added single and multi core deviation

scripts/main_ui.py

@@ -43,8 +43,8 @@ if __name__ == "__main__":
     random.seed(1000)
     # best single is 400/400 with 0,2 ca speed and 0,09 redraw / multi is 300/300 with 0.083
     neighborhood = MooreNeighborhood(EdgeRule.FIRST_AND_LAST_CELL_OF_DIMENSION_ARE_NEIGHBORS)
-    ca = make_cellular_automaton(dimension=[100, 100], neighborhood=neighborhood, rule=TestRule(), state_class=MyState)
-    ca_processor = CellularAutomatonProcessor(process_count=4, cellular_automaton=ca)
+    ca = make_cellular_automaton(dimension=[400, 400], neighborhood=neighborhood, rule=TestRule(), state_class=MyState)
+    ca_processor = CellularAutomatonProcessor(process_count=1, cellular_automaton=ca)
 
     ca_window = PyGameFor2D(window_size=[1000, 800], cellular_automaton=ca)
-    ca_window.main_loop(cellular_automaton_processor=ca_processor, ca_iterations_per_draw=10)
+    ca_window.main_loop(cellular_automaton_processor=ca_processor, ca_iterations_per_draw=1)

src/cellular_automaton/ca_cell_state.py

@@ -3,31 +3,30 @@ from ctypes import c_float, c_bool
 
 
 class CellState:
-    _state_save_slot_count = 2
     """
         This is the base class for all cell states.
         When using the cellular automaton display, inherit this class and implement get_state_draw_color.
     """
+
+    _state_save_slot_count = 2
+
     def __init__(self, initial_state=(0., ), draw_first_state=True):
-        self._state_slots = [RawArray(c_float, initial_state) for i in range(self.__class__._state_save_slot_count)]
-        self._active = [RawValue(c_bool, False) for i in range(self.__class__._state_save_slot_count)]
-        self._active[0].value = True
-        if draw_first_state:
-            self._dirty = RawValue(c_bool, True)
-        else:
-            self._dirty = RawValue(c_bool, False)
+        self._state_slots = [list(initial_state) for i in range(self.__class__._state_save_slot_count)]
+        self._active = [False for i in range(self.__class__._state_save_slot_count)]
+        self._active[0] = True
+        self._dirty = draw_first_state
 
     def is_active(self, iteration):
-        return self._active[self.__calculate_slot(iteration)]
+        return self._active[self._calculate_slot(iteration)]
 
     def set_active_for_next_iteration(self, iteration):
-        self._active[self.__calculate_slot(iteration + 1)].value = True
+        self._active[self._calculate_slot(iteration + 1)] = True
 
     def is_set_for_redraw(self):
-        return self._dirty.value
+        return self._dirty
 
     def was_redrawn(self):
-        self._dirty.value = False
+        self._dirty = False
 
     def get_state_of_last_iteration(self, current_iteration_index):
         return self.get_state_of_iteration(current_iteration_index - 1)
@@ -37,7 +36,7 @@ class CellState:
         :param iteration:   Uses the iteration index, to differ between concurrent states.
         :return The state for this iteration.
         """
-        return self._state_slots[self.__calculate_slot(iteration)]
+        return self._state_slots[self._calculate_slot(iteration)]
 
     def set_state_of_iteration(self, new_state, iteration):
         """ Will set the new state for the iteration modulo number of saved states.
@@ -45,25 +44,50 @@ class CellState:
         :param iteration:  Uses the iteration index, to differ between concurrent states.
         :return True if state has changed.
         """
+        changed = self._change_state(new_state, iteration)
+        self._dirty |= changed
+        self._active[self._calculate_slot(iteration)] = False
+        return changed
 
+    def _change_state(self, new_state, iteration):
         current_state = self.get_state_of_iteration(iteration)
-
         changed = False
-        for i in range(len(current_state)):
+        for i, ns in enumerate(new_state):
             try:
-                if current_state[i] != new_state[i]:
+                if current_state[i] != ns:
                     changed = True
-                    current_state[i] = new_state[i]
+                    current_state[i] = ns
             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
 
     @classmethod
-    def __calculate_slot(cls, iteration):
+    def _calculate_slot(cls, iteration):
         return iteration % cls._state_save_slot_count
+
+
+class SynchronousCellState(CellState):
+    def __init__(self, initial_state=(0., ), draw_first_state=True):
+        super().__init__(initial_state, draw_first_state)
+        self._state_slots = [RawArray(c_float, initial_state) for i in range(self.__class__._state_save_slot_count)]
+        self._active = [RawValue(c_bool, False) for i in range(self.__class__._state_save_slot_count)]
+        self._active[0].value = True
+        self._dirty = RawValue(c_bool, draw_first_state)
+
+    def set_active_for_next_iteration(self, iteration):
+        self._active[self._calculate_slot(iteration + 1)].value = True
+
+    def is_set_for_redraw(self):
+        return self._dirty.value
+
+    def was_redrawn(self):
+        self._dirty.value = False
+
+    def set_state_of_iteration(self, new_state, iteration):
+        changed = self._change_state(new_state, iteration)
+        self._dirty.value |= changed
+        self._active[self._calculate_slot(iteration)].value = False
+        return changed

src/cellular_automaton/cellular_automaton.py

@@ -17,11 +17,18 @@ class CellularAutomatonProcessor:
         self.ca = cellular_automaton
         cells = {i: (c.state, c.neighbours) for i, c in enumerate(self.ca.cells)}
         self.evolve_range = range(len(self.ca.cells))
-        self.pool = multiprocessing.Pool(processes=process_count,
-                                         initializer=_init_process,
-                                         initargs=(cells,
-                                                   self.ca.evolution_rule,
-                                                   self.ca.evolution_iteration_index))
+        self._evolve_method = lambda x, y: None
+
+        if process_count > 1:
+            self.pool = multiprocessing.Pool(processes=process_count,
+                                             initializer=_init_process,
+                                             initargs=(cells,
+                                                       self.ca.evolution_rule,
+                                                       self.ca.evolution_iteration_index))
+            self._evolve_method = self.pool.map
+        else:
+            _init_process(cells, self.ca.evolution_rule, self.ca.evolution_iteration_index)
+            self._evolve_method = lambda x, y: list(map(x, y))
         for cell in self.ca.cells:
             del cell.neighbours
 
@@ -31,7 +38,7 @@ class CellularAutomatonProcessor:
 
     def evolve(self):
         self.ca.evolution_iteration_index.value += 1
-        self.pool.map(_process_routine, self.evolve_range)
+        self._evolve_method(_process_routine, self.evolve_range)
 
 
 global_cells = None