second try

.gitignore

@@ -2,4 +2,5 @@
 *.pyc__pycache__
 __pycache__
 .coverage
-htmlcov/
+htmlcov/
+*.orig

scripts/main_ui.py

@@ -1,63 +1,51 @@
 #!/usr/bin/env python3
 
-import random
-
-from cellular_automaton.cellular_automaton import CellularAutomaton, CellularAutomatonProcessor
-from cellular_automaton.ca_rule import Rule
-from cellular_automaton.ca_neighborhood import MooreNeighborhood, EdgeRule
-from cellular_automaton.ca_display import PyGameFor2D
 from cellular_automaton.ca_cell_state import CellState
-from cellular_automaton.ca_grid import Grid
+from cellular_automaton.ca_rule import Rule
 
 
 class TestRule(Rule):
-    def evolve_cell(self, cell, iteration_index):
-        if cell.state is None:
-            return self._init_state(cell)
-        else:
-            return self._evolve_state(cell, iteration_index)
-
     @staticmethod
-    def _evolve_state(cell, iteration_index):
+    def evolve_cell(last_cell_state, last_neighbour_states):
         try:
-            left_neighbour_state = cell.neighbours[0].state.get_status_of_iteration(iteration_index - 1)
+            return last_neighbour_states[0]
-            active = cell.state.set_status_of_iteration(left_neighbour_state, iteration_index)
-            if active:
-                cell.is_set_for_redraw = True
-            return active
         except IndexError:
-            return False
+            pass
+        return False
 
-    @staticmethod
+
-    def _init_state(cell):
+class MyState(CellState):
+    def __init__(self):
         rand = random.randrange(0, 101, 1)
-        if rand <= 99:
+        init = 0
-            cell.state = MyStatus(0)
+        if rand > 99:
-            return False
+            init = 1
-        else:
-            cell.state = MyStatus(1)
-            cell.is_set_for_redraw = True
-            return True
 
-
+        super().__init__(init, draw_first_state=False)
-class MyStatus(CellState):
-    def __init__(self, initial_state):
-        super().__init__([initial_state])
 
     def get_state_draw_color(self, iteration):
         red = 0
-        if self._state_slots[iteration % 2][0]:
+        if self.get_state_of_last_iteration(iteration)[0]:
             red = 255
         return [red, 0, 0]
 
 
 if __name__ == "__main__":
+    import random
+    from multiprocessing import freeze_support
+
+    from cellular_automaton.cellular_automaton import CellularAutomaton, CellularAutomatonProcessor
+    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)
     rule = TestRule()
     grid = Grid(dimension=[400, 400],
-                neighborhood=MooreNeighborhood(EdgeRule.FIRST_AND_LAST_CELL_OF_DIMENSION_ARE_NEIGHBORS))
+                neighborhood=MooreNeighborhood(EdgeRule.FIRST_AND_LAST_CELL_OF_DIMENSION_ARE_NEIGHBORS),
+                state_class=MyState)
     ca = CellularAutomaton(grid, rule)
     ca_window = PyGameFor2D(window_size=[1000, 800], cellular_automaton=ca)
-    ca_processor = CellularAutomatonProcessor(process_count=8)
+    ca_processor = CellularAutomatonProcessor(process_count=2, cellular_automaton=ca)
     ca_window.main_loop(cellular_automaton_processor=ca_processor,
                         ca_iterations_per_draw=5)

src/cellular_automaton/ca_cell.py

@@ -1,7 +1,61 @@
+import multiprocessing
+
+from cellular_automaton.ca_cell_state import CellState
+
+
 class Cell:
-    def __init__(self, name, coordinate: list):
+    def __init__(self, name, state_class: CellState.__class__, coordinate: list):
-        self.name = name
+        self._name = name
-        self.coordinate = coordinate
+        self._coordinate = coordinate
-        self.neighbours = []
+        self._state = state_class()
-        self.state = None
+        self._neighbours = []
-        self.is_set_for_redraw = False
+        self._active = multiprocessing.Value('i', 1)
+        self._age = multiprocessing.Value('i', 0)
+
+    def set_neighbours(self, neighbours):
+        self._neighbours = neighbours
+
+    def get_coordinate(self):
+        return self._coordinate
+
+    def evolve_if_ready(self, rule):
+        if self._neighbours_are_younger():
+            if self._is_active():
+                new_state = rule(self.get_current_state(), self.get_neighbour_states())
+                self.set_new_state_and_activate(new_state)
+
+            self.increase_age()
+
+    def _neighbours_are_younger(self):
+        for n in self._neighbours:
+            if n.get_age() < self.get_age():
+                return False
+
+    def get_age(self):
+        return self._age.value
+
+    def _is_active(self):
+        return self._active.value > self._age.value
+
+    def get_current_state(self):
+        return self._state.get_state_of_iteration(self._age.value)
+
+    def get_neighbour_states(self):
+        return [n.get_state_from_iteration(self._age.value) for n in self._neighbours]
+
+    def set_new_state_and_activate(self, new_state: CellState):
+        changed = self._state.set_current_state(new_state, self._age.value + 1)
+        if changed:
+            self._set_active()
+
+    def _set_active(self):
+        self.set_active_for_next_iteration(self._age.value)
+        for n in self._neighbours:
+            n.set_active_for_next_iteration(self._age.value)
+
+    def set_active_for_next_iteration(self, iteration):
+        self._active.value = max(self._active.value, iteration + 1)
+
+    def increase_age(self):
+        with self._age.get_lock():
+            self._age += 1

