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.
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:
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,divisionThe file names below correspond to the names printed in the margins of the chapter pages next to each program.
evenodd.py– Check two integers to ensure one is even and the other odd
fibonacci.py– Print out the Fibonacci numbers up to 1000
polar.py– Convert from polar to Cartesian coordinates
rydberg.py– Print out the wavelengths of hydrogen lines
circular.py– Make a density plot from the data in a file
hrdiagram.py– Calculate and display a Hertzsprung–Russell diagram for a catalog of nearby stars
lattice.py– Create a 3D visualization of a simple cubic lattice
revolve.py– Create an animation of a moving sphere
ripples.py– Calculate and display the interference pattern generated by two circular sets of waves
qsho.py– Calculate the internal energy of a quantum simple harmonic oscillator at temperature T
gaussint.py– Evaluate an integral using Gaussian quadrature
intinf.py– Evaluate an integral over an infinite domain
trapezoidal.py– Evaluate an integral using the trapezoidal rule
Here are some data sets that accompany the examples and exercises in the chapters above:
altitude.txt– Altitude above sea level, or depth below it, of points on the Earth's surface, measured on a grid
circular.txt– Data file for the density plots in Figure 2.3
stars.txt– Catalog of temperatures and magnitudes for 7860 nearby stars
stm.txt– STM measurements of the (111) surface of silicon
sunspots.txt– Data on sunspots since 1749
velocities.txt– Velocity of a moving particle as a function of time
Here are a few other pieces of Python code that are useful for some of the exercises.
banded.py– Solve a tridiagonal or banded system of linear equations using Gaussian elimination
colormaps.py– Definitions of some useful colormaps for density plots
dcst.py– Perform forward and inverse fast cosine and sine transforms
gaussxw.py– Calculate positions and weights of integration points for Gaussian quadrature
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:
Special thanks to Gus Evrard, Brad Orr, Len Sander, and Bruce Sherwood for Python info and comments.
Sources for data sets: