pybamm-param: PyBaMM Parameter Optimization Tool

Important

All the functionality in pybamm-param is now available in PyBOP, which will be the focus of future development. We recommend new users to use PyBOP instead of pybamm-param.

pybamm-param is a tool to fit PyBaMM models to experimental data in order to determine the model parameter values. pybamm-param provides helper classes to handle the most common parameterisation problems, and allows the users to easily mix combine cost functions and optimisers to suit their needs. Examples on how to run this package can be found in the examples folder

🚀 Installing pybamm-param

Using pip

The recommended way to install pybamm-param is by installing the latest release from PyPI. This can be done running

pip install pbparam

We strongly recommend using virtual environments, see more detailed instructions below (steps 1 and 2).

Install from source

Another option is to install from source. This is not recommended, unless you want to make edits to the code.

The first step is to install virtualenv in order to create virtual environments

pip install virtualenv

The module dependencies are listed in pyproject.toml, the dependancies which are non optional which are installed with the package.

The optional dependancies are split into dev and docs. dev are used for testing and linting, docs are used for building the sphinx documentation.

Linux & MacOS

1. Create a virtual environment (this is strongly recommended to avoid clashes with the dependencies)

   virtualenv --python="<path to python 3.11>" env

2. Activate the virtual environment

   source env/bin/activate

   The virtual environment can later be deactivated (if needed) by running

   deactivate

3. Install packages into the virtual envronment

   pip install -e ./[dev,docs]

Windows

1. Create a virtual environment (this is strongly recommended to avoid clashes with the dependencies)

   python -m virtualenv env

2. Activate the virtual environment

   env\Scripts\activate.bat

   The virtual environment can later be deactivated (if needed) by running

   deactivate

3. Install package from Github

   pip install -e .\\[dev,docs]

🛠️ Contributing to pybamm-param

If you'd like to help us develop pybamm-param by adding new methods, writing documentation, or fixing embarrassing bugs, please have a look at these guidelines first.

Notes

requirements.txt

This is not intended for modification or use installing dependencies, it is a result of a git runner to ensure full package information is included on all pushes. Strict requirements are found in pyproject.toml

coverage.xml

This is produced during a git run and not intended to be modified directly

FAIRS

We are working towards conformity with the FAIRS software standards for research software. The following items should help detail contributions to and ways to work with this software:

Codemeta

The metadata is available in machine-readable format in the codemeta.json file.

Contributors

Active

• Ferran Brosa Planella
• Philip John Grylls

Inactive or past

• Dhammika Widanalage
• Muhammed Nedim Sogut
• Alexandru Pascu

Other Information

• Funder: The development of pybamm-param has been supported by the University of Warwick EPSRC Impact Acceleration Account.
• Programming language: Python
• Keywords: parameter estimation, battery modelling, continuum models, Doyle-Fuller-Newman model, Single Particle Model, lithium-ion batteries.
• Date created: 01/03/2024 (first release - v0.1)