increased api documentation
This commit is contained in:
Richard Feistenauer
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9964e3b483
@ -79,8 +79,9 @@ class MyRule(Rule):
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Just inherit from `cellular_automaton.rule:Rule` and define the evolution rule and initial state.
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## Visualisation
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The module provides a pygame window for common two dimensional.
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To add another kind of display option e.g. for other dimensions or hexagonal grids you can extrend the provided implementation or build you own.
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The package provides a module for visualization in a pygame window for common two dimensional automatons.
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To add another kind of display option e.g. for other dimensions or hexagonal grids you can extrend the provided implementation or build your own.
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The visual part of this module is fully decoupled and thus should be easily replaceable.
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## Examples
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@ -93,3 +94,6 @@ Those two example automaton implementations should provide a good start for your
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## Dependencies
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As mentioned above the module depends on [pygame](https://www.pygame.org/news) for visualisation.
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This is the only dependency however.
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## Licence
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This package is distributed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0), see [LICENSE](./LICENSE.txt)
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@ -21,14 +21,20 @@ from ctypes import c_int
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class CellularAutomatonProcessor:
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""" This class is responsible for the evolution of the cells. """
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def __init__(self, cellular_automaton):
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self._ca = cellular_automaton
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def evolve_x_times(self, x):
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""" Evolve all cells x times.
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:param x: The number of evolution steps processed with the call of this method.
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"""
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for x in range(x):
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self.evolve()
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def evolve(self):
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""" Evolve all cells """
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self._ca.current_evolution_step += 1
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i = self._ca.current_evolution_step
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r = self._ca.evolution_rule.evolve_cell
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@ -48,6 +54,14 @@ class CellularAutomatonProcessor:
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class CellularAutomatonMultiProcessor(CellularAutomatonProcessor):
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""" This is a variant of CellularAutomatonProcessor that uses multi processing.
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The evolution of the cells will be outsourced to new processes.
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WARNING:
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This variant has high memory use!
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The inter process communication overhead can make this variant slower than single processing!
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"""
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def __init__(self, cellular_automaton, process_count: int = 2):
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multiprocessing.freeze_support()
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if process_count < 1:
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@ -57,9 +71,9 @@ class CellularAutomatonMultiProcessor(CellularAutomatonProcessor):
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self.evolve_range = range(len(self._ca.cells))
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self._ca.current_evolution_step = RawValue(c_int, self._ca.current_evolution_step)
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self.__init_processes_and_clean_cell_instances(process_count)
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self.__init_processes(process_count)
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def __init_processes_and_clean_cell_instances(self, process_count):
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def __init_processes(self, process_count):
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self.pool = multiprocessing.Pool(processes=process_count,
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initializer=_init_process,
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initargs=(tuple(self._ca.cells.values()),
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@ -23,22 +23,31 @@ class Cell:
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self._neighbor_states = neighbors
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def is_set_for_redraw(self):
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""" Flag indicating a change in the cells state since last call of 'was_redrawn'. """
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return self._state.is_set_for_redraw()
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def was_redrawn(self):
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""" Should be called after this cell was drawn to prevent unnecessary redraws. """
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self._state.was_redrawn()
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def get_current_state(self, evolution_step):
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return self._state.get_state_of_evolution_step(evolution_step)
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def evolve_if_ready(self, rule, evolution_step):
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""" When there was a change in this cell or one of its neighbours,
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evolution rule is called and the new state and redraw flag gets set if necessary.
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If there was a change neighbours will be notified.
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"""
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if self._state.is_active(evolution_step):
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new_state = rule(list(self._state.get_state_of_last_evolution_step(evolution_step)),
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[list(n.get_state_of_last_evolution_step(evolution_step)) for n in self._neighbor_states])
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self.set_new_state_and_activate(new_state, evolution_step)
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self.__set_new_state_and_consider_activation(new_state, evolution_step)
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def set_new_state_and_activate(self, new_state: cell_state.CellState, evolution_step):
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def __set_new_state_and_consider_activation(self, new_state: cell_state.CellState, evolution_step):
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changed = self._state.set_state_of_evolution_step(new_state, evolution_step)
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self.__activate_if_necessary(changed, evolution_step)
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def __activate_if_necessary(self, changed, evolution_step):
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if changed:
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self._state.set_active_for_next_evolution_step(evolution_step)
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for n in self._neighbor_states:
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@ -35,20 +35,19 @@ class CellState:
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def is_active(self, current_evolution_step):
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""" Returns the active status for the requested evolution_step
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:param current_evolution_step: The evolution_step of interest.
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:return: True if the cell state is set active for this evolution_step.
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:return: True if the cell or one of its neighbours changed in the last evolution step.
