I will be using two main pieces of software to demonstrate the principles in the class. The first is SymPy which a computer algebra system that includes a package for deriving analytical equations of motion. The second main software is PyDy which is a simulation and visualization tool for models created with SymPy. Both of these are open source software packages and are part of the Scientific Python ecosystem of software packages.
Running The Software
Log into http://jupyter.libretexts.org with your UCD email address to access our local JupyterHub server. You can then either create a new terminal session or a "Python 3" Jupyter notebook.
This webcast is a nice way to get familiar with the Jupyter Lab interface.
Backing Up Your Work
The JupyterHub server has an automated backup in place should any problems occur, but it is recommended to regularly back up your own work. Make sure to download any important files from the service reguarly.
Installing the Software On Your Personal Computer
We recommend that you install the Anaconda distribution of Python which includes most all of the packages you will need.
You can open up either Jupyter notebooks or use the Spyder IDE (which also can open notebooks).
More instructions for getting PyDy and other more specialized packages installed will be added here at a later date.
Learning Python For Engineering Computation
Start with the SymPy tutorial to get familiar with symbolic manipulation in Python:
The SymPy Physics Vector and Mechanics documentation provides explanations for the advanced features for rigid body mechanics;
There is also a PyDy tutorial which starts with SymPy and ends with simulation with PyDy:
To learn the core Python language (not scientific oriented computing) there are many many resources. My recommendations for beginners are:
Python becomes most powerful for engineers by using the various packages in the Scientific Python Ecosystem. Here are my recommend resources for learning these topics:
- The SciPy Lecture Notes is a wholistic resource for all things numerical computing in Python: http://www.scipy-lectures.org/
- The book "Effective Computation in Physics" by Anthony Scopatz & Kathryn Huff is a guide that starts at ground zero for Python and leads you through the tools and methods to be a computational engineer. http://physics.codes/
- If you know some Matlab this guide is very helpful for looking up equivalent commands in NumPy: NumPy for Matlab Users.
- Getting good at asking Google programming questions will almost always lead you to https://stackoverflow.com/ which is a key resources for all kind of programming questions.
Each software package also has documentation: