Nils Aall Barricelli cellular automata provisional code

barricelli.py

import numpy as np
from numba import njit

@njit
def evolve(initial_row, time):
    size = initial_row.shape[0]
    world = np.zeros((time, size), dtype = np.int8)
    world[0] = initial_row
    for t in range(1, time):
        world_after_movements = np.zeros(size, dtype = np.int8)
        collisions = -1*np.ones(size, dtype = np.int8)
        #movements and collisions
        for pos, n in enumerate(world[t-1]):
            if n!=0:
                next_position = (pos + n)%size
                world_after_movements[next_position] = n
                collisions[next_position] += 1
        
        world_after_movements[collisions>0] = 0

        #mutation
        for pos, c in enumerate(collisions):
            if (c>0) and (world[t-1,pos]) == 0:
                closest_right = 0
                closest_right_d = 0
                for i in range(pos+1, size):
                    if world[t-1,i]!=0:
                        closest_right = world[t-1,i]
                        closest_right_d = i-pos
                        break
                closest_left = 0
                closest_left_d = 0
                for i in range(pos-1, -1, -1):
                    if world[t-1,i]!=0:
                        closest_left = world[t-1,i]
                        closest_left_d = pos-i
                        break
                sig = np.sign(closest_left*closest_right)
                val = min(closest_left_d,closest_right_d)
                world_after_movements[pos] = sig*val
                    
        #reproduction
        world_after_reproduction = world_after_movements.copy()
        children = np.zeros(size, dtype = np.int8)
        birth = np.zeros(size, dtype = np.int8)
        for pos, n in enumerate(world_after_movements):
            if (n!=0) and (world[t-1,pos] != 0):
                next_position = (pos - n + world[t-1,pos])%size
                birth[next_position] += 1
                children[next_position] = n
        valid_birth = (birth == 1) & (world_after_reproduction == 0)
        world_after_reproduction[valid_birth] = children[valid_birth]
        world[t] = world_after_reproduction.copy()
    return world

size=1000
time=1000
max_number = 5


ir = np.random.choice(np.arange(-max_number,max_number+1), size)
world = evolve(ir, time)