PyBaMM user guide#

This guide is an overview and explains the important features; details are found in API documentation.

Getting started

Fundamentals and usage

Contributing guide

Example notebooks#

PyBaMM ships with example notebooks that demonstrate how to use it and reveal some of its functionalities and its inner workings. For more examples, see the Examples section.

Getting Started

Tutorial 1 - How to run a model
Tutorial 2 - Compare models
Tutorial 3 - Basic plotting
Tutorial 4 - Setting parameter values
Tutorial 5 - Run experiments
Tutorial 6 - Managing simulation outputs
Tutorial 7 - Model options
Tutorial 8 - Solver options
Tutorial 9 - Changing the mesh

Creating Models

Creating a simple ODE model
Creating a simple PDE model
A step towards the Single Particle Model
Comparing full and reduced-order models
A half cell model
Creating a Simple Model for SEI Growth

Performance

Understanding the PyBaMM pipeline and performance profile of the Simulation class
Using Input Parameters to efficiently re-run simulations with different parameters
The PyBaMM Solvers
Interpolation and evaluation points
Solver tolerances
Defining output variables to reduce memory usage
Running many simulations in parallel using OpenMP

Telemetry#

PyBaMM optionally collects anonymous usage data to help improve the library. This telemetry is opt-in and can be easily disabled. Here’s what you need to know:

  • What is collected: Basic usage information like PyBaMM version, Python version, and which functions are run.

  • Why: To understand how PyBaMM is used and prioritize development efforts.

  • Opt-out: To disable telemetry, set the environment variable PYBAMM_DISABLE_TELEMETRY=true (or any value other than false) or use pybamm.telemetry.disable() in your code.

  • Privacy: No personal information (name, email, etc) or sensitive information (parameter values, simulation results, etc) is ever collected.