refactoring and dynamic neighbourhood

scripts/main_ui.py

@@ -1,43 +1,43 @@
 #!/usr/bin/env python3
 
 import random
-from multiprocessing import freeze_support
-from cellular_automaton import *
+from cellular_automaton.ca_rule import Rule
+from cellular_automaton.ca_cell_state import CellState, SynchronousCellState
 
 
 class TestRule(Rule):
     @staticmethod
-    def evolve_cell(last_cell_state, neighbours_last_states):
+    def evolve_cell(last_cell_state, neighbors_last_states):
         try:
-            return neighbours_last_states[0]
+            return neighbors_last_states[0]
         except IndexError:
             return last_cell_state
 
 
-class MyState(SynchronousCellState):
+# class MyState(SynchronousCellState):
+class MyState(CellState):
+    random_seed = random.seed(1000)
+
     def __init__(self):
         rand = random.randrange(0, 101, 1)
         init = max(.0, float(rand - 99))
         super().__init__((init,), draw_first_state=init > 0)
 
-    def get_state_draw_color(self, iteration):
-        state1 = self.get_state_of_iteration(iteration)[0]
-        return [255 if state1 else 0, 0, 0]
-
-
-def make_cellular_automaton(dimension, neighborhood, rule, state_class):
-    cells = CAFactory.make_cellular_automaton(dimension=dimension, neighborhood=neighborhood, state_class=state_class)
-    return CellularAutomaton(cells, dimension, rule)
+    def get_state_draw_color(self, evolution_step):
+        state = self.get_state_of_evolution_step(evolution_step)[0]
+        return [255 if state else 0, 0, 0]
 
 
 if __name__ == "__main__":
-    freeze_support()
-
-    random.seed(1000)
+    from cellular_automaton import *
     # 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 = CellularAutomatonMultiProcessor(cellular_automaton=ca, process_count=4)
+    ca = CAFactory.make_cellular_automaton(dimension=[100, 100],
+                                           neighborhood=neighborhood,
+                                           rule=TestRule(),
+                                           state_class=MyState)
+    # ca_processor = CellularAutomatonMultiProcessor(cellular_automaton=ca, process_count=4)
+    ca_processor = CellularAutomatonProcessor(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=1)
+    ca_window.main_loop(cellular_automaton_processor=ca_processor, evolution_steps_per_draw=1)

src/cellular_automaton/__init__.py

@@ -1,7 +1,8 @@
 from .ca_cell import *
 from .ca_cell_state import *
 from .ca_display import *
-from .ca_factory import *
 from .ca_neighborhood import *
 from .ca_rule import *
+from .ca_state import *
 from .cellular_automaton import *
+from .ca_factory import *

src/cellular_automaton/ca_cell.py

@@ -5,19 +5,17 @@ from typing import Type
 class Cell:
     def __init__(self, state_class: Type[CellState]):
         self.state = state_class()
-        self.neighbours = []
+        self.neighbor_states = []
 
-    @staticmethod
-    def evolve_if_ready(cell, rule, iteration):
-        if cell.state.is_active(iteration):
-            new_state = rule(cell.state.get_state_of_last_iteration(iteration),
-                             [n.get_state_of_last_iteration(iteration) for n in cell.neighbours])
-            Cell.set_new_state_and_activate(cell, new_state, iteration)
+    def evolve_if_ready(self, rule, evolution_step):
+        if self.state.is_active(evolution_step):
+            new_state = rule(self.state.get_state_of_last_evolution_step(evolution_step),
+                             [n.get_state_of_last_evolution_step(evolution_step) for n in self.neighbor_states])
+            self.set_new_state_and_activate(new_state, evolution_step)
 
-    @staticmethod
-    def set_new_state_and_activate(cell, new_state: CellState, iteration):
-        changed = cell.state.set_state_of_iteration(new_state, iteration)
+    def set_new_state_and_activate(self, new_state: CellState, evolution_step):
+        changed = self.state.set_state_of_evolution_step(new_state, evolution_step)
         if changed:
-            cell.state.set_active_for_next_iteration(iteration)
-            for n in cell.neighbours:
-                n.set_active_for_next_iteration(iteration)
+            self.state.set_active_for_next_evolution_step(evolution_step)
+            for n in self.neighbor_states:
+                n.set_active_for_next_evolution_step(evolution_step)

src/cellular_automaton/ca_cell_state.py

@@ -16,63 +16,82 @@ class CellState:
         self._active[0] = True
         self._dirty = draw_first_state
 
-    def is_active(self, iteration):
-        return self._active[self._calculate_slot(iteration)]
+    def is_active(self, current_evolution_step):
+        """ Returns the active status for the requested evolution_step
+        :param current_evolution_step: The evolution_step of interest.
+        :return: True if the cell state is set active for this evolution_step.
+        """
+        return self._active[self._calculate_slot(current_evolution_step)]
 
