neuropercolation/cellular_automaton/_automaton.py
2019-02-23 16:20:48 +01:00

90 lines
2.9 KiB
Python

"""
Copyright 2019 Richard Feistenauer
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import multiprocessing
from multiprocessing import freeze_support
from ctypes import c_int
class CellularAutomatonProcessor:
def __init__(self, cellular_automaton):
self._ca = cellular_automaton
def evolve_x_times(self, x):
for x in range(x):
self.evolve()
def evolve(self):
i = self._ca.current_evolution_step
r = self._ca.evolution_rule.evolve_cell
list(map(lambda c: c.evolve_if_ready(r, i), tuple(self._ca.cells.values())))
self._ca.current_evolution_step += 1
def get_dimension(self):
return self._ca.dimension
def get_cells(self):
return self._ca.cells
def get_current_evolution_step(self):
return self._ca.current_evolution_step
def get_current_rule(self):
return self._ca.evolution_rule
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._ca.current_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._ca.current_evolution_step))
def evolve(self):
self.pool.map(_process_routine, self.evolve_range)
self._ca.current_evolution_step.value += 1
def get_current_evolution_step(self):
return self._ca.current_evolution_step.value
global_cells = None
global_rule = None
global_evolution_step = None
def _init_process(cells, rule, index):
global global_rule, global_cells, global_evolution_step
global_cells = cells
global_rule = rule
global_evolution_step = index
def _process_routine(i):
global_cells[i].evolve_if_ready(global_rule.evolve_cell, global_evolution_step.value)