ionbench 1.0.1

A benchmarking tool for comparing different parameter optimization algorithms for ion channel models

ionBench

A benchmarking tool for comparing different parameter optimization algorithms for ion channel models.

Project Structure

The tree structure of this project is outlined below.

├───docs
├───ionbench
├───scipts
│   ├───text
│   └───figures
└───test

The docs directory contains information and guides on how to use the benchmarker problems and the optimisation algorithms.

The ionbench directory contains the majority of the code, including the benchmarker and problems classes and the different optimisation algorithms. This is what is installed using pip.

The scripts directory contains scripts for generating figures, tables and data for the paper.

The test directory contains unit tests for ensuring changes do not break previous functionality.

Installation

Installing ionBench package

The ionBench package can be installed from PyPI using pip.

pip install ionbench

Note that ionBench uses myokit to do its simulations, which relies on CVODES (from Sundials). For Linux and macOS users a working installation of CVODES is required. For Windows users, CVODES should be automatically installed with myokit.

Getting the scripts and results

The scripts are results from ionBench are available in this repo. If you also want access to these, you should:

1. Clone the repository.
2. Install ionBench locally using pip (navigate to the the root directory of the repo and run pip install -e .) or install ionBench from PyPI (pip install ionbench).
3. Navigate to the scripts directory and run the scripts you are interested in.

Getting Started

If you want to use ionBench, check out the introduction.md and tutorial.ipynb in the docs directory.

Workflow

The intended workflow for using the benchmarker is to generate a benchmarker object, set up the optimisers modification and apply it to the benchmarker, and pass the benchmarker into the optimisation algorithm to evaluate. All optimisers should accept a single benchmarker as input with all other inputs being optional.

import ionbench

bm = ionbench.problems.staircase.HH()
modification = ionbench.optimisers.pints_optimisers.cmaes_pints.get_modification()
modification.apply(bm)
optimisedParameters = ionbench.optimisers.scipy_optimisers.nelderMead_scipy.run(bm)