This is source code that is either used in the presentation, or was developed to create it. There is some material not covered in the presentation as well.

• Python version: at least 3.6
• Packages (names listed taht can be used with pip or conda to install):
  • numpy
  • scipy
  • matplotlib
  • bokeh
  • sympy
  • pytables
  • scikit-image
  • jupyter
  • ipywidgets
• Optional packages
  • opencv
  • numexpr
  • pandas
  • vpython

• birdsong: illustration of signal processing with scipy, reading and writing of a WAV file, computing the amplitude spectrum using FFT, applying a high-pass filter.
• boekh: illustrations of how to create plots using bokeh, including interactive plots.
• hdf5: illustrations of how to create and access HDF5 files.
• image-processing: illustrations of image processing using scikit-image and video processing using OpenCV.
• matplotlib: illustrations of how to create plots using matplotlib.
• matrices: some numpy illustrations.
• numpy: some numpy and scipy illustrations.
• sympy: some illustrations of doing symbolic computations using sympy.
• xarray: introduction to xarray, a library for indexed, labeled N-dimensional arrays.
• netcdf: sample code for reading and writing NetCDF data.
• [vpython]: illustrations of using VPython for physics and mathematics animations.
• manim: sample code for illustrating manim, a framework to create mathematical animations.
• cannon.ipynb: Jupyter notebook illustrating the use of numpy, scipy, sympy and bokeh.
• cellular_automata.ipynb: Jupyter notebook illustrating the use of matplotlib.
• diffusion_limited_aggregation.ipynb: Jupyter notebook illustrating the use of numpy and matplotlib.
• ising_model.ipynb: Jupyter notebook illustrating the use of numpy and dependency injection in simulation design.
• lennard_jones.ipynb: Jupyter notebook illustrating the use of numpy, scipy, sympy and matplotlib.
• prison_guard.ipynb: Jupyter notebook illustrating the use of numpy and matplotlib.