Tutorials

Tutorials


CosmoStat is committed to the philosophy of reproducible research, endeavouring to provide source code and data for all publications. In this spirit, we have additionally put significant effort into providing useful educational materials. The aim being to provide other researchers with an in-depth understanding of the various tools we use in our work.

All tutorials can be found on the CosmoStat GitHub repository.


  • This tutorial was originally presented at the ninth edition of the Astronomical Data Analysis (ADAIX) summer school held in Valencia in 2018. The objective is to provide a beginner level introduction to the concept of sparsity, in particular as a regularisation method for solving linear inverse problems. The tutorial does not provide an in-depth mathematical background nor detailed explanations for every topic. Tutees should supplement this tutorial with further reading of the various references provided in the notebooks for a more comprehensive understanding of the subject.

  • The objective of this tutorial is to provide a first look at Python for beginners. The level is aimed at individuals with little or no experience whatsoever with Python. Experienced users are unlikely to benefit from this tutorial.

  • The objective is to provide a beginner level introduction to the concept of low-rank approximation, in particular as a regularisation method for solving linear inverse problems. The tutorial does not provide an in-depth mathematical background nor detailed explanations for every topic. Tutees should supplement this tutorial with further reading of the various references provided in the notebooks for a more comprehensive understanding of the subject.