src/cellular_automaton/ca_cell_state.py

@@ -1,30 +1,65 @@
+from multiprocessing import Array, Value
+
+
 class CellState:
     """
         This is the base class for all cell states.
         When using the cellular automaton display, inherit this class and implement get_state_draw_color.
     """
-    def __init__(self, initial_state, state_save_slot_count=2):
+    def __init__(self, initial_state=(0., ), state_save_slot_count=2, draw_first_state=True):
         self._state_save_slot_count = state_save_slot_count
-        self._state_slots = [initial_state] * state_save_slot_count
+        self._state_slots = [Array('d', initial_state)] * state_save_slot_count
+        if draw_first_state:
+            self._dirty = Value('i', 1)
+        else:
+            self._dirty = Value('i', 0)
 
-    def set_status_of_iteration(self, new_status, iteration):
+    def is_set_for_redraw(self):
-        """ Will set the new status for the iteration modulo number of saved states.
+        return self._dirty != 1
-        :param new_status:  The new status to set.
+
+    def get_state_changes(self):
+        return self._dirty
+
+    def set_for_redraw(self):
+        self._dirty = 1
+
+    def was_redrawn(self):
+        self._dirty = 0
+
+    def set_current_state(self, new_state, current_iteration_index):
+        return self.set_state_of_iteration(new_state, current_iteration_index)
+
+    def get_state_of_last_iteration(self, current_iteration_index):
+        return self.get_state_of_iteration(current_iteration_index - 1)
+
+    def set_state_of_iteration(self, new_state, iteration):
+        """ Will set the new state for the iteration modulo number of saved states.
+        :param new_state:  The new state to set.
         :param iteration:   Uses the iteration index, to differ between concurrent states.
-        :return True if status has changed.
+        :return True if state has changed.
         """
         slot_count = self._state_save_slot_count
         states = self._state_slots
 
-        states[iteration % slot_count] = new_status
+        current_state = states[iteration % slot_count]
 
-        return states[(iteration - 1) % slot_count] \
+        changed = False
-            != states[iteration % slot_count]
+        for i in range(len(current_state)):
+            try:
+                if current_state[i] != new_state[i]:
+                    changed = True
 
-    def get_status_of_iteration(self, iteration):
+                current_state[i] = new_state[i]
-        """ Will return the status for the iteration modulo number of saved states.
+            except IndexError:
+                raise IndexError("New State length or type is invalid!")
+
+        self._dirty |= changed
+        return changed
+
+    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 status for this iteration.
+        :return The state for this iteration.
         """
         return self._state_slots[iteration % self._state_save_slot_count]
 

src/cellular_automaton/ca_display.py

@@ -59,25 +59,27 @@ class PyGameFor2D:
 
         while running:
             time_ca_start = time.time()
-            cellular_automaton_processor.evolve_x_times(self._cellular_automaton, ca_iterations_per_draw)
             time_ca_end = time.time()
             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)
+
             for event in pygame.event.get():
                 if event.type == pygame.QUIT:
+                    cellular_automaton_processor.stop()
                     running = False
 
 
 def _cell_redraw_rectangles(cells, evolution_index, display_info):
     for cell in cells:
-        if cell.is_set_for_redraw:
+        if cell.state.is_set_for_redraw():
             cell_color = cell.state.get_state_draw_color(evolution_index)
             cell_pos = _calculate_cell_position(display_info.cell_size, cell)
             surface_pos = list(map(operator.add, cell_pos, display_info.grid_pos))
             yield display_info.screen.fill(cell_color, (surface_pos, display_info.cell_size))
-            cell.is_set_for_redraw = False
+            cell.state.was_redrawn()
 
 
 def _calculate_cell_position(cell_size, cell):

src/cellular_automaton/ca_grid.py

@@ -3,10 +3,11 @@ from cellular_automaton.ca_neighborhood import Neighborhood
 
 
 class Grid:
-    def __init__(self, dimension: list, neighborhood: Neighborhood):
+    def __init__(self, dimension: list, neighborhood: Neighborhood, state_class):
         self._dimension = dimension
         self._cells = {}
         self._active_cells = {}
+        self._state_class = state_class
 
         self._init_cells(neighborhood)
 
@@ -58,7 +59,7 @@ class Grid:
             self._recursive_step_down_dimensions(coordinate, dimension_index, self._create_cells)
         except IndexError:
             coordinate_string = _join_coordinate(coordinate)
-            self._cells[coordinate_string] = Cell(coordinate_string, coordinate)
+            self._cells[coordinate_string] = Cell(coordinate_string, self._state_class, coordinate)
 
     def _recursive_step_down_dimensions(self, coordinate, dimension_index, recursion_method):
         """ For the range of the current dimension, recalls the recursion method.
@@ -72,9 +73,9 @@ class Grid:
 
     def _set_cell_neighbours(self, neighborhood):
         for cell in self._cells.values():
-            neighbours_coordinates = neighborhood.calculate_cell_neighbor_coordinates(cell.coordinate,
+            neighbours_coordinates = neighborhood.calculate_cell_neighbor_coordinates(cell.get_coordinate(),
                                                                                       self._dimension)
-            cell.neighbours = list(map(self._get_cell_by_coordinate, neighbours_coordinates))
+            cell.set_neighbours(list(map(self._get_cell_by_coordinate, neighbours_coordinates)))
 
     def _get_cell_by_coordinate(self, coordinate):
         return self._cells[_join_coordinate(coordinate)]

src/cellular_automaton/ca_rule.py

@@ -1,4 +1,3 @@
-from cellular_automaton.ca_cell import Cell
 from abc import abstractmethod
 
 
@@ -6,11 +5,12 @@ class Rule:
     def __init__(self):
         pass
 
+    @staticmethod
     @abstractmethod
-    def evolve_cell(self, cell: Cell, iteration_index: int):
+    def evolve_cell(last_cell_state, last_neighbour_states):
         """ Calculates and sets new state of 'cell'.
-        :param cell:              The cell to calculate new state for.
+        :param last_cell_state:   The cells current state to calculate new state for.
-        :param iteration_index:   The current iteration index, to choose the correct state.
+        :param last_neighbour_states:  The cells neighbours current states.
         :return: True if state changed, False if not.
         A cells evolution will only be called if it or at least one of its neighbours has changed last iteration cycle.
         """

src/cellular_automaton/cellular_automaton.py

@@ -1,9 +1,7 @@
+import multiprocessing
+
 from cellular_automaton.ca_grid import Grid
 from cellular_automaton.ca_rule import Rule
-from cellular_automaton.ca_cell_state import CellState
-
-from multiprocessing import Process, Pipe,  Array, Value
-import multiprocessing
 
 
 class CellularAutomaton:
@@ -13,160 +11,29 @@ class CellularAutomaton:
         self.evolution_iteration_index = 0
 
 
-class _EvolutionProcess:
-    def __init__(self, process: Process, pipe: Pipe):
-        self.process = process
-        self.pipe = pipe
-        self.cell = None
-
-
 class CellularAutomatonProcessor:
-    def __init__(self, process_count: int = 1):
+    def __init__(self, cellular_automaton, process_count: int = 1):
-        self._processes = list(_create_processes(process_count))
+        self.active = multiprocessing.Value('i', 1)
+        cells = list(cellular_automaton.grid.get_cells().values())
+        chunk_size = int(len(cells) / process_count)
+        self._processes = [multiprocessing.Process(target=_process_routine,
+                                                   name=str(i),
+                                                   args=(cells[i*chunk_size:i*chunk_size + chunk_size],
+                                                         cellular_automaton.evolution_rule,
+                                                         self.active))
+                           for i in range(process_count)]
+        for p in self._processes:
+            p.start()
         self.__cellular_automaton = None
 
