Add solution to day 18 part 1
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#################################################################################
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#################################################################################
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import matplotlib.pyplot as plt
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import math
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import networkx
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import sys
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import enum
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import string
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import itertools
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from dataclasses import dataclass
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from typing import Any, Iterable, Set, List, Tuple, Optional, FrozenSet
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class NodeType(enum.Enum):
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WALL = 0
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OPEN = 1
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PLAYER = 2
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KEY = 3
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DOOR = 4
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@staticmethod
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def make_from_input_char(char: str):
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CHAR_MAP = {
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'#': NodeType.WALL,
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'.': NodeType.OPEN,
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'@': NodeType.PLAYER,
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}
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node_type = CHAR_MAP.get(char)
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if node_type is None:
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if char in string.ascii_lowercase:
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node_type = NodeType.KEY
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elif char in string.ascii_uppercase:
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node_type = NodeType.DOOR
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else:
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raise ValueError(f"Invalid NodeType char '{char}'' ")
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return node_type
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@dataclass
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class Node:
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node_type: NodeType
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char: str
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class PathCache(dict):
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@dataclass(frozen=True, eq=True)
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class Key:
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collected_keys: FrozenSet[str]
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src: Tuple[int, int]
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@classmethod
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def make_from_iterable(cls, collected_keys: Iterable[str], src: Tuple[int, int]) -> None:
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return cls(frozenset(collected_keys), src)
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@dataclass
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class Entry:
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cost: int
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path: List[Tuple[int, int]]
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# Make a graph from the maze input
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def make_graph_from_input(input_lines: List[str]) -> networkx.Graph:
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def connect_adjacent_nodes(node_pos: Tuple[int, int]) -> None:
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for d_row, d_col in [(1, 0), (-1, 0), (0, 1), (0, -1)]:
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row, col = node_pos
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row += d_row
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col += d_col
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if (row, col) in graph.nodes:
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graph.add_edge(node_pos, (row, col))
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graph = networkx.Graph()
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for row, line in enumerate(input_lines):
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for col, char in enumerate(line):
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node_type = NodeType.make_from_input_char(char)
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if node_type == NodeType.WALL:
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continue
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node = Node(node_type, char)
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graph.add_node((row, col), info=node)
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for node in graph.nodes:
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connect_adjacent_nodes(node)
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return graph
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def make_reduced_graph(graph: networkx.Graph) -> networkx.Graph:
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interactive_nodes = {node: data for node, data in graph.nodes.data('info') if data.node_type != NodeType.OPEN}
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reduced_graph = networkx.Graph()
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for node1, node2 in itertools.combinations(interactive_nodes.items(), 2):
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pos1, info1 = node1
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pos2, info2 = node2
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reduced_graph.add_node(pos1, info=info1)
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reduced_graph.add_node(pos2, info=info2)
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path = networkx.shortest_path(graph, pos1, pos2)
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# Check if any nodes are interactive and within the path.
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# We don't want to make an edge between node1 and node2 if there are.
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intermediate_interactive_nodes = set(path[1:-1]).intersection(set(interactive_nodes.keys()))
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if len(intermediate_interactive_nodes) > 0:
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continue
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reduced_graph.add_edge(pos1, pos2, distance=len(path)-1)
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return reduced_graph
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# Check if we can "clear" a whole path, which is determined by if all keys come before their doors
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def path_is_clearable(graph: networkx.Graph, path: Iterable[Tuple[int, int]], collected_keys: Set[str]) -> bool:
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all_collected_keys = collected_keys.copy()
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for node in path:
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node_info = graph.nodes[node]['info']
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if node_info.node_type == NodeType.KEY:
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all_collected_keys.add(node_info.char)
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elif node_info.node_type == NodeType.DOOR and node_info.char.lower() not in all_collected_keys:
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return False
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return True
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def find_shortest_path_cost(
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graph: networkx.Graph,
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starting_node: Optional[Tuple[int, int]] = None,
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visited: Optional[Set[str]] = None,
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path_cache: PathCache = None,
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depth: int = 0, prev_dest: str = None) -> Tuple[int, List[Tuple[int, int]]]:
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if starting_node is None:
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# Start at the player node if no starting node is spcified
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starting_node = next(node for node, data in graph.nodes.data('info') if data.node_type == NodeType.PLAYER)
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if visited is None:
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visited = set((starting_node,))
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if path_cache is None:
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path_cache = PathCache()
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# If we have already visited all nodes, we're done, and there's no more cost to it.
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key_nodes = set(node for node in graph.nodes
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if graph.nodes[node]['info'].char.lower() == graph.nodes[node]['info'].char
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and graph.nodes[node]['info'].char.isalpha())
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if visited == key_nodes:
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return 0, []
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shortest_paths = networkx.shortest_path(graph)
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collected_keys = set(graph.nodes[node]['info'].char for node in visited
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if graph.nodes[node]['info'].char.lower() == graph.nodes[node]['info'].char)
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cache_key = PathCache.Key.make_from_iterable(collected_keys, starting_node)
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try:
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cache_entry = path_cache[cache_key]
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return (cache_entry.cost, cache_entry.path)
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except KeyError:
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# If we don't have a cache entry, keep going.
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# We need to do this instead of .get because otherwise __missing__ won't be called.
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pass
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could_check_path = False
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best_path = None
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best_cost = math.inf
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for destination in key_nodes:
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path = shortest_paths[starting_node][destination]
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if destination in visited:
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continue
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elif not path_is_clearable(graph, path, collected_keys):
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continue
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could_check_path = True
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visited_copy = visited.copy()
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visited_copy.update(path)
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cost = sum(graph.edges[(node1, node2)]['distance'] for node1, node2 in zip(path, path[1:]))
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path_cost, rest_of_path = find_shortest_path_cost(graph, destination, visited_copy, path_cache)
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cost += path_cost
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full_path = path + rest_of_path[1:]
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if cost < best_cost:
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best_path = full_path
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best_cost = cost
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# If the loop didn't run, none of the paths are elgible for use.
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if not could_check_path:
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path_cache[cache_key] = PathCache.Entry(0, [])
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return 0, []
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path_cache[cache_key] = PathCache.Entry(best_cost, best_path)
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return (best_cost, best_path)
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def part1(graph: networkx.Graph) -> int:
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reduced_graph = make_reduced_graph(graph)
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cost, _ = find_shortest_path_cost(reduced_graph)
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return cost
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# A debug function used to print the path as letters
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def print_readable_path(graph: networkx.Graph, path: Iterable[Tuple[int, int]]) -> None:
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print([graph.nodes[node]['info'].char for node in path])
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# A debug function used to visualize the drawn graph
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def draw_graph(graph: networkx.Graph) -> None:
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positions = networkx.spring_layout(graph)
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networkx.draw_networkx_nodes(graph, positions)
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networkx.draw_networkx_edges(graph, positions)
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networkx.draw_networkx_labels(graph, positions, {node: data.char for node, data in graph.nodes.data('info')})
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networkx.draw_networkx_edge_labels(graph, positions, {(node1, node2): data for node1, node2, data in graph.edges.data('distance')})
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plt.show()
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if __name__ == "__main__":
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if len(sys.argv) != 2:
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print("Usage: ./main.py in_file")
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sys.exit(1)
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with open(sys.argv[1]) as f:
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input_lines = [line.rstrip('\n') for line in f.readlines()]
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graph = make_graph_from_input(input_lines)
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print(part1(graph))
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