def part2(_, state):
"""Solve for the answer to part 2."""
distances = state["distances"]
start_portal = state["start_portal"]
end_portal = state["end_portal"]
Node = collections.namedtuple("Node", "portal level")
def get_edges(node):
# Walking edges
for portal, dist in distances[node.portal].items():
yield Node(portal, node.level), dist
# Teleporting edges
if node.portal.label not in ("AA", "ZZ"):
if node.portal.is_inner:
yield Node(flip_portal(node.portal), node.level + 1), 1
elif node.level > 0:
yield Node(flip_portal(node.portal), node.level - 1), 1
# Assume the shortest path doesn't go beyond level 50
max_level = 50
return dijkstra(
nodes={
Node(portal, level)
for portal in distances.keys() for level in range(max_level + 1)
},
edge_producer=get_edges,
start_node=Node(start_portal, level=0),
end_node=Node(end_portal, level=0),
)
def node_distance_matrix(self):
self.alldistances = {}
nnodes = len(self.node_list)
self.distancematrix = np.empty((nnodes, nnodes))
for node in self.node_list:
distance, pred = util.dijkstra(self, self.edge_lengths, node, n=float('inf'))
pred = np.array(pred)
tree = util.generatetree(pred)
self.alldistances[node] = (distance, tree)
self.distancematrix[node] = distance
def node_distance_matrix(self, n_processes):
"""
Called from: allneighbordistances()
nearestneighbordistances()
distancebandweights()
"""
self.alldistances = {}
nnodes = len(self.node_list)
self.distancematrix = np.empty((nnodes, nnodes))
# Single-core processing
if not n_processes:
for node in self.node_list:
distance, pred = util.dijkstra(self,
self.edge_lengths,
node,
n=float('inf'))
pred = np.array(pred)
#tree = util.generatetree(pred) <---- something to look at in the future
tree = None
self.alldistances[node] = (distance, tree)
self.distancematrix[node] = distance
# Multiprocessing
if n_processes:
import multiprocessing as mp
from itertools import repeat
if n_processes == "all":
cores = mp.cpu_count()
else:
cores = n_processes
p = mp.Pool(processes=cores)
distance_pred = p.map(
util.dijkstra_multi,
zip(repeat(self), repeat(self.edge_time), self.node_list))
distance = [
distance_pred[iteration][0]
for iteration in range(len(distance_pred))
]
pred = [
distance_pred[iteration][1]
for iteration in range(len(distance_pred))
]
pred = np.array(pred)
#tree = util.generatetree(pred[node])
for node in self.node_list:
self.distancematrix[node] = distance[node]
def part1(maze, state):
"""Solve for the answer to part 1."""
inner_portals, outer_portals = find_portals(maze)
distances = state["distances"] = find_distances(maze, inner_portals,
outer_portals)
start_portal = state["start_portal"] = Portal(label="AA", is_inner=False)
end_portal = state["end_portal"] = Portal(label="ZZ", is_inner=False)
def get_edges(portal):
# Walking edges
yield from distances[portal].items()
# Teleporting edges
if portal.label not in ("AA", "ZZ"):
yield flip_portal(portal), 1
return dijkstra(
nodes=distances.keys(),
edge_producer=get_edges,
start_node=start_portal,
end_node=end_portal,
)
def node_distance_matrix(self, n_processes):
"""
Called from: allneighbordistances()
nearestneighbordistances()
distancebandweights()
"""
self.alldistances = {}
nnodes = len(self.node_list)
self.distancematrix = np.empty((nnodes, nnodes))
# Single-core processing
if not n_processes:
for node in self.node_list:
distance, pred = util.dijkstra(self, self.edge_lengths, node, n=float('inf'))
pred = np.array(pred)
#tree = util.generatetree(pred) <---- something to look at in the future
tree = None
self.alldistances[node] = (distance, tree)
self.distancematrix[node] = distance
# Multiprocessing
if n_processes:
import multiprocessing as mp
from itertools import repeat
if n_processes == "all":
cores = mp.cpu_count()
else:
cores = n_processes
p = mp.Pool(processes=cores)
distance_pred = p.map(util.dijkstra_multi, zip(repeat(self),
repeat(self.edge_time),
self.node_list))
distance = [distance_pred[iteration][0] for iteration in range(len(distance_pred))]
pred = [distance_pred[iteration][1] for iteration in range(len(distance_pred))]
pred = np.array(pred)
#tree = util.generatetree(pred[node])
for node in self.node_list:
self.distancematrix[node] = distance[node]