-    def evolve_x_times(self, cellular_automaton: CellularAutomaton, evolution_steps: int):
+    def stop(self):
-        """ Evolve all cells for x time steps.
+        self.active.value = 0
-        :param cellular_automaton: The cellular automaton to evolve.
+        for p in self._processes:
-        :param evolution_steps: The count of evolutions done.
+            p.join()
-        :return: True if all cells are inactive
-        """
-        for evo in range(evolution_steps):
-            finished = self.evolve(cellular_automaton)
-            if finished:
-                return True
-        return False
-
-    def evolve(self, cellular_automaton: CellularAutomaton):
-        """ Evolves all active cells for one time step.
-        :param cellular_automaton: The cellular automaton to evolve.
-        :return: True if all cells are inactive.
-        """
-        self.__cellular_automaton = cellular_automaton
-        if self._is_evolution_finished():
-            print("finished")
-            return True
-        else:
-            cellular_automaton.evolution_iteration_index += 1
-            self._evolve_all_active_cells()
-            return False
-
-    def _is_evolution_finished(self):
-        return len(self.__cellular_automaton.grid.get_active_cell_names()) == 0
-
-    def _evolve_all_active_cells(self):
-        active_cells = self.__cellular_automaton.grid.get_active_cells()
-        self.__cellular_automaton.grid.clear_active_cells()
-        self._evolve_cells(active_cells.values())
-        print(len(self.__cellular_automaton.grid.get_active_cells()))
-
-    def _evolve_cells(self, cells):
-        cellular_automaton = self.__cellular_automaton
-        processes = self._processes
-        process_count = len(processes)
-        for i, cell in enumerate(cells):
-            evolution_process = processes[i % process_count]
-            if evolution_process.cell:
-                response = evolution_process.pipe.recv()
-                evolved_cell = evolution_process.cell
-                evolved_cell.state = response[0]
-                evolved_cell.is_set_for_redraw |= response[1]
-                if evolved_cell.is_set_for_redraw:
-                    cellular_automaton.grid.set_cells_active([evolved_cell] + evolved_cell.neighbours)
-                evolution_process.cell = None
-
-            cell_info = self.read_cell_info(cell, cellular_automaton.evolution_iteration_index)
-            evolution_process.pipe.send(cell_info)
-            evolution_process.cell = cell
-
-        for evolution_process in processes:
-            response = evolution_process.pipe.recv()
-            cell = evolution_process.cell
-            cell.state = response[0]
-            cell.is_set_for_redraw |= response[1]
-            if cell.is_set_for_redraw:
-                cellular_automaton.grid.set_cells_active([cell] + cell.neighbours)
-            evolution_process.cell = None
-
-            # if cellular_automaton.evolution_rule.evolve_cell(cell.state, cellular_automaton.evolution_iteration_index):
-            #     cellular_automaton.grid.set_cells_active([cell] + cell.neighbours)
-
-    @staticmethod
-    def read_cell_info(cell, iteration):
-        coordinate = cell.coordinate
-        return [coordinate, cell.state, [n.state for n in cell.neighbours], iteration]
-
-        # if cell.state is not None:
-        #     cell_state = cell.state.get_status_of_iteration(iteration - 1)
-        # else:
-        #     cell_state = None
-        #
-        # neighbor_states = []
-        # for neighbor in cell.neighbours:
-        #     if neighbor.state is not None:
-        #         neighbor_state = neighbor.state.get_status_of_iteration(iteration - 1)
-        #     else:
-        #         neighbor_state = None
-        #     neighbor_states.append(neighbor_state)
-        #
-        # return [coordinate,  cell_state, neighbor_states]
 
 
-def _create_processes(count):
+def _process_routine(cells, rule, active):
-    for i in range(count):
+    while active.value == 1:
-        parent_pipe_connection, child_pipe_connection = Pipe()
+        for cell in cells:
-        p = Process(target=process_routine, args=(child_pipe_connection, ))
+            cell.evolve_if_ready(rule)
-        p.start()
-        yield _EvolutionProcess(p, parent_pipe_connection)
 
-
-def process_routine(pipe_conn: Pipe):
-    while True:
-        info = pipe_conn.recv()
-        pipe_conn.send(evolve_cell(*info))
-
-
-def evolve_cell(coordinate, cell_state, neighbor_states, index):
-    if cell_state is None:
-        return _init_state()
-    else:
-        new_state = _evolve_state(cell_state, neighbor_states, index)
-        if new_state is None:
-            print(",".join([str(x) for x in neighbor_states]))
-            return [cell_state, False]
-        else:
-            changed = cell_state.set_status_of_iteration(new_state, index)
-            return [cell_state, changed]
-
-
-def _evolve_state(cell_state, neighbor_states, index):
-    try:
-        left_neighbour_state = neighbor_states[0].get_status_of_iteration(index - 1)
-        return left_neighbour_state
-    except (IndexError, AttributeError):
-        return None
-
-
-import random
-
-
-def _init_state():
-    rand = random.randrange(0, 101, 1)
-    if rand <= 99:
-        return [MyStatus(0), False]
-    else:
-        return [MyStatus(1), True]
-
-
-class MyStatus(CellState):
-    def __init__(self, initial_state):
-        super().__init__(initial_state)
-
-    def get_state_draw_color(self, iteration):
-        red = 0
-        if self._state_slots[iteration % 2][0]:
-            red = 255
-        return [red, 0, 0]
-
-    def __str__(self):
-        return super().__str__()