-    def set_active_for_next_iteration(self, iteration):
-        self._active[self._calculate_slot(iteration + 1)] = True
+    def set_active_for_next_evolution_step(self, current_evolution_step):
+        """ Sets the cell active for the next evolution_step, so it will be evolved.
+        :param current_evolution_step: The current evolution_step index.
+        :return:
+        """
+        self._active[self._calculate_slot(current_evolution_step + 1)] = True
 
     def is_set_for_redraw(self):
+        """ States if this state should be redrawn.
+        :return: True if redraw is needed.
+        """
         return self._dirty
 
     def was_redrawn(self):
+        """ Remove the state from redraw cycle until next state change """
         self._dirty = False
 
-    def get_state_of_last_iteration(self, current_iteration_index):
-        return self.get_state_of_iteration(current_iteration_index - 1)
+    def get_state_of_last_evolution_step(self, current_evolution_step):
+        return self.get_state_of_evolution_step(current_evolution_step - 1)
 
-    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.
+    def get_state_of_evolution_step(self, evolution_step):
+        """ Returns the state of the evolution_step.
+        :param evolution_step:  Uses the evolution_step index, to differ between concurrent states.
+        :return The state of this evolution_step.
         """
-        return self._state_slots[self._calculate_slot(iteration)]
+        return self._state_slots[self._calculate_slot(evolution_step)]
 
-    def set_state_of_iteration(self, new_state, iteration):
-        """ Will set the new state for the iteration modulo number of saved states.
+    def set_state_of_evolution_step(self, new_state, evolution_step):
+        """ Sets the new state for the evolution_step.
         :param new_state:  The new state to set.
-        :param iteration:  Uses the iteration index, to differ between concurrent states.
-        :return True if state has changed.
+        :param evolution_step:  The evolution_step index, to differ between concurrent states.
+        :return True if the state really changed.
         """
-        self._change_state_values(new_state, iteration)
-        changed = self._did_state_change(iteration)
+        changed = self._set_new_state_if_valid(new_state, evolution_step)
         self._dirty |= changed
-        self._active[self._calculate_slot(iteration)] = False
-
+        self._active[self._calculate_slot(evolution_step)] = False
         return changed
 
-    def _did_state_change(self, iteration):
-        for a, b in zip(self._state_slots[self._calculate_slot(iteration)],
-                        self._state_slots[self._calculate_slot(iteration - 1)]):
+    def _set_new_state_if_valid(self, new_state, evolution_step):
+        current_state = self.get_state_of_evolution_step(evolution_step)
+        if len(new_state) != len(current_state):
+            raise IndexError("State length may not change!")
+
+        self.__change_current_state_values(current_state, new_state)
+        return self.__did_state_change(evolution_step)
+
+    @staticmethod
+    def __change_current_state_values(current_state, new_state):
+        for i, ns in enumerate(new_state):
+            if current_state[i] != ns:
+                current_state[i] = ns
+
+    def __did_state_change(self, evolution_step):
+        for a, b in zip(self.get_state_of_evolution_step(evolution_step),
+                        self.get_state_of_last_evolution_step(evolution_step)):
             if a != b:
                 return True
         return False
 
-    def _change_state_values(self, new_state, iteration):
-        current_state = self.get_state_of_iteration(iteration)
-        if len(new_state) != len(current_state):
-            raise IndexError("State length may not change!")
-
-        for i, ns in enumerate(new_state):
-                if current_state[i] != ns:
-                    current_state[i] = ns
-
-    def get_state_draw_color(self, iteration):
+    def get_state_draw_color(self, evolution_step):
+        """ When implemented should return the color representing the requested state.
+        :param evolution_step: Requested evolution_step.
+        :return: Color of the state as rgb tuple
+        """
         raise NotImplementedError
 
     @classmethod
-    def _calculate_slot(cls, iteration):
-        return iteration % cls._state_save_slot_count
+    def _calculate_slot(cls, evolution_step):
+        return evolution_step % cls._state_save_slot_count
 
 
 class SynchronousCellState(CellState):
@@ -86,8 +105,8 @@ class SynchronousCellState(CellState):
         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 set_active_for_next_evolution_step(self, current_evolution_step):
+        self._active[self._calculate_slot(current_evolution_step + 1)].value = True
 
     def is_set_for_redraw(self):
         return self._dirty.value
@@ -95,13 +114,8 @@ class SynchronousCellState(CellState):
     def was_redrawn(self):
         self._dirty.value = False
 
-    def set_state_of_iteration(self, new_state, iteration):
-        self._change_state_values(new_state, iteration)
-        changed = self._did_state_change(iteration)
+    def set_state_of_evolution_step(self, new_state, evolution_step):
+        changed = self._set_new_state_if_valid(new_state, evolution_step)
         self._dirty.value |= changed
-        self._active[self._calculate_slot(iteration)].value = False
+        self._active[self._calculate_slot(evolution_step)].value = False
         return changed
-
-    @classmethod
-    def _calculate_slot(cls, iteration):
-        return iteration % cls._state_save_slot_count

src/cellular_automaton/ca_display.py

@@ -7,7 +7,8 @@ import pstats
 from pympler import asizeof
 
 
-from cellular_automaton.cellular_automaton import CellularAutomaton, CellularAutomatonProcessor
+from cellular_automaton.ca_state import CellularAutomatonState
+from cellular_automaton.cellular_automaton import CellularAutomatonProcessor
 
 
 class _DisplayInfo:
@@ -19,7 +20,7 @@ class _DisplayInfo:
 
 
 class DisplayFor2D:
-    def __init__(self, grid_rect: list, cellular_automaton: CellularAutomaton, screen):
+    def __init__(self, grid_rect: list, cellular_automaton: CellularAutomatonState, screen):
         self._cellular_automaton = cellular_automaton
         cell_size = self._calculate_cell_display_size(grid_rect[-2:])
         self._display_info = _DisplayInfo(grid_rect[-2:], grid_rect[:2], cell_size, screen)
@@ -31,8 +32,8 @@ class DisplayFor2D:
     def _cell_redraw_rectangles(self):
         for coordinate, cell in self._cellular_automaton.cells.items():
             if cell.state.is_set_for_redraw():
-                cell_color = cell.state.get_state_draw_color(self._cellular_automaton.evolution_iteration_index)
-                cell_pos = _calculate_cell_position(self._display_info.cell_size, coordinate)
+                cell_color = cell.state.get_state_draw_color(self._cellular_automaton.current_evolution_step)
+                cell_pos = self._calculate_cell_position(self._display_info.cell_size, coordinate)
                 surface_pos = list(map(operator.add, cell_pos, self._display_info.grid_pos))
                 yield self._display_info.screen.fill(cell_color, (surface_pos, self._display_info.cell_size))
                 cell.state.was_redrawn()
@@ -41,9 +42,13 @@ class DisplayFor2D:
         grid_dimension = self._cellular_automaton.dimension
         return list(map(operator.truediv, grid_size, grid_dimension))
 
+    @staticmethod
+    def _calculate_cell_position(cell_size, coordinate):
+        return list(map(operator.mul, cell_size, coordinate))
+
 
 class PyGameFor2D:
-    def __init__(self, window_size: list, cellular_automaton: CellularAutomaton):
+    def __init__(self, window_size: list, cellular_automaton: CellularAutomatonState):
         self._window_size = window_size
         self._cellular_automaton = cellular_automaton
         pygame.init()
@@ -63,7 +68,7 @@ class PyGameFor2D:
         update_rect = self._screen.blit(label, pos)
         pygame.display.update(update_rect)
 
-    def main_loop(self, cellular_automaton_processor: CellularAutomatonProcessor, ca_iterations_per_draw):
+    def main_loop(self, cellular_automaton_processor: CellularAutomatonProcessor, evolution_steps_per_draw):
         running = True
         cellular_automaton_processor.evolve()
         first = True
@@ -75,7 +80,7 @@ class PyGameFor2D:
                 self._evolve_with_performance(cellular_automaton_processor)
                 first = False
             else:
-                cellular_automaton_processor.evolve_x_times(ca_iterations_per_draw)
+                cellular_automaton_processor.evolve_x_times(evolution_steps_per_draw)
             time_ca_end = time.time()
             self.ca_display.redraw_cellular_automaton()
             time_ds_end = time.time()
@@ -95,7 +100,3 @@ class PyGameFor2D:
         p.sort_stats('time').print_stats(10)
         print("TOTAL TIME: " + "{0:.4f}".format(time_ca_end - time_ca_start) + "s")
         print("SIZE: " + "{0:.4f}".format(size / (1024 * 1024)) + "MB")
-
-
-def _calculate_cell_position(cell_size, coordinate):
-    return list(map(operator.mul, cell_size, coordinate))

src/cellular_automaton/ca_factory.py

@@ -1,5 +1,4 @@
-from cellular_automaton.ca_cell import Cell, CellState
-from cellular_automaton.ca_neighborhood import Neighborhood
+from cellular_automaton import *
 from typing import Type
 import itertools
 
@@ -7,12 +6,12 @@ import itertools
 class CAFactory:
     @staticmethod
     def make_cellular_automaton(dimension,
-                                neighborhood: Type[Neighborhood],
-                                state_class: Type[CellState]):
-
+                                neighborhood: Neighborhood,
+                                state_class: Type[CellState],
+                                rule: Type[Rule]):
         cells = CAFactory._make_cells(dimension, state_class)
-        CAFactory._apply_neighbourhood_to_cells(cells, neighborhood, dimension)
-        return cells
+        CAFactory._apply_neighborhood_to_cells(cells, neighborhood, dimension)
+        return CellularAutomatonState(cells, dimension, rule)
 
     @staticmethod
     def _make_cells(dimension, state_class):
@@ -22,8 +21,8 @@ class CAFactory:
         return cells
 
     @staticmethod
-    def _apply_neighbourhood_to_cells(cells, neighborhood, dimension):
+    def _apply_neighborhood_to_cells(cells, neighborhood, dimension):
         for coordinate, cell in cells.items():
             n_coordinates = neighborhood.calculate_cell_neighbor_coordinates(coordinate, dimension)
-            cell.neighbours = [cells[tuple(nc)].state for nc in n_coordinates]
+            cell.neighbor_states = [cells[tuple(nc)].state for nc in n_coordinates]
 

src/cellular_automaton/ca_neighborhood.py

@@ -1,56 +1,75 @@
 from enum import Enum
 from operator import add
+from itertools import product
 
 
 class EdgeRule(Enum):
-    IGNORE_MISSING_NEIGHBORS_OF_EDGE_CELLS = 0
-    IGNORE_EDGE_CELLS = 1
+    IGNORE_EDGE_CELLS = 0
+    IGNORE_MISSING_NEIGHBORS_OF_EDGE_CELLS = 1
     FIRST_AND_LAST_CELL_OF_DIMENSION_ARE_NEIGHBORS = 2
 
 
 class Neighborhood:
-    def __init__(self, neighbours_relative: list, edge_rule: EdgeRule):
-        """ Defines a neighborhood for cells.
-        :param neighbours_relative: List of relative coordinates of cells neighbours.
+    def __init__(self, neighbors_relative, edge_rule: EdgeRule):
+        """ Defines a neighborhood of a cell.
+        :param neighbors_relative: List of relative coordinates for cell neighbors.
         :param edge_rule: EdgeRule to define, how cells on the edge of the grid will be handled.
         """
-        self._rel_neighbors = neighbours_relative
-        self.edge_rule = edge_rule
-        self.grid_dimensions = []
+        self._rel_neighbors = neighbors_relative
+        self.__edge_rule = edge_rule
+        self.__grid_dimensions = []
 
     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
-        :param grid_dimensions:          The dimensions of the grid, to apply edge the rule.
-        :return:
+        """ Get a list of absolute coordinates for the cell neighbors.
+            The EdgeRule can reduce the returned neighbor count.
+        :param cell_coordinate:  The coordinate of the cell.
+        :param grid_dimensions:  The dimensions of the grid, to apply the edge the rule.
+        :return: list of absolute coordinates for the cells neighbors.
         """
-        self.grid_dimensions = grid_dimensions
-        return list(self._neighbours_generator(cell_coordinate))
+        self.__grid_dimensions = grid_dimensions
+        return list(self.__neighbors_generator(cell_coordinate))
 
-    def _neighbours_generator(self, cell_coordinate):
-        if not self._does_ignore_edge_cell_rule_apply(cell_coordinate):
+    def __neighbors_generator(self, cell_coordinate):
+        if not self.__does_ignore_edge_cell_rule_apply(cell_coordinate):
             for rel_n in self._rel_neighbors:
-                yield from self._calculate_abs_neighbour_and_decide_validity(cell_coordinate, rel_n)
+                yield from self.__calculate_abs_neighbor_and_decide_validity(cell_coordinate, rel_n)
 
-    def _calculate_abs_neighbour_and_decide_validity(self, cell_coordinate, rel_n):
+    def __calculate_abs_neighbor_and_decide_validity(self, cell_coordinate, rel_n):
         n = list(map(add, rel_n, cell_coordinate))
-        n_folded = self._apply_edge_overflow(n)
-        if n == n_folded or self.edge_rule == EdgeRule.FIRST_AND_LAST_CELL_OF_DIMENSION_ARE_NEIGHBORS:
+        n_folded = self.__apply_edge_overflow(n)
+        if n == n_folded or self.__edge_rule == EdgeRule.FIRST_AND_LAST_CELL_OF_DIMENSION_ARE_NEIGHBORS:
             yield n_folded
 
-    def _does_ignore_edge_cell_rule_apply(self, coordinate):
-        return self.edge_rule == EdgeRule.IGNORE_EDGE_CELLS and self._is_coordinate_on_an_edge(coordinate)
+    def __does_ignore_edge_cell_rule_apply(self, coordinate):
+        return self.__edge_rule == EdgeRule.IGNORE_EDGE_CELLS and self.__is_coordinate_on_an_edge(coordinate)
 
