are made to the API, console scripts, entry points, new optional dependencies are added, support for major Python versions is dropped or added, or core project information and metadata are modified at the time of the release, make sure to update the `meta.yaml` file in the `recipe/` folder of the [conda-forge/pybamm-feedstock](https://github.com/conda-forge/pybamm-feedstock) repository accordingly by following the instructions in the [conda-forge documentation](https://conda-forge.org/docs/maintainer/updating_pkgs.html#updating-the-feedstock-repository) and re-rendering the recipe +- The conda-forge release workflow will automatically be triggered following a stable PyPI release, and the aforementioned updates can be carried out either in a personal fork of the feedstock repository, or directly in the main repository by pushing changes to the automated PR created by the conda-forge-bot. From 2324af9254e4e567264c073f3d0da4e19fc4f230 Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Sun, 12 Nov 2023 01:26:33 +0530 Subject: [PATCH 75/75] Add a sentence about manual PRs in the feedstock Co-Authored-By: Saransh Chopra --- .github/release_workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/release_workflow.md b/.github/release_workflow.md index 8334a1d5dc..ed94962b92 100644 --- a/.github/release_workflow.md +++ b/.github/release_workflow.md @@ -78,4 +78,4 @@ Some other essential things to check throughout the release process - git push -f # can only be carried out by the maintainers ``` - If changes are made to the API, console scripts, entry points, new optional dependencies are added, support for major Python versions is dropped or added, or core project information and metadata are modified at the time of the release, make sure to update the `meta.yaml` file in the `recipe/` folder of the [conda-forge/pybamm-feedstock](https://github.com/conda-forge/pybamm-feedstock) repository accordingly by following the instructions in the [conda-forge documentation](https://conda-forge.org/docs/maintainer/updating_pkgs.html#updating-the-feedstock-repository) and re-rendering the recipe -- The conda-forge release workflow will automatically be triggered following a stable PyPI release, and the aforementioned updates can be carried out either in a personal fork of the feedstock repository, or directly in the main repository by pushing changes to the automated PR created by the conda-forge-bot. +- The conda-forge release workflow will automatically be triggered following a stable PyPI release, and the aforementioned updates should be carried out directly in the main repository by pushing changes to the automated PR created by the conda-forge-bot. A manual PR can also be created if the updates are not included in the automated PR for some reason. This manual PR **must** bump the build number in `meta.yaml` and **must** be from a personal fork of the repository.