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Cobaya Tutorials

Welcome to the Cobaya Tutorials repository! This repository contains a series of tutorials in the form of jupyter notebooks designed to help users understand how cosmological inference works and effectively learn and use Cobaya (Cosmological Bayesian Analysis), a powerful tool for cosmological parameter estimation and model testing.

Overview

Cobaya is a Python-based framework for cosmological parameter estimation and model comparison. These tutorials provide step-by-step guidance on using Cobaya to perform various types of cosmological analyses. Whether you're new to cosmological analysis or looking to refine your skills with Cobaya, these tutorials will help you keeping your skills up to date.

Exercises

  • Tutorial 1: It contains two exercises about (1) how to sample a multi-variate Gaussian and Ring likelihood distributions, and (2) how to run simple cosmology chains.
  • Tutorial 2: It teaches the user how to use the get_model() wrapper of Cobaya to retrieve all internal calculations and requirements without hacking the source code.

Installation

  • Clone the Repository:

    git clone https://github.com/gcanasherrera/cobaya-tutorials.git
cd cobaya-tutorials

  • Requirements:

    python, latest Cobaya version and its cosmology dependencies. We recommend running the notebooks in Google Colab, and get Cobaya and dependencies installed in the cloud by adding in the first cells:

    !pip install cobaya
!cobaya-install cosmo -p .

Contributing

If you would like to contribute, follow the steps below:

  1. Open an issue to let the cloelite maintainers know about your contribution plans
  2. Fork the repository
  3. Create a new branch: 
    git checkout -b feature/your-feature-name
  4. Commit your changes: 
    git commit -m 'Add some feature'
  5. Push to the branch: 
    git push origin feature/your-feature-name
  6. Open a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

Dr. Guadalupe Cañas-Herrera.