ComputationalPhysics2016

My personal results derived from the 2016 tasks in computational physics

Computational Physics Task 1.1

Standard map of the 'Kicked Rotor'

Computational Physics Task 2.1

Elementary numerical methods I

Computational Physics Task 3.1

Elementary numerical methods II

Computational Physics Task 4.1

Dynamics of a particle in the driven double trough potential. V(x) = x^4 - x^2 + x[A + Bsin(wt)]

Computational Physics Task 5.1

Quantum mechanics of 1D potentials

Computational Physics Task 6.1

Quantum mechanics of 1D potentials II - time evolution

Computational Physics Task 7.1

Quantum mechanics of 1D potentials III - periodic potentials and tight-binding approximation

Computational Physics Task 8.1

Calculation of average pressure in time interval dt, which is exerted on side surface A of a cuboid with N particles inside

Computational Physics Task 9.1

Numerical simulation of particle locations in diffusion + drift and absorption

Computational Physics Task 10.1

2D Ising model, where in the left plot the spin state of a 50x50 grid with periodic boundary conditions is shown.

Install:

Option 0: GitHub Codespaces (Easiest)

The easiest way to get started is using GitHub Codespaces, which provides a pre-configured cloud development environment:

1. Click the green "Code" button on the GitHub repository page
2. Select "Codespaces" tab
3. Click "Create codespace on main" (or existing codespace)
4. Wait for the environment to set up automatically (~2-3 minutes)

All Python dependencies will be installed automatically, and VS Code will be configured with helpful extensions for Python development.

Option 1: Conda (Recommended for Local Setup)

With Conda on the system installed the dependencies for this project can be automatically installed in a new environment:

Go to the projects base directory.

Open a conda-command-prompt with admin privileges and run the commands from the project folder

• to create a new environment with basic dependencies:

conda env create -f .\environment.yml

• to activate the environment:

conda activate compphys2016

Option 2: pip (Local Setup)

Alternatively, you can install dependencies using pip:

pip install -r requirements.txt

For exact versions (recommended for reproducible results):

pip install -r stable_requirements.txt

Note: This project has been updated to use newer versions of scientific Python packages (NumPy 2.2+, Matplotlib 3.10+, SciPy 1.15+, SymPy 1.14+) for better performance and bug fixes.

Validation

After installing dependencies, you can validate that everything works correctly by running:

python validate_dependencies.py

How to use

With everything installed it comes down to running with the respective shell active just

python .\1_1_martin_roebke.py

Testing

To verify that all dependencies are working correctly with the computational physics scripts, run the comprehensive integration test suite:

python run_all_tests.py

This will:

• Test all 11 computational physics scripts
• Verify dependency compatibility
• Check for proper matplotlib backend handling
• Validate memory usage and safety
• Generate a detailed test report (TEST_REPORT.md)

Individual test modules can also be run separately:

python test_integration.py          # Core integration tests
python test_script_execution.py     # Script execution tests

Report problems

Check if the above steps are properly done.

If yes and there is something to be done - log into github and look at Issues (or open new) - or contact the maintainer ;-)