-    def _is_coordinate_on_an_edge(self, coordinate):
-        return all(0 == ci or ci == di-1 for ci, di in zip(coordinate, self.grid_dimensions))
+    def __is_coordinate_on_an_edge(self, coordinate):
+        return all(0 == ci or ci == di-1 for ci, di in zip(coordinate, self.__grid_dimensions))
 
-    def _apply_edge_overflow(self, n):
-        return list(map(lambda ni, di: (ni + di) % di, n, self.grid_dimensions))
+    def __apply_edge_overflow(self, n):
+        return list(map(lambda ni, di: (ni + di) % di, n, self.__grid_dimensions))
 
 
 class MooreNeighborhood(Neighborhood):
-    def __init__(self, edge_rule: EdgeRule = EdgeRule.IGNORE_EDGE_CELLS):
-        super().__init__([[-1, -1], [0, -1], [1, -1],
-                          [-1, 0], [1, 0],
-                          [-1, 1], [0, 1], [1, 1]],
+    def __init__(self, edge_rule: EdgeRule = EdgeRule.IGNORE_EDGE_CELLS, range_=1, dimension=2):
+        super().__init__(tuple(_rel_neighbor_generator(dimension, range_, lambda rel_n: True)),
                          edge_rule)
+
+
+class VonNeumannNeighborhood(Neighborhood):
+    def __init__(self, edge_rule: EdgeRule = EdgeRule.IGNORE_EDGE_CELLS, range_=1, dimension=2):
+        self.range_ = range_
+        super().__init__(tuple(_rel_neighbor_generator(dimension, range_, self.neighbor_rule)),
+                         edge_rule)
+
+    def neighbor_rule(self, rel_n):
+        cross_sum = 0
+        for ci in rel_n:
+            cross_sum += abs(ci)
+        return cross_sum <= self.range_
+
+
+def _rel_neighbor_generator(dimension, range_, rule):
+    for c in product(range(-range_, range_ + 1), repeat=dimension):
+        if rule(c) and c != (0, ) * dimension:
+            yield tuple(reversed(c))

src/cellular_automaton/ca_rule.py

@@ -7,11 +7,11 @@ class Rule:
 
     @staticmethod
     @abstractmethod
-    def evolve_cell(last_cell_state, neighbours_last_states):
+    def evolve_cell(last_cell_state, neighbors_last_states):
         """ Calculates and sets new state of 'cell'.
         :param last_cell_state:   The cells current state to calculate new state for.
-        :param neighbours_last_states:  The cells neighbours current states.
+        :param neighbors_last_states:  The cells neighbors 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.
+        A cells evolution will only be called if it or at least one of its neighbors has changed last evolution_step cycle.
         """
         return False

src/cellular_automaton/ca_state.py

@@ -0,0 +1,10 @@
+from cellular_automaton.ca_rule import Rule
+from typing import Type
+
+
+class CellularAutomatonState:
+    def __init__(self, cells, dimension, evolution_rule: Type[Rule]):
+        self.cells = cells
+        self.dimension = dimension
+        self.evolution_rule = evolution_rule
+        self.current_evolution_step = -1

src/cellular_automaton/cellular_automaton.py

@@ -1,18 +1,8 @@
 import multiprocessing
-
-from cellular_automaton.ca_rule import Rule
-from cellular_automaton.ca_cell import Cell
+from multiprocessing import freeze_support
 from ctypes import c_int
 
 
-class CellularAutomaton:
-    def __init__(self, cells, dimension, evolution_rule: Rule):
-        self.cells = cells
-        self.dimension = dimension
-        self.evolution_rule = evolution_rule
-        self.evolution_iteration_index = -1
-
-
 class CellularAutomatonProcessor:
     def __init__(self, cellular_automaton):
         self._ca = cellular_automaton
@@ -22,51 +12,51 @@ class CellularAutomatonProcessor:
             self.evolve()
 
     def evolve(self):
-        self._ca.evolution_iteration_index += 1
-        i = self._ca.evolution_iteration_index
+        self._ca.current_evolution_step += 1
+        i = self._ca.current_evolution_step
         r = self._ca.evolution_rule.evolve_cell
-        list(map(lambda c: Cell.evolve_if_ready((c.state, c.neighbours), r, i), tuple(self._ca.cells.items())))
-        # print(sum(1 for c in self._ca.cells if c.state.is_set_for_redraw()))
+        list(map(lambda c: c.evolve_if_ready(r, i), tuple(self._ca.cells.values())))
 
 
 class CellularAutomatonMultiProcessor(CellularAutomatonProcessor):
     def __init__(self, cellular_automaton, process_count: int = 2):
+        freeze_support()
         if process_count < 1:
             raise ValueError
 
         super().__init__(cellular_automaton)
 
         self.evolve_range = range(len(self._ca.cells))
-        self.evolution_iteration_index = multiprocessing.RawValue(c_int, -1)
+        self.shared_evolution_step = multiprocessing.RawValue(c_int, self._ca.current_evolution_step)
 
