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2c6f553ccb
* [mypy] Fix type annotations for cellullar_automata * mypy --ignore-missing-imports * mypy --ignore-missing-imports * Blank lines * Blank lines Co-authored-by: Christian Clauss <cclauss@me.com>
75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
"""
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Return an image of 16 generations of one-dimensional cellular automata based on a given
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ruleset number
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https://mathworld.wolfram.com/ElementaryCellularAutomaton.html
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"""
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from __future__ import annotations
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from PIL import Image
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# Define the first generation of cells
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# fmt: off
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CELLS = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
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# fmt: on
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def format_ruleset(ruleset: int) -> list[int]:
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"""
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>>> format_ruleset(11100)
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[0, 0, 0, 1, 1, 1, 0, 0]
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>>> format_ruleset(0)
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[0, 0, 0, 0, 0, 0, 0, 0]
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>>> format_ruleset(11111111)
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[1, 1, 1, 1, 1, 1, 1, 1]
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"""
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return [int(c) for c in f"{ruleset:08}"[:8]]
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def new_generation(cells: list[list[int]], rule: list[int], time: int) -> list[int]:
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population = len(cells[0]) # 31
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next_generation = []
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for i in range(population):
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# Get the neighbors of each cell
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# Handle neighbours outside bounds by using 0 as their value
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left_neighbor = 0 if i == 0 else cells[time][i - 1]
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right_neighbor = 0 if i == population - 1 else cells[time][i + 1]
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# Define a new cell and add it to the new generation
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situation = 7 - int(f"{left_neighbor}{cells[time][i]}{right_neighbor}", 2)
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next_generation.append(rule[situation])
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return next_generation
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def generate_image(cells: list[list[int]]) -> Image.Image:
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"""
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Convert the cells into a greyscale PIL.Image.Image and return it to the caller.
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>>> from random import random
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>>> cells = [[random() for w in range(31)] for h in range(16)]
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>>> img = generate_image(cells)
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>>> isinstance(img, Image.Image)
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True
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>>> img.width, img.height
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(31, 16)
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"""
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# Create the output image
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img = Image.new("RGB", (len(cells[0]), len(cells)))
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pixels = img.load()
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# Generates image
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for w in range(img.width):
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for h in range(img.height):
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color = 255 - int(255 * cells[h][w])
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pixels[w, h] = (color, color, color)
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return img
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if __name__ == "__main__":
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rule_num = bin(int(input("Rule:\n").strip()))[2:]
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rule = format_ruleset(int(rule_num))
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for time in range(16):
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CELLS.append(new_generation(CELLS, rule, time))
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img = generate_image(CELLS)
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# Uncomment to save the image
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# img.save(f"rule_{rule_num}.png")
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img.show()
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