neuropercolation/cellular_automaton/cell_state.py

from multiprocessing import RawArray, RawValue
from ctypes import c_float, c_bool


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.
    """

    _state_save_slot_count = 2

    def __init__(self, initial_state=(0., ), draw_first_state=True):
        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, 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_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_evolution_step(self, current_evolution_step):
        return self.get_state_of_evolution_step(current_evolution_step - 1)

    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(evolution_step)]

    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 evolution_step:  The evolution_step index, to differ between concurrent states.
        :return True if the state really changed.
        """
        changed = self._set_new_state_if_valid(new_state, evolution_step)
        self._dirty |= changed
        self._active[self._calculate_slot(evolution_step)] = False
        return changed

    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 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, evolution_step):
        return evolution_step % cls._state_save_slot_count


class SynchronousCellState(CellState):
    """
        CellState version using shared values for multi processing purpose.
    """
    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_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

    def was_redrawn(self):
        self._dirty.value = False

    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(evolution_step)].value = False
        return changed