LPP dev guide¶
Efficient scientific coding¶
You will find here material to help you get started with your coding. Depending on whether you mostly do data analysis or numerical modeling, you'll need slightly different tools and methods. the advice we give you will help to be efficient, rigorous and to write code than you'll be able to use, maintain and share in the long run. Now remember, If you're a PhD or a post-doc, following these advice will not only help you improving the quality and reproducibility of your science, but also will make all your coding efforts reusable for your future you and by people in the lab once you're gone. More importantly, it will give you the basic knowledge you need to legitimately claim for a data science / computing science position in the private sector.
- The commandments of programming
- Common tools and methodology you'll need to get started.
Now, what are you doing?
- I'm mostly coding for data analysis
- I'm mostly coding for numerical modeling
Code review and analysis¶
Review¶
Code Review for Teams Too Busy to Review Code (youtube video)
Nice tutorial on code review with Rhodecode
Analysis¶
Performance¶
Videos
Optimizations
Setting up a clean Python environment¶
C++ development¶
- The C++ language
- Design Pattern in general and in C++
- C++ Gurus
- patterns in C++
Courses¶
These are useful links to check out regularly
PRACE training: https://events.prace-ri.eu/category/2/
Catalogue of courses: http://formation-calcul.fr/
Formation IDRIS: https://cours.idris.fr/php-plan/affiche_planning.php?total
Code Design and Architecture¶
Writing code¶
Documentation¶
- [[hyb-par: Documentationtools| Documentation Tools]]
Dev Dej games¶
1 - show me your snippet
2 - explique à ton voisin
3 - montre nous ton blog préféré
4 - pull me quizz
5 - sell me (some features of ) your editor
6 - critic my code
7 - show me a youtube video
8 - optimize my snippet
9 - translate my code
10 - Ze lib of Ze Week
Updated by Nicolas Aunai almost 8 years ago · 19 revisions