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.

Tutorials can be found on the CosmoStat:


  • This tutorial was originally presented at the ninth edition of the Astronomical Data Analysis (ADAIX) summer school held in Valencia

  • The goal of this tutorial is to introduce researchers to bash and scripting. It is a short tutorial that shows

  • This tutorial is comprised of a series of Jupyter notebooks with simple demonstrations and exercises on how to use CAMB and CLASS using python

  • This tutorial demonstrates how to create a Docker container to distribute a complete Jupyter notebook environment.

  • This tutorial will help you practice the basics of the GitHub flow and how to work on open source projects.

  • This tutorial is designed to provide a first look at using make and CMake to build C/C++ projects.

  • The objective of this tutorial is to provide a first look at Python for beginners. The level is aimed at

  • The objective of this tutorial is to introduce Jekyll and show you how to build a website that you can

  • The objective is to provide a beginner level introduction to the concept of low-rank approximation, in particular as a regularisation

  • This tutorial is designed to provide a first look at using make and CMake to build C/C++ projects.

  • This tutorial is meant to present how one can compute a power spectrum from a lensing map (in the flat

  • This tutorial provides tips on how to adapt presentations for different goals. The tutorial is mainly meant for scientists, but

  • This tutorial introduces some techniques for deterministic and memory profiling of Python scripts, followed by some tips on how to

  • This intends to be a first introduction to TensorFlow 2.0 not as much from a machine learning point of view