myokit 1.37.4

A modeling and simulation tool for cardiac cellular electrophysiology

Myokit is a tool for modeling and simulation of cardiac cellular electrophysiology. It's open-source, written in Python, hosted on GitHub and available on PyPi. For the latest documentation, see myokit.readthedocs.io.

More information, including examples and an installation guide, is available on myokit.org. A list of changes introduced in each Myokit release is provided in the Changelog.

Install

For full installation details (on linux, mac, or windows), please see https://myokit.org/install. A shorter installation guide for experienced users is given below.

To install Myokit, using PyQt5 for Myokit's GUI components, run:

pip install myokit[pyqt]

to use PySide2 instead, run:

pip install myokit[pyside]

If you're not planning to use the GUI components (for example to run simulations on a server), you can simply install with

pip install myokit

On Linux and Windows, start menu icons can be added by running

python -m myokit icons

To run single-cell simulations, CVODES must be installed (but Windows users can skip this step, as binaries are included in the pip install). In addition, Myokit needs a working C/C++ compiler to be present on the system.

Existing Myokit installations can be upgraded using

pip install --upgrade myokit

Quick-start guide

After installation, to quickly test if Myokit works, type

python -m myokit run example

or simply

myokit run example

To open an IDE window, type

myokit ide

To see what else Myokit can do, type

myokit -h

Contributing to Myokit

Contributing to Myokit is as easy as asking questions or posting issues and feature requests, and we have pledged to make this an inclusive experience.

We are always looking for people to contribute code too! Guidelines to help you do this are provided in CONTRIBUTING.md, but before diving in please open an issue so that we can first discuss what needs to be done.

A high-level plan for Myokit's future is provided in the roadmap.

Meet the team!

Myokit's development is driven by a team at the Universities of Nottingham, Oxford, and Macao, led by Michael Clerx (Nottingham). It is guided by an external advisory group composed of Jordi Heijman (Maastricht University), Trine Krogh-Madsen (Weill Cornell Medicine), and David Gavaghan (Oxford).

Citing Myokit

If you use Myokit in your research, please cite it using the information in our CITATION file.

We like to keep track of who's using Myokit for research (based on publications) and teaching (based on peronsal correspondence). If you've used Myokit in teaching, we're always happy to hear about it so please get in touch via the discussion board!