Source code

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.

Requirements

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

What is it?

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.