course-E375004.md

Python for Scientific Computations and Control

Codenames: E375004, 2375004

Classification

Note

In case of getting error while accessing jupyter notebooks from our repository, use https://nbviewer.org and provide the link to the notebook you need to open. This is unfortunately not an error on our side and we cannot do anything about that.

Sylabus

1. Python Basics 1 - installation, IDEs, data types, for, while, if, functions (Week 26 February - 1 March)

   • Notebook a) intro
   • Notebook b) Program flow
   • task: Caesar cipher encryption
   • responsible person: [email protected]

2. Python Basics 2 - functions, classes, files and venv (Week 4 March - 8 March)

   • study any introduction to Python classes, for example: W3Schools (easy to understand) or Python docs (more detailed)
   • Notebook a) Advanced functions
   • Notebook b) Classes and objects
   • Notebook c) File manipulation
   • Notebook d) Virtual environment
   • task: Rock, paper, scissors
   • responsible person: [email protected]

3. Math and visualization - work with packages: numpy, scipy, matplotlib - linear algebra, calculus, graphs (Week 11 March - 15 March)

   • Notebook: numpy, scipy, matplotlib
   • task: image convolution
   • responsible person: [email protected]

4. Sympy - Equations of Motion (Week 18 March - 22 March)

   • Introduction
   • task: Derive an equation / set of equations that require derivatives to be taken
   • responsible person: [email protected]

5. Convex optimization (Week 25 March - 29 March)

   • Introduction
   • task: Solve an optimization problem of your chooosing
   • responsible person: [email protected]

6. Model Predictive Control (Weeks 25 March - 12 April)

   • Introduction
   • task: None
   • responsible person: [email protected]

7. Simulation of mechanical systems using Mujoco (Week 8 April - 12 April)

   • Controlled Cart-Pole
     • LQR
     • MPC
   • task: None
   • responsible person: [email protected]

8. Data processing and visualization using pandas (Week 15 April - 19 April)

   • Notebook a) tabular data, pandas
   • Notebook b) COVID data example
   • Interactive graphs using plotly: plotly_example
   • task: covid data
   • responsible person: [email protected]

9. Asyncio (Week 22 April - 26 April)

   • Notebook: asyncio
   • task: CTUSS Commander
   • responsible person: [email protected]

10. Artificial Intelligence I. (Week 29 April - 3 May)

    • Source codes: start-here
    • responsible person: [email protected]

11. Artificial Intelligence II. (Week 6 May - 10 May)

    • Source codes: start-here
    • responsible person: [email protected]
    • task: Activation Functions

12. Web app using Streamlit (Week 13 May - 17 May)

    • Source codes: streamlit folder:
    • responsible person: [email protected]

13. Requests, database, API (Week 20 May - 24 May)

    • Using HTTP protocol: Requests
    • Building own API: FastAPI
    • Exploring database: sqlite
    • Example application: user_register
    • Complete lecture code in one file: main.py
      • run with fastapi dev main.py
    • responsible person: [email protected]