readme.txt

A fairly simple implementation of Conway's Game of Life. I hadn't seen one done in
Python before and after playing around with the CAGE library decided to see what I
could do in a day or two of coding. The universe (grid) size is determined by the
window size and the current grid scale. Display slows down as the population size
increases, but the size of the grid doesn't impact display speed. You'll probably have
to drop the scale down to 1 and use a maximized window in order to display some of the
larger Life patterns and even on a fast box and video card you're unlikely to see much
more than a generation/frame a second with really large patterns like Breeder.

The algorithm I use to calculate the state of the grid at each generation does not
wrap. I have not implemented scrolling yet.

You can draw in the grid to create your own patterns. I also added clipboard support
so that you can paste patterns such as those found in the lifep glossary.doc or
anywhere else where you find patterns made up of lines of . and *.

On 2002-12-18 I added a Lexicon window which will be displayed if you copy Stephen
Silver's lexicon.txt file into the life samples directory before starting up the life
sample. There are over 780 definitions and patterns in the lexicon, so I highly
recommend you download it. The INTRODUCTION and BIBLIOGRAPHY are listed with the rest
of the definitions and patterns. The Lexicon home page is:

  http://www.argentum.freeserve.co.uk/lex_home.htm

And you can download the zip file that contains lexicon.txt directly at:

  http://www.argentum.freeserve.co.uk/lex_asc.zip


For information on Conway's Game of Life and its rules, see Math.com's Life Page.

  http://www.math.com/students/wonders/life/life.html


The original article from the October 1970 issue of Scientific American which
introduced the game to the public:

  http://hensel.lifepatterns.net/october1970.html


The fastest and most complete Life program I know of is Life32 by Johan Bontes.

  http://psoup.math.wisc.edu/Life32.html

Life32 like a few other Life programs works on a very large universe size (grid) of 1
million x 1 million, yet is extremely fast because of the clever way the cells in the
grid are stored and calculated each generation. I don't know if it uses the same
algorithm as Alan Hensel's Java applet, but that applet is also very fast.

  http://hensel.lifepatterns.net/

The algorithm, source, etc. is available at:

  http://hensel.lifepatterns.net/lifeapplet.html

I expect that if something similar was done in Python then my program would be fast
too. I'll leave it as an exercise for the reader  Another algorithm by Paul B.
Callahan is slower, but is probably easier to implement, and certainly much faster
than the brute force approach that I've used.

  http://www.radicaleye.com/lifepage/patterns/javalife.html


Alan Hensel has a great collection of Life patterns at:

  http://www.ibiblio.org/lifepatterns/lifep.zip

Paul B. Callahan provides an excellent illustrated glossary:

  http://www.radicaleye.com/lifepage/picgloss/picgloss.html

as well as a illustrated catalog:

  http://www.radicaleye.com/lifepage/patterns/contents.html


Additional patterns can be found at the pages above as well as links off of those
pages. Put the .lif files in the patterns directory and you can easily browse the
descriptions and display the patterns from the files. I've only included one file with
the sample.


More on Cellular Automata
life.py is the first of a series of Cellular Automatan (CA) simulations I will
probably make available as PythonCard samples. Erik Max Francis already has a set of
modules for exploring a variety of CA sets, but the UI is mostly limited to the
command-line and curses (Unix only). In addition, CAGE is not designed for speed, so
even a simplistic brute force algorithm like the one I coded up for life.py is faster
than doing a GUI display using CAGE. Still, CAGE is a nice set of modules to take a
look at.

  http://www.alcyone.com/pyos/cage/

There are a lot of places to find information and programs dealing with CA. The page
below is a good a place to start.

  http://dmoz.org/Computers/Artificial_Life/Cellular_Automata/