My teaching codes, developed in steps

I first write a small part, test it, then add the next part. Each notebook explains the calculation and includes stage checks. These are newly organized teaching versions; the original complete programs and historical sources remain separate.

Python

python -m pip install numpy matplotlib jupyter

Extract this archive without changing its folder structure. Open a terminal in examples/stepwise, then use jupyter notebook. Select a Python notebook and run its cells in order. The phase-field lesson uses ../phase_field.py for its existing elastic kernel.

Julia

The algorithms use Julia Base and standard libraries. Fourier examples also require FFTW:

julia -e 'using Pkg; Pkg.add("FFTW")'

For notebooks install IJulia:

julia -e 'using Pkg; Pkg.add("IJulia")'

Select the Julia kernel in Jupyter. Complete scripts are included; for example julia diffusion.jl. Run scripts from examples/stepwise. The phase-field lesson uses ../phase_field.jl. Python and Julia random generators differ, so individual random paths need not match.

C

See ../hoshen-kopelman/README.md. Six C checkpoints each compile and run independently. These are teaching checkpoints; my original research C files are not included.

Checks

See check-results.txt. Python notebooks have executed outputs. Julia scripts were executed; IJulia notebook-kernel execution was not tested. Short teaching workloads do not establish numerical convergence of a full simulation. The 256³ spinodal model remains available separately.

The website lessons define the quantities and conventions used by each model. I permit free noncommercial teaching, learning and demonstrations with acknowledgment. Research (including academic research) and commercial use require my explicit written permission. Earlier MIT and GPL releases retain their existing permissions. See CODE-USE-TERMS.txt. Original teaching prose follows CC BY 4.0. See https://saswataiith.github.io/using-my-resources/.

Ternary spinodal decomposition

ternary-spinodal-python.ipynb develops my polynomial ternary model in nine steps. ternary_spinodal.py contains the same runnable cells. I use a 256 × 256 periodic grid, a constant positive-definite mobility matrix and a coupled Fourier update. The run saves ternary-fields.npz for the correlation lesson. All quantities are dimensionless. See the notebook for corrections to the original working version and the limits of the completed checks.