Python/cellular_automata/one_dimensional.py

75 lines
2.3 KiB
Python

"""
Return an image of 16 generations of one-dimensional cellular automata based on a given
ruleset number
https://mathworld.wolfram.com/ElementaryCellularAutomaton.html
"""
from __future__ import annotations
from PIL import Image # type: ignore
# Define the first generation of cells
# fmt: off
CELLS = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
# fmt: on
def format_ruleset(ruleset: int) -> list[int]:
"""
>>> format_ruleset(11100)
[0, 0, 0, 1, 1, 1, 0, 0]
>>> format_ruleset(0)
[0, 0, 0, 0, 0, 0, 0, 0]
>>> format_ruleset(11111111)
[1, 1, 1, 1, 1, 1, 1, 1]
"""
return [int(c) for c in f"{ruleset:08}"[:8]]
def new_generation(cells: list[list[int]], rule: list[int], time: int) -> list[int]:
population = len(cells[0]) # 31
next_generation = []
for i in range(population):
# Get the neighbors of each cell
# Handle neighbours outside bounds by using 0 as their value
left_neighbor = 0 if i == 0 else cells[time][i - 1]
right_neighbor = 0 if i == population - 1 else cells[time][i + 1]
# Define a new cell and add it to the new generation
situation = 7 - int(f"{left_neighbor}{cells[time][i]}{right_neighbor}", 2)
next_generation.append(rule[situation])
return next_generation
def generate_image(cells: list[list[int]]) -> Image.Image:
"""
Convert the cells into a greyscale PIL.Image.Image and return it to the caller.
>>> from random import random
>>> cells = [[random() for w in range(31)] for h in range(16)]
>>> img = generate_image(cells)
>>> isinstance(img, Image.Image)
True
>>> img.width, img.height
(31, 16)
"""
# Create the output image
img = Image.new("RGB", (len(cells[0]), len(cells)))
pixels = img.load()
# Generates image
for w in range(img.width):
for h in range(img.height):
color = 255 - int(255 * cells[h][w])
pixels[w, h] = (color, color, color)
return img
if __name__ == "__main__":
rule_num = bin(int(input("Rule:\n").strip()))[2:]
rule = format_ruleset(int(rule_num))
for time in range(16):
CELLS.append(new_generation(CELLS, rule, time))
img = generate_image(CELLS)
# Uncomment to save the image
# img.save(f"rule_{rule_num}.png")
img.show()