multi processing fast and running with some tests

performance.txt

@@ -0,0 +1,11 @@
+# With 100x100 10 times
+
+Try One
+TOTAL TIME: 0.1217s
+SIZE: 21.7525MB
+
+TOTAL TIME: 0.1171s   	# Only set on change
+SIZE: 21.7525MB			# process size 51,4 / main(75,9)
+
+TOTAL TIME: 0.1161s
+SIZE: 20.3338MB         # removed grid

scripts/main_ui.py

@@ -39,14 +39,16 @@ if __name__ == "__main__":
     from cellular_automaton.ca_neighborhood import MooreNeighborhood, EdgeRule
     from cellular_automaton.ca_display import PyGameFor2D
     from cellular_automaton.ca_grid import Grid
+
     freeze_support()
     random.seed(1000)
+    dimension = [100, 100]
     rule = TestRule()
-    grid = Grid(dimension=[200, 200],  # best is 400/400 with 0,2 ca speed and 0,09 redraw
+    grid = Grid(dimension=dimension,  # best is 400/400 with 0,2 ca speed and 0,09 redraw
                 neighborhood=MooreNeighborhood(EdgeRule.FIRST_AND_LAST_CELL_OF_DIMENSION_ARE_NEIGHBORS),
                 state_class=MyState)
-    ca = CellularAutomaton(grid, rule)
+    ca = CellularAutomaton(tuple(grid.get_cells().values()), dimension, rule)
     ca_window = PyGameFor2D(window_size=[1000, 800], cellular_automaton=ca)
-    ca_processor = CellularAutomatonProcessor(process_count=2, cellular_automaton=ca)
+    ca_processor = CellularAutomatonProcessor(process_count=4, cellular_automaton=ca)
     ca_window.main_loop(cellular_automaton_processor=ca_processor,
                         ca_iterations_per_draw=5)

src/cellular_automaton/ca_cell.py

@@ -1,8 +1,9 @@
 from cellular_automaton.ca_cell_state import CellState
+from typing import Type
 
 
 class Cell:
-    def __init__(self, state_class: CellState.__class__, coordinate: list):
+    def __init__(self, state_class: Type[CellState], coordinate: list):
         self._coordinate = coordinate
         self._state = state_class()
         self._neighbours = []
@@ -16,29 +17,20 @@ class Cell:
     def get_coordinate(self):
         return self._coordinate
 
-    def evolve_if_ready(self, rule):
+    def evolve_if_ready(self, rule, iteration):
-        if self._neighbours_are_younger():
+        if self._state.is_active(iteration):
-            if self._state.is_active():
+            new_state = rule(self._state.get_state_of_last_iteration(iteration), self.get_neighbour_states(iteration))
-                new_state = rule(self._state.get_current_state(), self.get_neighbour_states())
+            self.set_new_state_and_activate(new_state, iteration)
-                self.set_new_state_and_activate(new_state)
 
-            self._state.increase_age()
+    def get_neighbour_states(self, index):
+        return [n.get_state_of_last_iteration(index) for n in self._neighbours]
 
-    def _neighbours_are_younger(self):
+    def set_new_state_and_activate(self, new_state: CellState, iteration):
-        for n in self._neighbours:
+        changed = self._state.set_state_of_iteration(new_state, iteration)
-            if n.get_age() < self._state.get_age():
-                return False
-        return True
-
-    def get_neighbour_states(self):
-        return [n.get_state_of_iteration(self._state.get_age()) for n in self._neighbours]
-
-    def set_new_state_and_activate(self, new_state: CellState):
-        changed = self._state.set_current_state(new_state)
         if changed:
-            self._set_active()
+            self._set_active_for_next_iteration(iteration)
 
-    def _set_active(self):
+    def _set_active_for_next_iteration(self, iteration):
-        self._state.set_active_for_next_iteration(self._state.get_age() + 1)
+        self._state.set_active_for_next_iteration(iteration)
         for n in self._neighbours:
-            n.set_active_for_next_iteration(self._state.get_age() + 1)
+            n.set_active_for_next_iteration(iteration)

src/cellular_automaton/ca_cell_state.py

@@ -1,4 +1,5 @@
-from multiprocessing import Array, Value
+from multiprocessing import RawArray, RawValue
+from ctypes import c_float, c_bool
 
 
 class CellState:
@@ -8,45 +9,35 @@ class CellState:
         When using the cellular automaton display, inherit this class and implement get_state_draw_color.
     """
     def __init__(self, initial_state=(0., ), draw_first_state=True):
-        self._state_slots = [Array('d', initial_state) for i in range(self.__class__._state_save_slot_count)]
+        self._state_slots = [RawArray(c_float, initial_state) for i in range(self.__class__._state_save_slot_count)]
-        self._active = Value('i', 1)
+        self._active = [RawValue(c_bool, False) for i in range(self.__class__._state_save_slot_count)]
-        self._age = Value('i', 0)
+        self._active[0].value = True
         if draw_first_state:
-            self._dirty = Value('i', 1)
+            self._dirty = RawValue(c_bool, True)
         else:
-            self._dirty = Value('i', 0)
+            self._dirty = RawValue(c_bool, False)
-
-    def get_age(self):
-        return self._age.value
-
-    def get_current_state(self):
-        return self.get_state_of_iteration(self.get_age())
 
     def get_state_of_iteration(self, iteration):
         """ Will return the state for the iteration modulo number of saved states.
         :param iteration:   Uses the iteration index, to differ between concurrent states.
         :return The state for this iteration.
         """
-        return self._state_slots[iteration % self.__class__._state_save_slot_count]
+        return self._state_slots[self.__calculate_slot(iteration)]
 
-    def is_active(self):
+    def __calculate_slot(self, iteration):
-        return self._active.value > self._age.value
+        return iteration % self.__class__._state_save_slot_count
+
+    def is_active(self, iteration):
+        return self._active[self.__calculate_slot(iteration)]
 
     def set_active_for_next_iteration(self, iteration):
-        self._active.value = max(self._active.value, iteration + 1)
+        self._active[self.__calculate_slot(iteration + 1)].value = True
-
-    def increase_age(self):
-        with self._age.get_lock():
-            self._age.value += 1
 
     def is_set_for_redraw(self):
-        return self._dirty != 1
+        return self._dirty.value
 
     def was_redrawn(self):
-        self._dirty = 0
+        self._dirty.value = False
-
-    def set_current_state(self, new_state):
-        return self.set_state_of_iteration(new_state, self.get_age() + 1)
 
     def get_state_of_last_iteration(self, current_iteration_index):
         return self.get_state_of_iteration(current_iteration_index - 1)
@@ -65,16 +56,13 @@ class CellState:
             try:
                 if current_state[i] != new_state[i]:
                     changed = True
-
                     current_state[i] = new_state[i]
             except IndexError:
                 raise IndexError("New State length or type is invalid!")
 
         self._dirty.value |= changed
+        self._active[self.__calculate_slot(iteration)].value = False
         return changed
 
     def get_state_draw_color(self, iteration):
         raise NotImplementedError
-
-    def __str__(self):
-        return str(self._state_slots)

src/cellular_automaton/ca_display.py

@@ -2,6 +2,11 @@ import pygame
 import time
 import operator
 
+import cProfile
+import pstats
+from pympler import asizeof
+
+
 from cellular_automaton.cellular_automaton import CellularAutomaton, CellularAutomatonProcessor
 
 
@@ -19,16 +24,13 @@ class DisplayFor2D:
         cell_size = self._calculate_cell_display_size(grid_rect[-2:])
         self._display_info = _DisplayInfo(grid_rect[-2:], grid_rect[:2], cell_size, screen)
 
-    def set_cellular_automaton(self, cellular_automaton):
+    def redraw_cellular_automaton(self):
-        self._cellular_automaton = cellular_automaton
+        pygame.display.update(list(_cell_redraw_rectangles(self._cellular_automaton.cells,
-
+                                                           0,
-    def _redraw_cellular_automaton(self):
-        pygame.display.update(list(_cell_redraw_rectangles(self._cellular_automaton.grid.get_cells().values(),
-                                                           self._cellular_automaton.evolution_iteration_index,
                                                            self._display_info)))
 
     def _calculate_cell_display_size(self, grid_size):
-        grid_dimension = self._cellular_automaton.grid.get_dimension()
+        grid_dimension = self._cellular_automaton.dimension
         return list(map(operator.truediv, grid_size, grid_dimension))
 
 
@@ -36,7 +38,7 @@ class PyGameFor2D:
     def __init__(self, window_size: list, cellular_automaton: CellularAutomaton):
         self._window_size = window_size
         self._cellular_automaton = cellular_automaton
-
+        self._cellular_automaton_proocessor = None
         pygame.init()
         pygame.display.set_caption("Cellular Automaton")
         self._screen = pygame.display.set_mode(self._window_size)
@@ -56,20 +58,34 @@ class PyGameFor2D:
 
     def main_loop(self, cellular_automaton_processor: CellularAutomatonProcessor, ca_iterations_per_draw):
         running = True
-
+        cellular_automaton_processor.evolve()
+        first = True
         while running:
+            pygame.event.get()
             time_ca_start = time.time()
+            if first:
+                self._evolve_with_performance(cellular_automaton_processor, time_ca_start)
+                first = False
+            else:
+                cellular_automaton_processor.evolve()
             time_ca_end = time.time()
-            self.ca_display._redraw_cellular_automaton()
+            self.ca_display.redraw_cellular_automaton()
             time_ds_end = time.time()
             self._print_process_duration(time_ca_end, time_ca_start, time_ds_end)
 
-            time.sleep(0.5)
+    def _evolve_with_performance(self, cap, time_ca_start):
-
+        size = asizeof.asizeof(self._cellular_automaton)
-            for event in pygame.event.get():
+        cProfile.runctx("cap.evolve_x_times(10)", None, locals(), "performance_test")
-                if event.type == pygame.QUIT:
+        print("PERFORMANCE")
-                    cellular_automaton_processor.stop()
+        p = pstats.Stats('performance_test2')
-                    running = False
+        p.strip_dirs()
+        # sort by cumulative time in a function
+        p.sort_stats('cumulative').print_stats(10)
+        # sort by time spent in a function
+        p.sort_stats('time').print_stats(10)
+        time_ca_end = time.time()
+        print("TOTAL TIME: " + "{0:.4f}".format(time_ca_end - time_ca_start) + "s")
+        print("SIZE: " + "{0:.4f}".format(size / (1024 * 1024)) + "MB")
 
 
 def _cell_redraw_rectangles(cells, evolution_index, display_info):

src/cellular_automaton/ca_neighborhood.py

@@ -17,9 +17,6 @@ class Neighborhood:
         self.edge_rule = edge_rule
         self.grid_dimensions = []
 
-    def get_relative_neighbor_coordinates(self):
-        return self._neighbors
-
     def calculate_cell_neighbor_coordinates(self, cell_coordinate, grid_dimensions):
         """ Get a list of coordinates for the cell neighbors. The EdgeRule can reduce the returned neighbor count.
         :param cell_coordinate:     The coordinate of the cell to get the neighbors

src/cellular_automaton/cellular_automaton.py

@@ -1,39 +1,49 @@
 import multiprocessing
 
-from cellular_automaton.ca_grid import Grid
 from cellular_automaton.ca_rule import Rule
 
 
 class CellularAutomaton:
-    def __init__(self, grid: Grid, evolution_rule: Rule):
+    def __init__(self, cells, dimension, evolution_rule: Rule):
-        self.grid = grid
+        self.cells = cells
+        self.dimension = dimension
         self.evolution_rule = evolution_rule
-        self.evolution_iteration_index = 0
+        self.evolution_iteration_index = multiprocessing.RawValue('i', -1)
 
 
 class CellularAutomatonProcessor:
     def __init__(self, cellular_automaton, process_count: int = 1):
-        self.active = multiprocessing.Value('i', 1)
+        self.ca = cellular_automaton
-        cells = list(cellular_automaton.grid.get_cells().values())
+        self.evolve_range = range(len(self.ca.cells))
-        chunk_size = int(len(cells) / process_count)
+        self.pool = multiprocessing.Pool(processes=process_count,
-        self._processes = [multiprocessing.Process(target=_process_routine,
+                                         initializer=_init_process,
-                                                   name=str(i),
+                                         initargs=(self.ca.cells,
-                                                   args=(cells[i*chunk_size:i*chunk_size + chunk_size],
+                                                   self.ca.evolution_rule,
-                                                         cellular_automaton.evolution_rule,
+                                                   self.ca.evolution_iteration_index))
-                                                         self.active))
+        for cell in self.ca.cells:
-                           for i in range(process_count)]
+            cell.set_neighbours(None)
-        for p in self._processes:
-            p.start()
-        self.__cellular_automaton = None
 
-    def stop(self):
+    def evolve_x_times(self, x):
-        self.active.value = 0
+        for x in range(x):
-        for p in self._processes:
+            self.evolve()
-            p.join()
+
+    def evolve(self):
+        self.ca.evolution_iteration_index.value += 1
+        self.pool.map(_process_routine, self.evolve_range)
 
 
-def _process_routine(cells, rule, active):
+global_cells = None
-    while active.value == 1:
+global_rule = None
-        for cell in cells:
+global_iteration = None
-            cell.evolve_if_ready(rule.evolve_cell)
+
+
+def _init_process(cells, rule, index):
+    global global_rule, global_cells, global_iteration
+    global_cells = cells
+    global_rule = rule
+    global_iteration = index
+
+
+def _process_routine(i):
+    global_cells[i].evolve_if_ready(global_rule.evolve_cell, global_iteration.value)
 

test/test_cell.py

@@ -1,12 +1,41 @@
 import sys
 sys.path.append('../src')
 
+import cellular_automaton.ca_cell_state as cas
 import cellular_automaton.ca_cell as cac
 import unittest
 
