Computational Physics with

Book: Computational Physics

The materials on this page are taken from the book Computational Physics by M. Newman, an introduction to the field of computational physics using the Python programming language. If you're interested you can find information about the book here. The book itself is available from the usual booksellers or online here.

The Python programming language is an excellent choice for learning, teaching, or doing computational physics. It is a well-designed, modern programming language that is simultaneously easy to learn and very powerful. It includes a range of features tailored for scientific computing, including features for handling vectors, inverting and diagonalizing matrices, performing Fourier transforms, making graphs, and creating 3D graphics.

This page contains a selection of resources I've developed for teachers and students interested in computational physics and Python.

Chapters for download

Here are several complete book chapters on Python computational physics. You're welcome to download these chapters, print them out, use them in class, or just read them for yourself. Comments or questions are welcome.

Subsequent chapters cover a range of further topics in computational physics, including the solution of linear and nonlinear systems of equations, the solution of ordinary and partial differential equations, Fourier transforms, stochastic processes, and Monte Carlo methods. For a full table of contents, see here.


Here are three useful appendices that go with the chapters above:

Example programs

The following files contain copies of the example programs from the chapters above. All programs are in Python version 3, but they will work fine in version 2 also (technically version 2.6 or later) if you add this line to the beginning of the program:

from __future__ import print_function,division
The file names below correspond to the names printed in the margins of the chapter pages next to each program.

Chapter 2:

Chapter 3:

Chapter 4:

Chapter 5:

Data sets

Here are some data sets that accompany the examples and exercises in the chapters above:

Miscellaneous useful code

Here are a few other pieces of Python code that are useful for some of the exercises.

Last modified: July 15, 2013

Special thanks to Gus Evrard, Brad Orr, Len Sander, and Bruce Sherwood for Python info and comments.

Sources for data sets:

Mark Newman