+        self.__init_processes_and_clean_cell_instances(process_count)
+
+    def __init_processes_and_clean_cell_instances(self, process_count):
         self.pool = multiprocessing.Pool(processes=process_count,
                                          initializer=_init_process,
                                          initargs=(tuple(self._ca.cells.values()),
                                                    self._ca.evolution_rule,
-                                                   self.evolution_iteration_index))
-        self._evolve_method = self.pool.map
-
+                                                   self.shared_evolution_step))
         for cell in self._ca.cells.values():
-            del cell.neighbours
+            del cell.neighbor_states
 
     def evolve(self):
-        self._ca.evolution_iteration_index += 1
-        self.evolution_iteration_index.value = self._ca.evolution_iteration_index
+        self._ca.current_evolution_step += 1
+        self.shared_evolution_step.value = self._ca.current_evolution_step
         self.pool.map(_process_routine, self.evolve_range)
 
 
 global_cells = None
 global_rule = None
-global_iteration = None
+global_evolution_step = None
 
 
 def _init_process(cells, rule, index):
-    global global_rule, global_cells, global_iteration
+    global global_rule, global_cells, global_evolution_step
     global_cells = cells
     global_rule = rule
-    global_iteration = index
+    global_evolution_step = index
 
 
 def _process_routine(i):
-    Cell.evolve_if_ready(global_cells[i], global_rule.evolve_cell, global_iteration.value)
-
+    global_cells[i].evolve_if_ready(global_rule.evolve_cell, global_evolution_step.value)

test/test_cell.py

@@ -13,27 +13,27 @@ class TestState(CellState):
 class TestCellState(unittest.TestCase):
     def setUp(self):
         self.cell = Cell(TestState)
-        self.neighbours = [TestState() for x in range(5)]
-        for neighbour in self.neighbours:
-            neighbour.set_state_of_iteration((0, ), 0)
-        self.cell.neighbours = self.neighbours
+        self.neighbors = [TestState() for x in range(5)]
+        for neighbor in self.neighbors:
+            neighbor.set_state_of_evolution_step((0, ), 0)
+        self.cell.neighbor_states = self.neighbors
 
-    def cell_and_neighbours_active(self, iteration):
-        self.neighbours.append(self.cell.state)
+    def cell_and_neighbors_active(self, evolution_step):
+        self.neighbors.append(self.cell.state)
         all_active = True
-        for state in self.neighbours:
-            if not state.is_active(iteration):
+        for state in self.neighbors:
+            if not state.is_active(evolution_step):
                 all_active = False
         return all_active
 
     def test_evolve_activation(self):
-        Cell.evolve_if_ready(self.cell, (lambda a, b: (1,)), 0)
-        all_active = self.cell_and_neighbours_active(1)
+        self.cell.evolve_if_ready((lambda a, b: (1,)), 0)
+        all_active = self.cell_and_neighbors_active(1)
         self.assertTrue(all_active)
 
     def test_evolve_activation_on_no_change(self):
-        Cell.evolve_if_ready(self.cell, (lambda a, b: (0,)), 0)
-        all_active = self.cell_and_neighbours_active(1)
+        self.cell.evolve_if_ready((lambda a, b: (0,)), 0)
+        all_active = self.cell_and_neighbors_active(1)
         self.assertFalse(all_active)
 
 

test/test_cell_state.py

@@ -10,33 +10,33 @@ class TestCellState(unittest.TestCase):
         self.cell_state = cs.SynchronousCellState(initial_state=(0,), draw_first_state=False)
 
     def test_get_state_with_overflow(self):
-        self.cell_state.set_state_of_iteration(new_state=(1,), iteration=0)
-        self.assertEqual(tuple(self.cell_state.get_state_of_iteration(2)), (1,))
+        self.cell_state.set_state_of_evolution_step(new_state=(1,), evolution_step=0)
+        self.assertEqual(tuple(self.cell_state.get_state_of_evolution_step(2)), (1,))
 
     def test_set_state_with_overflow(self):
-        self.cell_state.set_state_of_iteration(new_state=(1,), iteration=3)
-        self.assertEqual(tuple(self.cell_state.get_state_of_iteration(1)), (1,))
+        self.cell_state.set_state_of_evolution_step(new_state=(1,), evolution_step=3)
+        self.assertEqual(tuple(self.cell_state.get_state_of_evolution_step(1)), (1,))
 
