51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
#!/usr/bin/env python3
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import random
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from multiprocessing import freeze_support
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from cellular_automaton import *
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class TestRule(Rule):
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@staticmethod
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def evolve_cell(last_cell_state, last_neighbour_states):
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try:
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return last_neighbour_states[0]
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except IndexError:
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print("damn neighbours")
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pass
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return False
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class MyState(CellState):
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def __init__(self):
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rand = random.randrange(0, 101, 1)
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init = 0
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if rand > 99:
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init = 1
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super().__init__((float(init),), draw_first_state=False)
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def get_state_draw_color(self, iteration):
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red = 0
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if self.get_state_of_last_iteration(iteration)[0]:
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red = 255
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return [red, 0, 0]
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def make_cellular_automaton(dimension, neighborhood, rule, state_class):
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cells = CAFactory.make_cellular_automaton(dimension=dimension, neighborhood=neighborhood, state_class=state_class)
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return CellularAutomaton(cells, dimension, rule)
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if __name__ == "__main__":
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freeze_support()
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random.seed(1000)
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# best single is 400/400 with 0,2 ca speed and 0,09 redraw / multi is 300/300 with 0.083
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neighborhood = MooreNeighborhood(EdgeRule.FIRST_AND_LAST_CELL_OF_DIMENSION_ARE_NEIGHBORS)
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ca = make_cellular_automaton(dimension=[400, 400], neighborhood=neighborhood, rule=TestRule(), state_class=MyState)
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ca_processor = CellularAutomatonProcessor(process_count=1, cellular_automaton=ca)
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ca_window = PyGameFor2D(window_size=[1000, 800], cellular_automaton=ca)
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ca_window.main_loop(cellular_automaton_processor=ca_processor, ca_iterations_per_draw=1)
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