calisim
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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.
Features and Functionality
A standardised and streamlined interface to multiple calibration procedures and libraries.
A low-code library, allowing modellers to rapidly construct multiple workflows for many calibration procedures.
An object-oriented programming architecture, allowing users to easily extend and modify calibration workflows for their own complex modelling use-cases.
An unopinionated approach to working with simulation models, allowing users to calibrate both Python-based and non-Python-based models.
Optional integration with PyTorch for access to more sophisticated Gaussian Process and deep learning surrogate models, state-of-the-art evolutionary algorithms, and deep generative modelling for simulation-based inference.
Communication
GitHub Discussions for questions.
GitHub issues for bug reports and feature requests.
Contributions and Support
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.