neuropercolation/cellular_automaton/factory.py
2019-02-23 17:37:18 +01:00

75 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 itertools
from typing import Type
from . import Neighborhood, Rule
from .automaton import CellularAutomatonProcessor, CellularAutomatonMultiProcessor
from .cell import Cell
from .state import CellularAutomatonState
from .cell_state import CellState, SynchronousCellState
class CAFactory:
@staticmethod
def make_single_process_cellular_automaton(dimension,
neighborhood: Neighborhood,
rule: Type[Rule]):
ca = CAFactory._make_cellular_automaton_state(dimension, neighborhood, CellState, rule)
return CellularAutomatonProcessor(ca)
@staticmethod
def _make_cellular_automaton_state(dimension, neighborhood, state_class, rule_class):
rule = rule_class(neighborhood)
cell_states = CAFactory._make_cell_states(state_class, rule, dimension)
cells = CAFactory._make_cells(cell_states, neighborhood, dimension)
return CellularAutomatonState(cells, dimension, rule)
@staticmethod
def make_multi_process_cellular_automaton(dimension,
neighborhood: Neighborhood,
rule: Type[Rule],
processes: int):
if processes < 1:
raise ValueError("At least one process is necessary")
elif processes == 1:
return CAFactory.make_single_process_cellular_automaton(dimension, neighborhood, rule)
else:
ca = CAFactory._make_cellular_automaton_state(dimension, neighborhood, SynchronousCellState, rule)
return CellularAutomatonMultiProcessor(ca, processes)
@staticmethod
def _make_cell_states(state_class, rule, dimension):
cell_states = {}
for c in itertools.product(*[range(d) for d in dimension]):
coordinate = tuple(c)
cell_states[coordinate] = state_class(rule.init_state(coordinate))
return cell_states
@staticmethod
def _make_cells(cell_states, neighborhood, dimension):
cells = {}
for coordinate, cell_state in cell_states.items():
n_coordinates = neighborhood.calculate_cell_neighbor_coordinates(coordinate, dimension)
neighbor_states = [cell_states[tuple(nc)] for nc in n_coordinates]
cells[coordinate] = Cell(cell_state, neighbor_states)
return cells