POSITRON: PythOn for Science In The Reblochon cOuNtry

Positron is a repository made collectively by several teachers and researchers from Annecy in France. It contains resources and exercises dealing with several topics related to numerical methods. They use the Python language. Most of the pages are in Jupyter format. They are therefore downloadable, modifiable and interactive.

The resources available are intended to give practical skills to students. They are not designed as exhaustive sources but more as tools to provide beginners with the basics.

It is divided by themes as follows:

• POSITRON: PythOn for Science In The Reblochon cOuNtry
• Setup your laptop
  • Installing Python
• Basics
  • Python basics
    • Scientific Python
    • Advanced plotting
  • Examples
    • Game of Life and other cellular automatons
  • Exercises
    • Conway’s Game of Life
• Image
  • Support material
    • Basics
    • Orientation an aspect ratio
  • Exercices
    • Tutorial: Counting objects using labeling
    • Practical Work: Steel microstructure
    • Practical Work Bonus: Play with dice
• Data
  • Support material
    • Introduction
    • Linear regression - Linear model
    • Linear regression - Advanced model
    • Derivation of numerical data
  • Ressources
    • Word Generator
  • Exercices
    • Analysis of data from a weather station
    • Tensile test analysis
• ODE
  • Support material
  • Ressources
    • Modelling the trajectory of a badminton shuttlecock
    • Simulation of a set of bodies subjected to gravity
    • Molecular Dynamics : simulation of a crystal formation in 2D
  • Exercices
    • Simple pendulum
    • Driven Harmonic Oscillator
    • Vibrational energy harvesting using a Duffing oscillator
• Optimization
  • Support material
  • Ressources
    • Interactive optimization
    • Illustration : Crossing a river
    • Step by step Optimization : understanding gradient based method
  • Exercices
    • Rosenbrock function and Curve fiting
    • Optimizing a bridge structure
• Machine Learning
  • Support material
  • Exercices
    • Supervised learning using PyTorch: a toy example
    • Reinforcement learning on snake with a genetic neural network

The contributors to this project are:

• Ludovic Charleux

• Emile Roux

• Christian Elmo

Legacy contributors:

• Thibault Goyallon

• Giovanni Feverati

• Pierre Nagorny

• Camille Saint-Martin