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.
We always welcome new tutorial requests, just click here.
- Presentation Tips |
Authors: @pettorin
This tutorial provides tips on how to adapt presentations for different goals. The tutorial is mainly meant for scientists, but several tips can be useful for other types of talks.
- Cosmology with Python wrappers for Einstein-Boltzmann Codes |
Authors: @santiagocasas
This tutorial is comprised of a series of Jupyter notebooks with simple demonstrations and exercises on how to useCAMBandCLASSusing python wrappers. The code is designed for non-experts in the field, therefore it includes relatively simple explanations of cosmological concepts. It intends to show a general overview of the things that are possible with Einstein-Boltzmann codes and python.
- Introduction to Python |
Authors: @sfarrens, @santiagocasas
- Tutorial 1: Beginner Topics 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.
- Tutorial 2: Intermediate and Advanced Topics The objective of this tutorial is to provide a more in-depth look at object-oriented and pythonic coding. The level is aimed at individuals with some experience with Python and good knowledge of basic object types. This tutorial will likely benefit all except the most advanced users.
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C++ |
Authors: @kansal9
This tutorial aims to help newcomers learn C++ and solve their programming problems. It is assumed that readers are already familiar with C, or at least that they do not have any difficulty reading C code. In other words, those who have experience in C and peo·ple who desire to quickly understand the features of modern C++ in a short period of time are well suited to follow this tutorial. -
Sparsity |
Authors: @sfarrens
This tutorial is comprised of a series of Jupyter notebooks that demonstrate how the tools implemented in sparsity work as well as showing the applicability of these tools to various simple problems. -
Low-Rank |
Authors: @sfarrens
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. -
TensorFlow |
Authors: @zaccharieramzi, @EiffL- First Steps with TensorFlow:
A short introduction to the basic concepts underpinning TensorFlow, in particular automatic differentation. - MRI reconstruction with TensorFlow:
An illustration of how to use TensorFlow to solve a toy MRI reconstruction problem using classical iterative reconstructions, and a deep learning approach.
- First Steps with TensorFlow:
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Git Tutorial |
Authors: @zaccharieramzi
This tutorial will help you practice the basics of the GitHub flow and how to work on open source projects. -
Jekyll Tutorial |
Authors: @sfarrens
The objective of this tutorial is to introduce Jekyll and show you how to build a website that you can host on GitHub for free. -
Make and CMake Tutorial |
Authors: @sfarrens
This tutorial is designed to provide a first look at using Make and CMake to build C/C++ projects. -
Introduction to Docker for Data Scientists |
Authors: @EiffL
This tutorial demonstrates how to create a Docker container to distribute a complete Jupyter notebook environment.
Thanks goes to these wonderful people (emoji key):
 Samuel Farrens 🖋 🤔 🚧 |
 Santiago Casas 🖋 🤔 |
 Zaccharie Ramzi 🖋 🤔 |
 Francois Lanusse 🤔 🚧 |
 Vanshika Kansal 🖋 |
 pettorin 🤔 🖋 |
This project follows the all-contributors specification. Contributions of any kind welcome!