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"""
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return self._active[self._calculate_slot(current_evolution_step)]
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def set_active_for_next_evolution_step(self, current_evolution_step):
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""" Sets the cell active for the next evolution_step, so it will be evolved.
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:param current_evolution_step: The current evolution_step index.
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:return:
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"""
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self._active[self._calculate_slot(current_evolution_step + 1)] = True
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def is_set_for_redraw(self):
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""" States if this state should be redrawn.
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:return: True if redraw is needed.
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:return: True if state changed since last call of 'was_redrawn'.
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"""
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return self._dirty
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@ -62,7 +61,7 @@ class CellState:
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def get_state_of_evolution_step(self, evolution_step):
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""" Returns the state of the evolution_step.
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:param evolution_step: Uses the evolution_step index, to differ between concurrent states.
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:return The state of this evolution_step.
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:return The state of the requested evolution_step.
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"""
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return self._state_slots[self._calculate_slot(evolution_step)]
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@ -71,6 +70,7 @@ class CellState:
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:param new_state: The new state to set.
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:param evolution_step: The evolution_step index, to differ between concurrent states.
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:return True if the state really changed.
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:raises IndexError: If the state length changed.
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"""
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changed = self._set_new_state_if_valid(new_state, evolution_step)
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self._dirty |= changed
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@ -111,9 +111,8 @@ class CellState:
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class SynchronousCellState(CellState):
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"""
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CellState version using shared values for multi processing purpose.
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"""
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""" CellState version using shared values for multi processing purpose. """
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def __init__(self, initial_state=(0., ), draw_first_state=True):
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super().__init__(initial_state, draw_first_state)
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self._state_slots = [RawArray(c_float, initial_state) for i in range(self.__class__._state_save_slot_count)]
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@ -33,10 +33,11 @@ class _CASurface:
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return [self.__rect.width / grid_dimension[0], self.__rect.height / grid_dimension[1]]
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def redraw_cellular_automaton(self):
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update_rectangles = list(self.__cell_redraw_dirty_rectangles())
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""" Redraws those cells which changed their state since last redraw. """
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update_rectangles = list(self.__redraw_dirty_cells())
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pygame.display.update(update_rectangles)
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def __cell_redraw_dirty_rectangles(self):
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def __redraw_dirty_cells(self):
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for coordinate, cell in self._cellular_automaton.get_cells().items():
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if cell.is_set_for_redraw():
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yield from self.__redraw_cell(cell, coordinate)
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@ -26,6 +26,8 @@ from .cell_state import CellState, SynchronousCellState
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class CAFactory:
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""" This factory provides an easy way to create cellular automatons with single or multi processing. """
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@staticmethod
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def make_single_process_cellular_automaton(dimension,
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neighborhood: Neighborhood,
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@ -67,7 +69,7 @@ class CAFactory:
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cells = {}
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for coordinate, cell_state in cell_states.items():
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n_coordinates = neighborhood.calculate_cell_neighbor_coordinates(coordinate, dimension)
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neighbor_states = [cell_states[tuple(nc)] for nc in n_coordinates]
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neighbor_states = tuple([cell_states[tuple(nc)] for nc in n_coordinates])
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cells[coordinate] = Cell(cell_state, neighbor_states)
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return cells
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@ -45,7 +45,7 @@ class Neighborhood:
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self.__grid_dimensions = grid_dimensions
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return list(self.__neighbors_generator(cell_coordinate))
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def get_neighbor_id_from_rel(self, rel_coordinate):
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def get_id_of_neighbor_from_relative_coordinate(self, rel_coordinate):
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return self._rel_neighbors.index(rel_coordinate)
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def __neighbors_generator(self, cell_coordinate):
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@ -70,12 +70,45 @@ class Neighborhood:
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class MooreNeighborhood(Neighborhood):
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""" Defines a Moore neighborhood:
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Moore defined a neighborhood with a radius applied on a the non euclidean distance to other cells in the grid.
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Example:
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Moor neighborhood in 2 dimensions with radius 1 and 2
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C = cell of interest
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N = neighbour of cell
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X = no neighbour of cell
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Radius 1 Radius 2
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X X X X X N N N N N
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X N N N X N N N N N
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X N C N X N N C N N
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X N N N X N N N N N
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X X X X X N N N N N
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"""
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def __init__(self, edge_rule: EdgeRule = EdgeRule.IGNORE_EDGE_CELLS, range_=1, dimension=2):
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super().__init__(tuple(_rel_neighbor_generator(dimension, range_, lambda rel_n: True)),
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edge_rule)
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class VonNeumannNeighborhood(Neighborhood):
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""" Defines a Von Neumann neighborhood:
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Von Neumann defined a neighborhood with a radius applied to Manhatten distance
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(steps between cells without diagonal movement).