 
+class TestState(cas.CellState):
+    def __init__(self):
+        super().__init__()
+
+
 class TestCellState(unittest.TestCase):
-    pass
+    def setUp(self):
+        self.cell = cac.Cell(TestState, [])
+        self.neighbours = [TestState() for x in range(5)]
+        for neighbour in self.neighbours:
+            neighbour.set_state_of_iteration((0, ), 0)
+        self.cell.set_neighbours(self.neighbours)
+
+    def cell_and_neighbours_active(self, iteration):
+        self.neighbours.append(self.cell.get_state())
+        all_active = True
+        for state in self.neighbours:
+            if not state.is_active(iteration):
+                all_active = False
+        return all_active
+
+    def test_evolve_activation(self):
+        self.cell.evolve_if_ready((lambda a, b: (1,)), 0)
+        all_active = self.cell_and_neighbours_active(1)
+        self.assertTrue(all_active)
+
+    def test_evolve_activation_on_no_change(self):
+        self.cell.evolve_if_ready((lambda a, b: (0,)), 0)
+        all_active = self.cell_and_neighbours_active(1)
+        self.assertFalse(all_active)
 
 
 if __name__ == '__main__':

test/test_cell_state.py

@@ -22,6 +22,21 @@ class TestCellState(unittest.TestCase):
         cell_state.set_state_of_iteration(new_state=(1,), iteration=0)
         self.assertEqual(tuple(cell_state.get_state_of_iteration(1)), (0,))
 
+    def test_redraw_state_on_change(self):
+        cell_state = cs.CellState(initial_state=(0,), draw_first_state=False)
+        cell_state.set_state_of_iteration(new_state=(1,), iteration=0)
+        self.assertTrue(cell_state.is_set_for_redraw())
+
+    def test_redraw_state_on_nochange(self):
+        cell_state = cs.CellState(initial_state=(0,), draw_first_state=False)
+        cell_state.set_state_of_iteration(new_state=(0,), iteration=0)
+        self.assertFalse(cell_state.is_set_for_redraw())
+
+    def test_active_state_after_set(self):
+        cell_state = cs.CellState(initial_state=(0,), draw_first_state=False)
+        cell_state.set_state_of_iteration(new_state=(1,), iteration=0)
+        self.assertFalse(cell_state.is_active(1))
+
 
 if __name__ == '__main__':
     unittest.main()

test/test_neighborhood.py

@@ -0,0 +1,40 @@
+import sys
+sys.path.append('../src')
+
+import cellular_automaton.ca_neighborhood as csn
+import unittest
+
+
+class TestCellState(unittest.TestCase):
+    def check_neighbors(self, neighborhood, neighborhood_sets):
+        for neighborhood_set in neighborhood_sets:
+            neighbors = neighborhood.calculate_cell_neighbor_coordinates(neighborhood_set[0], [3, 3])
+            if neighborhood_set[1] != neighbors:
+                print((neighborhood_set[1]), (neighbors))
+                return False
+        return True
+
+    def test_ignore_missing_neighbors(self):
+        neighborhood = csn.MooreNeighborhood(csn.EdgeRule.IGNORE_MISSING_NEIGHBORS_OF_EDGE_CELLS)
+        n00 = [[0, 0], [[1, 0], [0, 1], [1, 1]]]
+        n11 = [[1, 1], [[0, 0], [1, 0], [2, 0], [0, 1], [2, 1], [0, 2], [1, 2], [2, 2]]]
+        n22 = [[2, 2], [[1, 1], [2, 1], [1, 2]]]
+        self.assertTrue(self.check_neighbors(neighborhood, [n00, n11, n22]))
+
+    def test_ignore_edge_cells(self):
+        neighborhood = csn.MooreNeighborhood(csn.EdgeRule.IGNORE_EDGE_CELLS)
+        n00 = [[0, 0], []]
+        n11 = [[1, 1], [[0, 0], [1, 0], [2, 0], [0, 1], [2, 1], [0, 2], [1, 2], [2, 2]]]
+        n22 = [[2, 2], []]
+        self.assertTrue(self.check_neighbors(neighborhood, [n00, n11, n22]))
+
+    def test_cyclic_dimensions(self):
+        neighborhood = csn.MooreNeighborhood(csn.EdgeRule.FIRST_AND_LAST_CELL_OF_DIMENSION_ARE_NEIGHBORS)
+        n00 = [[0, 0], [[2, 2], [0, 2], [1, 2], [2, 0], [1, 0], [2, 1], [0, 1], [1, 1]]]
+        n11 = [[1, 1], [[0, 0], [1, 0], [2, 0], [0, 1], [2, 1], [0, 2], [1, 2], [2, 2]]]
+        n22 = [[2, 2], [[1, 1], [2, 1], [0, 1], [1, 2], [0, 2], [1, 0], [2, 0], [0, 0]]]
+        self.assertTrue(self.check_neighbors(neighborhood, [n00, n11, n22]))
+
+
+if __name__ == '__main__':
+    unittest.main()