calisim: Examples and Workshop Material

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A toolbox for the calibration and evaluation of simulation models.

Table of contents

• calisim: Examples and Workshop Material
• Table of contents
• Introduction
• Workshop
  • Setup
    • Binder
    • Google Colab
    • Virtual environment
    • Docker

Introduction

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.

Workshop

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

This workshop material covers the following example models:

1. JFruit2
2. TEgenomeSimulator

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

Setup

To setup the 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

Binder

Click this link to launch the workshop material within Binder.

Note that you may need to wait roughly 5 minutes for the workshop Docker image to be pulled when first using Binder.

Google Colab

JFruit2 workshop content may be accessed via the following links:

• Introduction
• Sensitivity analysis
• Optimisation
• Advanced

Virtual environment

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

Docker

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

docker compose up calisim

# ctrl + C to exit

This will launch JupyterLab within your browser.