     def test_set_state_does_not_effect_all_slots(self):
-        self.cell_state.set_state_of_iteration(new_state=(1,), iteration=0)
-        self.assertEqual(tuple(self.cell_state.get_state_of_iteration(1)), (0,))
+        self.cell_state.set_state_of_evolution_step(new_state=(1,), evolution_step=0)
+        self.assertEqual(tuple(self.cell_state.get_state_of_evolution_step(1)), (0,))
 
     def test_redraw_state_on_change(self):
-        self.cell_state.set_state_of_iteration(new_state=(1,), iteration=0)
+        self.cell_state.set_state_of_evolution_step(new_state=(1,), evolution_step=0)
         self.assertTrue(self.cell_state.is_set_for_redraw())
 
     def test_redraw_state_on_nochange(self):
-        self.cell_state.set_state_of_iteration(new_state=(0,), iteration=0)
+        self.cell_state.set_state_of_evolution_step(new_state=(0,), evolution_step=0)
         self.assertFalse(self.cell_state.is_set_for_redraw())
 
     def test_active_state_after_set(self):
-        self.cell_state.set_state_of_iteration(new_state=(1,), iteration=0)
+        self.cell_state.set_state_of_evolution_step(new_state=(1,), evolution_step=0)
         self.assertFalse(self.cell_state.is_active(0))
         self.assertFalse(self.cell_state.is_active(1))
 
-    def test_set_active_for_next_iteration(self):
-        self.cell_state.set_state_of_iteration(new_state=(1,), iteration=0)
-        self.cell_state.set_active_for_next_iteration(0)
+    def test_set_active_for_next_evolution_step(self):
+        self.cell_state.set_state_of_evolution_step(new_state=(1,), evolution_step=0)
+        self.cell_state.set_active_for_next_evolution_step(0)
         self.assertFalse(self.cell_state.is_active(0))
         self.assertTrue(self.cell_state.is_active(1))
 
@@ -44,7 +44,7 @@ class TestCellState(unittest.TestCase):
         self.assertRaises(IndexError, self.__set_state_with_new_length)
 
     def __set_state_with_new_length(self):
-        return self.cell_state.set_state_of_iteration(new_state=(1, 1), iteration=0)
+        return self.cell_state.set_state_of_evolution_step(new_state=(1, 1), evolution_step=0)
 
     def test_redraw_flag(self):
         self.cell_state = cs.SynchronousCellState(initial_state=(0,), draw_first_state=True)

test/test_factory.py

@@ -12,56 +12,56 @@ class TestFac(CAFactory):
         return CAFactory._make_cells(dimension, state_class)
 
     @staticmethod
-    def apply_neighbourhood(cells, neighborhood, dimension):
-        return CAFactory._apply_neighbourhood_to_cells(cells, neighborhood, dimension)
+    def apply_neighborhood(cells, neighborhood, dimension):
+        return CAFactory._apply_neighborhood_to_cells(cells, neighborhood, dimension)
 
 
 class TestCAFactory(unittest.TestCase):
+    def setUp(self):
+        self._neighborhood = MooreNeighborhood(EdgeRule.IGNORE_EDGE_CELLS)
+
     def test_make_ca_calls_correct_methods(self):
         with mock.patch.object(CAFactory, '_make_cells', return_value={1: True}) as m1:
-            with mock.patch.object(CAFactory, '_apply_neighbourhood_to_cells') as m2:
-                CAFactory.make_cellular_automaton([10], Neighborhood, CellState)
+            with mock.patch.object(CAFactory, '_apply_neighborhood_to_cells') as m2:
+                CAFactory.make_cellular_automaton([10], self._neighborhood, CellState, Rule())
                 m1.assert_called_once_with([10], CellState)
-                m2.assert_called_once_with({1: True}, Neighborhood, [10])
+                m2.assert_called_once_with({1: True}, self._neighborhood, [10])
 
     def test_make_ca_returns_correct_values(self):
         with mock.patch.object(CAFactory, '_make_cells', return_value={1: True}):
-            with mock.patch.object(CAFactory, '_apply_neighbourhood_to_cells'):
-                cells = CAFactory.make_cellular_automaton([10], Neighborhood, CellState)
-                self.assertEqual(tuple(cells.values()), (True, ))
+            with mock.patch.object(CAFactory, '_apply_neighborhood_to_cells'):
+                ca = CAFactory.make_cellular_automaton([10], self._neighborhood, CellState, Rule())
+                self.assertIsInstance(ca, CellularAutomatonState)
+                self.assertEqual(tuple(ca.cells.values()), (True, ))
 
     def test_1dimension_coordinates(self):
-        fac = TestFac()
-        c = fac.make_cells([3], CellState)
+        c = TestFac.make_cells([3], CellState)
         self.assertEqual(list(c.keys()), [(0,), (1,), (2,)])
 
