Getting started

If this is your first experience with Python, I suggest getting started using the Colaboratory Notebook provided by Google. It is a cloud-based wrapper for the iPython / Jupyter Notebook discussed below, and provides a streamlined way to access all the tools below and save your work directly in Google Drive.

Python is a popular and full-featured interpreted programming language -- a huge amount of information about the language and related resources is available here:

https://wiki.python.org/moin/BeginnersGuide

Python for Matlab users

Programming with Python

The w3schools website has an excellent introduction to the Python programming language:

https://www.w3schools.com/python/python_intro.asp

Scientific computing with Python

The syntax and functionality of Python, when augmented with supporting modules like NumPy, SciPy, and Matplotlib, is similar to Matlab. If you are familiar with Matlab and wish to become more familiar with the Python suite of tools, this page is very helpful:

http://mathesaurus.sourceforge.net/matlab-numpy.html

Note that both Python and Matlab's core linear algebra functionality is provided by a wrapper to the LAPACK FORTRAN library (i.e. Matlab is a really expensive wrapper for open-source software developed in the 1990's):

http://www.netlib.org/lapack/

Jupyter Notebook

The Jupyter notebook is a browser-based GUI for interactive computation with the Python suite of tools. You can learn more by reading the documentation:

http://ipython.org/ipython-doc/2/index.html

Here are some introductory links:

http://ipython.org/ipython-doc/2/interactive/tutorial.html#the-four-most-helpful-commands

http://ipython.org/ipython-doc/2/interactive/tutorial.html#tab-completion

http://ipython.org/ipython-doc/2/interactive/reference.html#using-the-python-debugger-pdb

http://ipython.org/ipython-doc/2/interactive/reference.html#plotting-with-matplotlib

http://ipython.org/ipython-doc/2/interactive/reference.html#interactive-demos-with-ipython

And here are some links that may help as you make use of the notebook server:

http://ipython.org/ipython-doc/2/notebook/notebook.html#starting-the-notebook-server

http://ipython.org/ipython-doc/2/notebook/notebook.html#notebook-user-interface

http://ipython.org/ipython-doc/2/notebook/notebook.html#basic-workflow

http://ipython.org/ipython-doc/2/notebook/notebook.html#plotting

http://ipython.org/ipython-doc/2/notebook/notebook.html#configuring-the-ipython-notebook

Dive into Python

To obtain a more systematic introduction to the Python programming language, I recommend the excellent "Dive into Python" reference:

http://www.diveintopython.net/toc/index.html

Numpy & Scipy

For a systematic introduction to Pythong's numerical and scientific computing libraries (NumPy and SciPy), I recommend the user guide and reference manual:

http://docs.scipy.org/doc/numpy/user/

http://docs.scipy.org/doc/numpy/reference/

Python Control (Systems Toolbox) and SLYCOT

For some control systems-specific calculations (such as solving a Lyapunov equation or computing the feedback matrix for an LQR optimal control problem), we will use the Python Control toolbox developed by Richard Murray and Scott Livingston:

http://python-control.readthedocs.io/en/latest/

Note that this package is a wrapper for the SLYCOT FORTRAN library, in the same way that NumPy (and Matlab's numerical linear algebra functionality) is a wrapper for the LAPACK FORTRAN library:

https://www.openhub.net/p/slycot

SAGE

Note that UW Math Professor William Stein maintains SAGE, a free and open-source Python package that interfaces with many mathematics packages:

http://www.sagemath.org/

To the best of my knowledge it does not currently support the Python Control package and thus will not suffice for all the work we'll do in this course -- inform me if I am mistaken!