Uses Python 3.
You can see the output of this project here.
main.py is the main program. It generates html pages and writes them to html. Its intermediate products are stored in cache.
graph_similarity.py uses graphviz's fdp to create a visual representation of the similarity metric. Depends on the output of main.py.
puretests.py contains some tests of the pure-type randomizers.
run-tests is a script which runs all the unit tests.
sample.py generates sequences of arbitrary length from a given randomizer.
testgame.py is a TAP-like game for testing the randomizers. It uses pygame.
randos holds the randomizers. Each file can hold multiple randomizers, but they are grouped by type.
analysis.py evaluates the randomizer-generated sequences according to a number of metrics.
compare.py performs a multidimensional distance comparison, in an attempt to quantify the similarity of the randomizers based on the reported metrics.
pure.py contains code related to the pure-type randomizers.
load.py contains the magic to load the randomizers. It also wraps ones with different interfaces so they use the standard interface.
The piecedata directory contains rotation system data.
Piece := a one-character string in 'jiltsoz'
randomizer() : <Stream>
Stream.next() : <Piece>
randomizer is a callable which returns a stream. A stream is an object with a member function, next, that returns the next piece in the sequence.
History := [<Piece>]
Weight := <float>
randomizer(<History>) : {<Piece>: <Weight>}
randomizer is a callable which takes a history, which is a list of pieces, and returns a dict mapping pieces to weights.
A given history must always produce the same piece weights. This is what makes these randomizers "pure".
The sum of the weights must be greater than 0 but needn't be 1. Pieces may be left out; missing pieces are assumed to have weight 0.