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
orconda
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 toxarray
, 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.