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Example:
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Von Neumann neighborhood in 2 dimensions with radius 1 and 2
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C = cell of interest
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N = neighbour of cell
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X = no neighbour of cell
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Radius 1 Radius 2
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X X X X X X X N X X
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X X N X X X N N N X
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X N C N X N N C N N
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X X N X X X N N N X
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X X X X X X X N X X
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"""
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def __init__(self, edge_rule: EdgeRule = EdgeRule.IGNORE_EDGE_CELLS, range_=1, dimension=2):
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self.range_ = range_
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super().__init__(tuple(_rel_neighbor_generator(dimension, range_, self.neighbor_rule)),
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@ -20,30 +20,36 @@ from . import neighborhood
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class Rule:
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""" Base class for evolution rules.
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This class has to be inherited if the cellular automaton is supposed to have any effect.
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"""
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def __init__(self, neighborhood_: neighborhood.Neighborhood):
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self._neighborhood = neighborhood_
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def _get_neighbor_by_relative_coordinate(self, neighbours, rel_coordinate):
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return neighbours[self._neighborhood.get_neighbor_id_from_rel(rel_coordinate)]
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return neighbours[self._neighborhood.get_id_of_neighbor_from_relative_coordinate(rel_coordinate)]
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@abc.abstractmethod
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def evolve_cell(self, last_cell_state, neighbors_last_states):
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""" Calculates and sets new state of 'cell'.
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:param last_cell_state: The cells current state to calculate new state for.
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:param neighbors_last_states: The cells neighbors current states.
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:return: New state.
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A cells evolution will only be called if it or at least one of its neighbors has changed last evolution_step cycle.
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A cells evolution will only be called if it or at least one of its neighbors has changed last evolution_step.
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:param last_cell_state: The cells state previous to the evolution step.
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:param neighbors_last_states: The cells neighbors current states.
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:return: New state. The state after this evolution step
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"""
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return last_cell_state
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@abc.abstractmethod
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def init_state(self, cell_coordinate):
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""" Set the initial state for the cell with the given coordinate.
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""" Will be called to initialize a cells state.
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:param cell_coordinate: Cells coordinate.
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:return: Iterable that represents the state
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:return: Iterable that represents the initial cell state
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Has to be compatible with 'multiprocessing.sharedctype.RawArray' when using multi processing.
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"""
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return [0]
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@abc.abstractmethod
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def get_state_draw_color(self, current_state):
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""" Return the draw color for the current state """
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return [0, 0, 0]
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@ -18,6 +18,8 @@ from . import Rule
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class CellularAutomatonState:
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""" Holds all relevant information about the cellular automaton """
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def __init__(self, cells, dimension, evolution_rule: Rule):
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self.cells = cells
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self.dimension = dimension
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@ -1,4 +1,19 @@
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#!/usr/bin/env python3
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"""
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Copyright 2019 Richard Feistenauer
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import random
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from cellular_automaton import *
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@ -8,7 +23,7 @@ ALIVE = [1.0]
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DEAD = [0]
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class TestRule(Rule):
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class ConwaysRule(Rule):
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random_seed = random.seed(13)
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def init_state(self, cell_coordinate):
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@ -45,6 +60,6 @@ if __name__ == "__main__":
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neighborhood = MooreNeighborhood(EdgeRule.FIRST_AND_LAST_CELL_OF_DIMENSION_ARE_NEIGHBORS)
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ca = CAFactory.make_multi_process_cellular_automaton(dimension=[100, 100],
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neighborhood=neighborhood,
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rule=TestRule,
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rule=ConwaysRule,
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processes=4)
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ca_window = CAWindow(cellular_automaton=ca, evolution_steps_per_draw=1)
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@ -1,10 +1,26 @@
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#!/usr/bin/env python3
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"""
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Copyright 2019 Richard Feistenauer
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import random
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from cellular_automaton import *
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class TestRule(Rule):
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class StarfallRule(Rule):
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""" A basic cellular automaton that just copies one neighbour state so get some motion in the grid. """
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random_seed = random.seed(1000)
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def init_state(self, cell_coordinate):
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@ -23,5 +39,5 @@ if __name__ == "__main__":
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neighborhood = MooreNeighborhood(EdgeRule.FIRST_AND_LAST_CELL_OF_DIMENSION_ARE_NEIGHBORS)
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ca = CAFactory.make_single_process_cellular_automaton(dimension=[100, 100],
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neighborhood=neighborhood,
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rule=TestRule)
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rule=StarfallRule)
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ca_window = CAWindow(cellular_automaton=ca, evolution_steps_per_draw=1)
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Block a user