neuropercolation/cellular_automaton/neighborhood.py
2019-02-17 11:11:27 +01:00

76 lines
3.1 KiB
Python

from enum import Enum
from operator import add
from itertools import product
class EdgeRule(Enum):
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, 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 = neighbors_relative
self.__edge_rule = edge_rule
self.__grid_dimensions = []
def calculate_cell_neighbor_coordinates(self, cell_coordinate, grid_dimensions):
""" 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.__neighbors_generator(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_neighbor_and_decide_validity(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:
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 __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))
class MooreNeighborhood(Neighborhood):
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))