     def test_2dimension_coordinates(self):
-        fac = TestFac()
-        c = fac.make_cells([2, 2], CellState)
+        c = TestFac.make_cells([2, 2], CellState)
         self.assertEqual(list(c.keys()), [(0, 0), (0, 1), (1, 0), (1, 1)])
 
     def test_3dimension_coordinates(self):
-        fac = TestFac()
-        c = fac.make_cells([2, 2, 2], CellState)
+        c = TestFac.make_cells([2, 2, 2], CellState)
         self.assertEqual(list(c.keys()), [(0, 0, 0), (0, 0, 1), (0, 1, 0), (0, 1, 1),
                                           (1, 0, 0), (1, 0, 1), (1, 1, 0), (1, 1, 1)])
 
-    def test_apply_neighbourhood(self):
-        fac = TestFac()
-        cells = fac.make_cells([3, 3], CellState)
-        fac.apply_neighbourhood(cells, MooreNeighborhood(EdgeRule.IGNORE_EDGE_CELLS), [3, 3])
+    def test_apply_neighborhood(self):
+        cells = TestFac.make_cells([3, 3], CellState)
+        TestFac.apply_neighborhood(cells, self._neighborhood, [3, 3])
 
-        neighbours = self.__create_neighbour_list_of_cell((1, 1), cells)
+        neighbors = self.__create_neighbor_list_of_cell((1, 1), cells)
 
-        self.assertEqual(set(neighbours), set(cells[(1, 1)].neighbours))
+        self.assertEqual(set(neighbors), set(cells[(1, 1)].neighbor_states))
 
     @staticmethod
-    def __create_neighbour_list_of_cell(cell_id, cells):
-        neighbours = []
+    def __create_neighbor_list_of_cell(cell_id, cells):
+        neighbors = []
         for c in cells.values():
             if c != cells[cell_id]:
-                neighbours.append(c.state)
-        return neighbours
+                neighbors.append(c.state)
+        return neighbors
 
 
 if __name__ == '__main__':

test/test_neighborhood.py

@@ -7,11 +7,12 @@ import unittest
 
 class TestNeighborhood(unittest.TestCase):
     @staticmethod
-    def check_neighbors(neighborhood, neighborhood_sets):
+    def check_neighbors(neighborhood, neighborhood_sets, dimension=(3, 3)):
         for neighborhood_set in neighborhood_sets:
-            neighbors = neighborhood.calculate_cell_neighbor_coordinates(neighborhood_set[0], [3, 3])
+            neighbors = neighborhood.calculate_cell_neighbor_coordinates(neighborhood_set[0], dimension)
             if neighborhood_set[1] != neighbors:
-                print("Error neighbours do not fit (expected, real): ", (neighborhood_set[1]), neighbors)
+                print("\nrel_n:", neighborhood._rel_neighbors)
+                print("\nWrong neighbors (expected, real): ", (neighborhood_set[1]), neighbors)
                 return False
         return True
 
@@ -36,6 +37,22 @@ class TestNeighborhood(unittest.TestCase):
         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]))
 
+    def test_von_neumann_r1(self):
+        neighborhood = csn.VonNeumannNeighborhood(csn.EdgeRule.FIRST_AND_LAST_CELL_OF_DIMENSION_ARE_NEIGHBORS)
+        n1 = [[1, 1], [[1, 0], [0, 1], [2, 1], [1, 2]]]
+        self.assertTrue(self.check_neighbors(neighborhood, [n1]))
+
+    def test_von_neumann_r2(self):
+        neighborhood = csn.VonNeumannNeighborhood(csn.EdgeRule.FIRST_AND_LAST_CELL_OF_DIMENSION_ARE_NEIGHBORS, range_=2)
+        n1 = [[2, 2], [[2, 0], [1, 1], [2, 1], [3, 1], [0, 2], [1, 2], [3, 2], [4, 2], [1, 3], [2, 3], [3, 3], [2, 4]]]
+        self.assertTrue(self.check_neighbors(neighborhood, [n1], dimension=[5, 5]))
+
+    def test_von_neumann_d3(self):
+        neighborhood = csn.VonNeumannNeighborhood(csn.EdgeRule.FIRST_AND_LAST_CELL_OF_DIMENSION_ARE_NEIGHBORS,
+                                                  dimension=3)
+        n1 = [[1, 1, 1], [[1, 1, 0], [1, 0, 1], [0, 1, 1], [2, 1, 1], [1, 2, 1], [1, 1, 2]]]
+        self.assertTrue(self.check_neighbors(neighborhood, [n1], dimension=[3, 3, 3]))
+
 
 if __name__ == '__main__':
     unittest.main()