Create one_dimensional.py

cellular_automata/one_dimensional.py

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+"""
+Return an image of 16 generations of one-dimensional cellular automata based on a given
+ruleset number
+https://mathworld.wolfram.com/ElementaryCellularAutomaton.html
+"""
+
+from typing import List
+
+from PIL import Image
+
+# 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
+        left_neighbor = 0 if i == 0 else cells[time][i - 1]  # special: leftmost cell
+        right_neighbor = 0 if i == population - 1 else cells[time][i + 1]  # rightmost 
+        # 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()