wfc_2019f

This is my research implementation of WaveFunctionCollapse in Python. It has two goals:

For more general-purpose WFC information, the original reference repository remains the best resource: https://github.com/mxgmn/WaveFunctionCollapse

Running WFC

If you want direct control over running WFC, call wfc_control.execute_wfc().

The arguments it accepts are:

filename: path to the input image file
tile_size=1: size of the tiles it uses (1 is fine for pixel images, larger is for things like a Super Metroid map)
pattern_width=2: size of the patterns; usually 2 or 3 because bigger gets slower and
rotations=8: how many reflections and/or rotations to use with the patterns
output_size=[48,48]: how big the output image is
ground=None: which patterns should be placed along the bottom-most line
attempt_limit=10: stop after this many tries
output_periodic=True: the output wraps at the edges
input_periodic=True: the input wraps at the edges
loc_heuristic="entropy": what location heuristic to use; entropy is the original WFC behavior. The heuristics that are implemented are lexical, hilbert, spiral, entropy, anti-entropy, simple, random, but when in doubt stick with entropy.
choice_heuristic="lexical": what choice heuristic to use; weighted is the original WFC behavior.
visualize=True: write intermediate images to disk?
global_constraint=False: what global constraint to use. Currently the only one implemented is allpatterns
backtracking=False: do we use backtracking if we run into a contradiction?
log_filename="log": what should the log file be named?
logging=True: should we write to a log file?
log_stats_to_output=None

pytest

python setup.py build_sphinx

With linux the documentation can be displayed with:

xdg-open build/sphinx/index.html