Lecture notes »Tools for Scientific Computing«

• 1. Introduction
• 2. Version Control with Git
  • 2.1. Why version control?
  • 2.2. Centralized and distributed version control systems
  • 2.3. Getting help
  • 2.4. Setting up a local repository
  • 2.5. Basic workflow
  • 2.6. Working with branches
  • 2.7. Collaborative code development with GitLab
  • 2.8. Sundry topics
    • 2.8.1. Stashing
    • 2.8.2. Tagging
    • 2.8.3. Detached head state
    • 2.8.4. Manipulating history
• 3. Testing of code
  • 3.1. Why testing?
  • 3.2. Doctests
  • 3.3. Testing with pytest
• 4. Scientific computing with NumPy and SciPy
  • 4.1. Python scientific ecosystem
  • 4.2. NumPy
    • 4.2.1. Python lists and matrices
    • 4.2.2. NumPy arrays
    • 4.2.3. Creating arrays
    • 4.2.4. Indexing arrays
    • 4.2.5. Broadcasting
    • 4.2.6. Universal functions
    • 4.2.7. Linear algebra
  • 4.3. SciPy
• 5. Run-time analysis
  • 5.1. General remarks
  • 5.2. Some pitfalls in run-time analysis
  • 5.3. The timeit module
  • 5.4. The cProfile module
  • 5.5. Line oriented run-time analysis
• 6. Documentation of code
  • 6.1. Markup with reStructuredText
  • 6.2. Sphinx documentation generator
    • 6.2.1. Setting up a Sphinx project
    • 6.2.2. Sphinx configuration
    • 6.2.3. Autogeneration of a documentation
• 7. Aspects of parallel computing
  • 7.1. Threads, processes and the GIL
  • 7.2. Parallel computing in Python
  • 7.3. Numba
• 8. Appendix
  • 8.1. Decorators