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A toolbox for the calibration and evaluation of simulation models.

• calisim: Examples and Workshop Material
• Table of contents
• Introduction
• Installation
• Usage with Docker
• Usage with Binder
• Workshop
  • Helpful background knowledge
  • Coordinators
  • Announcements
• Communication
• Contributions and Support
• License

calisim is an open-source, low-code model calibration library that streamlines and standardises your workflows, while aiming to be as flexible and extensible as needed to support more complex use-cases. Using calisim will speed up your experiment cycle substantially and make you more productive.

calisim is primarily a wrapper around popular libraries and frameworks including Optuna, PyMC, scikit-learn, and emcee among many others. The design and simplicity of calisim was inspired by the scikit-learn and PyCaret libraries.

To setup the examples and workshop material, we will first need to clone the GitHub repo like so:

git clone https://github.com/Plant-Food-Research-Open/calisim-examples-workshop-material.git

cd calisim-examples-workshop-material

To run the workshop material within a Python virtual environment, first ensure that Poetry (a Python dependency manager) is installed.

For this workshop, we will use an older version of Poetry (1.8.5):

poetry self update 1.8.5

After which, run the following to install all required dependencies:

export POETRY_VIRTUALENVS_IN_PROJECT=true # Install .venv your project directory, rather than home directory
poetry install --no-root --with dev,docs
poetry shell

Finally, launch JupyterLab in your web browser:

jupyter lab

To run the examples and workshop material within a Docker container, execute the following:

wget https://raw.githubusercontent.com/Plant-Food-Research-Open/calisim-examples-workshop-material/refs/heads/main/docker-compose.yaml
docker compose up calisim

# ctrl + C to exit

This will launch JupyterLab within your browser.

Click this link to launch the examples and workshop material within Binder.

Note that you may need to wait roughly 2 or more minutes for the workshop Docker image to be pulled when first using Binder.

Workshop material for calisim may be found in the workshop directory.

This workshop material includes the following example models:

1. JFruit2
2. TEgenomeSimulator

We will work though basic examples for optimisation and sensitivity analysis, alongside more complex calibration methods.

Click this link to launch the workshop material within Binder, which is recommended for users who do not wish to configure the workshop environment locally.

Familiarity with Python and computer simulation modelling. Some introductory-level understanding of black-box optimisation, sensitivity analysis, and Bayesian calibration.

• Hana Liang ([email protected])
• Ting-Hsuan Chen ([email protected])
• Jingjing Zhang ([email protected])
• Chris Van Houtte ([email protected])
• James Bristow ([email protected])

To view workshop announcements, please select this link.

Please refer to the following links:

• GitHub Discussions for questions.
• GitHub Issues for bug reports and feature requests.

Contributions are more than welcome. For general guidelines on how to contribute to this project, take a look at CONTRIBUTING.md.

For our community code of conduct, please also view CODE_OF_CONDUCT.md.

calisim is published under the Apache License (see LICENSE).

View all third party licenses (see third_party)