Index

Sommaire:

• 1. Programming for IT specialists only?
  • 1.1. A need for the digital scientist but also the experimenter
• 2. Why is python among so many different solutions?
• 3. Prerequisites, objectives and evaluation
• 4. The Python environment
  • 4.1. Installation
  • 4.2. Using python as a calculator
  • 4.3. A more user-friendly environment
  • 4.4. For the curious … Jupyter and notebooks
• 5. Variables and control structures
  • 5.1. Define and manipulate variables
  • 5.2. The basic types (so not the only ones possible)!
  • 5.3. Control structures
• 6. Containers and loops
  • 6.1. Containers — because a variable is good but a large package is better
  • 6.2. Loops — or how to take advantage of computer stupidity
• 7. Functions and modules
  • 7.1. Let’s organize all of this!
  • 7.2. Python modules
• 8. Classes, object and OOP paradigm
  • 8.1. Python an object language
  • 8.2. Define your objects in python
  • 8.3. Integrate your objects into the Python ecosystem
  • 8.4. Heritage
  • 8.5. Quelques concepts avancés
• 9. Introduction to Numpy
  • 9.1. Numpy
• 10. Course exercises
  • 10.1. Slicing exercise
  • 10.2. Exercises on strings
  • 10.3. Exercise on the lists
  • 10.4. Exercise on dictionaries
  • 10.5. Exercises on functions
  • 10.6. Exercises on the classes
  • 10.7. Numpy exercise
  • 10.8. Scipy exercises
• 11. Sujets de projet pour l’évaluation
  • 11.1. Modalités d’évaluation
  • 11.2. Analyse d’un essai de traction
  • 11.3. Propagation de fissure
  • 11.4. Réaction-diffusion : modèle de Gray-Scott
  • 11.5. Plus court chemin
  • 11.6. Le jeu de la vie
  • 11.7. Mix énergétique optimale
  • 11.8. Réseau électrique Européen
• 12. Image segmentation project
• 13. Smart bibliography project